6
E D I T I O N
Cognitive Psychology
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6
E D I T I O N
Cognitive Psychology
ROBERT J. STERNBERG
Oklahoma State University
KARIN STERNBERG
Oklahoma State University
with contributions of the
Investigating Cognitive Psychology boxes by
JEFF MIO
California State University–Pomona
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Cognitive Psychology, Sixth Edition
Robert J. Sternberg and
Karin Sternberg
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1 2 3 4 5 6 7 15 14 13 12 11
Contents in Brief
CHAPTER 1
Introduction to Cognitive Psychology 1
CHAPTER 2
Cognitive Neuroscience 41
CHAPTER 3
Visual Perception 84
CHAPTER 4
Attention and Consciousness 135
CHAPTER 5
Memory: Models and Research Methods 185
CHAPTER 6
Memory Processes 228
CHAPTER 7
The Landscape of Memory: Mental Images, Maps, and Propositions 269
CHAPTER 8
The Organization of Knowledge in the Mind 319
CHAPTER 9
Language 359
CHAPTER 10
Language in Context 401
CHAPTER 11
Problem Solving and Creativity 442
CHAPTER 12
Decision Making and Reasoning 487
Glossary 530
References 538
Name Index 593
Subject Index 603
v
Contents
CHAPTER 1
Introduction to Cognitive Psychology
1
n Believe It or Not: Now You See It, Now You Don’t!
Cognitive Psychology Defined
2
3
Philosophical Antecedents of Psychology: Rationalism versus Empiricism
Psychological Antecedents of Cognitive Psychology
Early Dialectics in the Psychology of Cognition 7
6
7
n Practical Applications of Cognitive Psychology: Pragmatism
9
It’s Only What You Can See That Counts: From Associationism to Behaviorism
n Believe It or Not: Scientific Progress!?
12
The Whole Is More Than the Sum of Its Parts: Gestalt Psychology
13
Emergence of Cognitive Psychology 13
Early Role of Psychobiology 14
Add a Dash of Technology: Engineering, Computation, and Applied Cognitive
Psychology 14
Cognition and Intelligence 17
What Is Intelligence? 17
n Investigating Cognitive Psychology: Intelligence
Three Cognitive Models of Intelligence
17
18
Research Methods in Cognitive Psychology
Goals of Research 22
Distinctive Research Methods 23
22
n In the Lab of Henry L. Roediger 24
n Investigating Cognitive Psychology: Self-Reports
32
Fundamental Ideas in Cognitive Psychology
34
Key Themes in Cognitive Psychology
Summary
36
38
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
40
Media Resources
40
CHAPTER 2
Cognitive Neuroscience
41
n Believe It or Not: Does Your Brain Use Less Power Than Your Desk Lamp?
42
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
Gross Anatomy of the Brain: Forebrain, Midbrain, Hindbrain 43
43
n In the Lab of Martha Farah
47
Cerebral Cortex and Localization of Function
vi
51
39
11
vii
Contents
Neuronal Structure and Function
Receptors and Drugs 64
61
Viewing the Structures and Functions of the Brain
Postmortem Studies 65
Studying Live Nonhuman Animals 66
Studying Live Humans 66
65
Brain Disorders 75
Stroke 75
Brain Tumors 76
n Believe It or Not: Brain Surgery Can Be Performed While You Are Awake!
Head Injuries
77
77
Intelligence and Neuroscience 78
Intelligence and Brain Size 78
Intelligence and Neurons 79
Intelligence and Brain Metabolism 79
Biological Bases of Intelligence Testing 80
The P-FIT Theory of Intelligence 80
Key Themes
Summary
81
81
Thinking about Thinking: Analytical, Creative,
and Practical Questions 82
Key Terms
82
Media Resources
83
CHAPTER 3
Visual Perception
84
n Believe It or Not: If You Encountered Tyrannosaurus Rex, Would Standing Still Save You?
n Investigating Cognitive Psychology: Perception 86
From Sensation to Representation 86
Some Basic Concepts of Perception 88
n Investigating Cognitive Psychology: The Ganzfeld Effect
90
Seeing Things That Aren’t There, or Are They? 90
How Does Our Visual System Work? 93
Pathways to Perceive the What and the Where 95
Approaches to Perception: How Do We Make Sense of What We See?
Bottom-Up Theories 97
Top-Down Theories 107
How Do Bottom-Up Theories and Top-Down Theories Go Together? 110
Perception of Objects and Forms 111
Viewer-Centered vs. Object-Centered Perception 111
n Practical Applications of Cognitive Psychology: Depth Cues in Photography
The Perception of Groups—Gestalt Laws
Recognizing Patterns and Faces 116
n In the Lab of Marvin Chun
119
113
112
96
85
viii
Contents
n Believe It or Not: Do Two Different Faces Ever Look the Same to You?
The Environment Helps You See
Perceptual Constancies 121
Depth Perception 124
120
121
n Investigating Cognitive Psychology: Binocular Depth Cues
127
Deficits in Perception 127
Agnosias and Ataxias 127
Anomalies in Color Perception 130
Why Does It Matter? Perception in Practice
Key Themes
Summary
131
132
132
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
134
134
Media Resources
134
CHAPTER 4
Attention and Consciousness
135
n Believe It or Not: Does Paying Attention Enable You to Make Better Decisions?
The Nature of Attention and Consciousness
136
137
Attention 138
Attending to Signals over the Short and Long Terms 139
Search: Actively Looking 143
Selective Attention 148
n Investigating Cognitive Psychology: Attenuation Model
Divided Attention
151
153
n Investigating Cognitive Psychology: Dividing Your Attention 155
n Believe It or Not: Are You Productive When You’re Multitasking? 157
Factors That Influence Our Ability to Pay Attention 159
Neuroscience and Attention: A Network Model 160
Intelligence and Attention 161
When Our Attention Fails Us 163
Attention Deficit Hyperactivity Disorder (ADHD) 163
Change Blindness and Inattentional Blindness 165
Spatial Neglect—One Half of the World Goes Amiss 165
Dealing with an Overwhelming World—Habituation and Adaptation
n Practical Applications of Cognitive Psychology: Overcoming Boredom
Automatic and Controlled Processes in Attention
Automatic and Controlled Processes 170
n In the Lab of John F. Kihlstrom
171
How Does Automatization Occur? 172
Automatization in Everyday Life 174
Mistakes We Make in Automatic Processes
175
169
167
167
Contents
Consciousness 177
The Consciousness of Mental Processes 177
Preconscious Processing 178
Key Themes
Summary
182
182
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
184
184
Media Resources
184
CHAPTER 5
Memory: Models and Research Methods
185
n Believe It or Not: Memory Problems? How about Flying Less?
186
Tasks Used for Measuring Memory 187
Recall versus Recognition Tasks 187
Implicit versus Explicit Memory Tasks 190
Intelligence and the Importance of Culture in Testing 192
Models of Memory 193
The Traditional Model of Memory 193
The Levels-of-Processing Model 200
n Investigating Cognitive Psychology: Levels of Processing 201
n Practical Applications of Cognitive Psychology: Elaboration Strategies
An Integrative Model: Working Memory
Multiple Memory Systems 209
n In the Lab of Marcia K. Johnson
A Connectionist Perspective
211
212
Exceptional Memory and Neuropsychology
Outstanding Memory: Mnemonists 214
214
n Believe It or Not: You Can Be a Memory Champion, Too!!!
Deficient Memory 217
How Are Memories Stored?
Key Themes
Summary
202
203
216
223
225
226
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
227
227
Media Resources
227
CHAPTER 6
Memory Processes
228
n Believe It or Not: There’s a Reason You Remember Those Annoying Songs
229
Encoding and Transfer of Information 230
Forms of Encoding 230
Transfer of Information from Short-Term Memory to Long-Term Memory 233
ix
x
Contents
n Practical Applications of Cognitive Psychology: Memory Strategies
238
Retrieval 242
Retrieval from Short-Term Memory 242
n Investigating Cognitive Psychology: Test Your Short-Term Memory
242
Retrieval from Long-Term Memory 244
Intelligence and Retrieval 246
Processes of Forgetting and Memory Distortion
Interference Theory 247
246
n Investigating Cognitive Psychology: Can You Recall Bartlett’s Legend? 249
n Investigating Cognitive Psychology: The Serial-Position Curve 250
n Investigating Cognitive Psychology: Primacy and Recency Effects 250
Decay Theory
251
The Constructive Nature of Memory
Autobiographical Memory 253
252
n Believe It or Not: Caught in the Past!?
256
Memory Distortions
256
n In the Lab of Elizabeth Loftus
260
The Effect of Context on Memory
Key Themes
Summary
263
266
266
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
267
268
Media Resources
268
CHAPTER 7
The Landscape of Memory: Mental Images, Maps,
and Propositions 269
n Believe It or Not: City Maps of Music for the Blind
270
Mental Representation of Knowledge 271
Communicating Knowledge: Pictures versus Words 273
n Investigating Cognitive Psychology: Representations in Pictures and Words
275
Pictures in Your Mind: Mental Imagery 276
Dual-Code Theory: Images and Symbols 277
n
n
n
n
Investigating Cognitive Psychology: Can Your Brain Store Images of Your Face? 277
Investigating Cognitive Psychology: Analogical and Symbolic Representations of Cats 279
Investigating Cognitive Psychology: Dual Coding 279
In the Lab of Stephen Kosslyn 280
Storing Knowledge as Abstract Concepts: Propositional Theory 281
Do Propositional Theory and Imagery Hold Up to Their Promises? 283
Mental Manipulations of Images 287
Principles of Visual Imagery 287
Neuroscience and Functional Equivalence 288
Mental Rotations 289
n Investigating Cognitive Psychology: Try Your Skills at Mental Rotation
Zooming in on Mental Images: Image Scaling
294
292
Contents
n Investigating Cognitive Psychology: Image Scaling 294
n Investigating Cognitive Psychology: Image Scanning 295
Examining Objects: Image Scanning
Representational Neglect 298
296
Synthesizing Images and Propositions 299
Do Experimenters’ Expectations Influence Experiment Outcomes? 299
Johnson-Laird’s Mental Models 301
Neuroscience: Evidence for Multiple Codes 304
Spatial Cognition and Cognitive Maps 308
Of Rats, Bees, Pigeons, and Humans 308
n Practical Applications of Cognitive Psychology: Dual Codes
Rules of Thumb for Using Our Mental Maps: Heuristics
308
310
n Believe It or Not: Memory Test? Don’t Compete with Chimpanzees!
n Investigating Cognitive Psychology: Mental Maps 314
Creating Maps from What You Hear: Text Maps
Key Themes
Summary
311
314
316
316
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
318
318
Media Resources
318
CHAPTER 8
The Organization of Knowledge in the Mind
n Believe It or Not: There Is a Savant in All of Us
Declarative versus Procedural Knowledge
320
319
321
n Investigating Cognitive Psychology: Testing Your Declarative and
Procedural Knowledge 321
Organization of Declarative Knowledge
Concepts and Categories 323
322
n Believe It or Not: Some Numbers Are Odd, and Some Are Odder
328
Semantic-Network Models 332
Schematic Representations 336
n Investigating Cognitive Psychology: Scripts—The Doctor 338
n Practical Applications of Cognitive Psychology: Scripts in Your Everyday Life
Representations of How We Do Things: Procedural Knowledge
The “Production” of Procedural Knowledge 340
Nondeclarative Knowledge 342
n Investigating Cognitive Psychology: Procedural Knowledge
n Investigating Cognitive Psychology: Priming 343
342
Integrative Models for Representing Declarative and
Nondeclarative Knowledge 344
Combining Representations: ACT-R 344
Parallel Processing: The Connectionist Model 348
How Domain General or Domain Specific Is Cognition? 354
340
339
xi
xii
Contents
n In the Lab of James L. McClelland
Key Themes
Summary
355
355
356
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
357
357
Media Resources
358
CHAPTER 9
Language
359
n Believe It or Not: Do the Chinese Think about Numbers Differently than Americans?
360
What Is Language? 361
Properties of Language 361
The Basic Components of Words 365
The Basic Components of Sentences 367
n Investigating Cognitive Psychology: Syntax
367
Understanding the Meaning of Words, Sentences, and Larger Text Units
368
Language Comprehension 368
Understanding Words 369
n Investigating Cognitive Psychology: Understanding Schemas
Understanding Meaning: Semantics
n Believe It or Not: Can It Really Be Hard to Stop Cursing?
Understanding Sentences: Syntax
n
n
n
n
373
374
375
377
Investigating Cognitive Psychology: Your Sense of Grammar 378
In the Lab of Steven Pinker 380
Investigating Cognitive Psychology: Syntax 381
Practical Applications of Cognitive Psychology: Speaking with
Non-Native English Speakers 385
Reading 386
When Reading Is a Problem—Dyslexia 386
Perceptual Issues in Reading 387
Lexical Processes in Reading 388
Understanding Conversations and Essays: Discourse
n Investigating Cognitive Psychology: Discourse 392
n Investigating Cognitive Psychology: Deciphering Text
392
393
Comprehending Known Words: Retrieving Word Meaning from Memory 393
n Investigating Cognitive Psychology: Effects of Expectations in Reading
394
Comprehending Unknown Words: Deriving Word Meanings from Context
Comprehending Ideas: Propositional Representations 395
Comprehending Text Based on Context and Point of View 396
Representing the Text in Mental Models 396
395
n Investigating Cognitive Psychology: Using Redundancy to Decipher Cryptic Text
Key Themes
Summary
398
398
398
Thinking about Thinking: Analytical, Creative, and Practical Questions
400
Contents
Key Terms
xiii
400
Media Resources
400
CHAPTER 10
Language in Context
401
n Believe It or Not: Is It Possible to Count Without Words for Numbers?
402
Language and Thought 403
Differences among Languages 403
n Believe It or Not: Do You See Colors to Your Left Differently than Colors to Your Right?
n In the Lab of Keith Rayner 411
Bilingualism and Dialects 412
Slips of the Tongue 418
Metaphorical Language 419
Language in a Social Context
421
n Investigating Cognitive Psychology: Language in Different Contexts
Speech Acts 423
Characteristics of Successful Conversations
Gender and Language 426
422
426
n Practical Applications of Cognitive Psychology: Improving Your
Communication with Others 429
Do Animals Have Language?
429
Neuropsychology of Language 432
Brain Structures Involved in Language 432
Aphasia 436
Autism 438
Key Themes
Summary
439
440
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
441
Media Resources
441
CHAPTER 11
Problem Solving and Creativity
442
n Believe It or Not: Can Novices Have An Advantage Over Experts?
The Problem-Solving Cycle
443
444
Types of Problems 447
Well-Structured Problems 447
n Investigating Cognitive Psychology: Move Problems
Ill-Structured Problems and the Role of Insight
447
454
Obstacles and Aids to Problem Solving 460
Mental Sets, Entrenchment, and Fixation 460
n Investigating Cognitive Psychology: Luchins’s Water-Jar Problems
461
441
408
xiv
Contents
Negative and Positive Transfer
462
n Investigating Cognitive Psychology: Problems Involving Transfer
Incubation 465
Neuroscience and Planning during Problem Solving
Intelligence and Complex Problem Solving 466
Expertise: Knowledge and Problem Solving
Organization of Knowledge 468
n In the Lab of K. Anders Ericsson
462
466
468
472
Innate Talent and Acquired Skill 474
Artificial Intelligence and Expertise 476
Creativity 479
What Are the Characteristics of Creative People? 480
n Believe It or Not: Does the Field You’re in Predict When You Will Do Your Best Work?
n Investigating Cognitive Psychology: Creativity in Problem-Solving 483
Neuroscience and Creativity
Key Themes
Summary
483
484
484
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
486
Media Resources
486
CHAPTER 12
Decision Making and Reasoning 487
n Believe It or Not: Can a Simple Rule of Thumb Outsmart a Nobel Laureate’s
Investment Strategy? 488
n Investigating Cognitive Psychology: The Conjunction Fallacy 488
Judgment and Decision Making 489
Classical Decision Theory 489
Heuristics and Biases 490
n Investigating Cognitive Psychology: Framing Effects
497
Fallacies 499
The Gist of It: Do Heuristics Help Us or Lead Us Astray?
Opportunity Costs 502
Naturalistic Decision Making 502
Group Decision Making 502
n In the Lab of Gerd Gigerenzer
503
Neuroscience of Decision Making
505
Deductive Reasoning 507
What Is Deductive Reasoning? 507
Conditional Reasoning 507
Syllogistic Reasoning: Categorical Syllogisms 513
Aids and Obstacles to Deductive Reasoning 517
n Practical Applications of Cognitive Psychology: Improving Your
Deductive Reasoning Skills 519
501
485
482
Contents
Inductive Reasoning 519
What Is Inductive Reasoning? 519
Causal Inferences 521
Categorical Inferences 521
Reasoning by Analogy 522
An Alternative View of Reasoning
Neuroscience of Reasoning
524
523
n Investigating Cognitive Psychology: When There Is No “Right” Choice
Key Themes
Summary
525
526
527
Thinking about Thinking: Analytical, Creative, and Practical Questions
Key Terms
528
Media Resources
Glossary
529
530
References
538
Name Index
Subject Index
593
603
528
xv
To the Instructor
Welcome to the Sixth Edition of Cognitive Psychology. This edition is now coauthored by Karin Sternberg, PhD. As you will see, this edition underwent a major
revision. We reorganized and meticulously revised all chapters with the goal of providing an even more comprehensible text that integrates the latest research but also
retains students’ interest by providing more examples from other areas of research
and from the real world.
What Are the Goals of this Book?
Cognitive psychologists study a wide range of psychological phenomena, such as perception, learning, memory, and thinking. In addition, cognitive psychologists study
seemingly less cognitively oriented phenomena, such as emotion and motivation. In
fact, almost any topic of psychological interest may be studied from a cognitive perspective. In this textbook, we describe some of the preliminary answers to questions
asked by researchers in the main areas of cognitive psychology. The goals of this
book are to:
• present the field of cognitive psychology in a comprehensive but engaging
manner;
• integrate the presentation of the field under the general banner of human
intelligence; and
• interweave throughout the text key themes and key ideas that permeate cognitive psychology.
Our Mission in Revising the Text
A number of goals guided us through revising Cognitive Psychology. In particular we
decided to:
• make the text more accessible and understandable;
• make cognitive psychology more fascinating and less intimidating;
• increase coverage of applications in other areas of psychology as well as in the
real world; and
• better integrate coverage of human intelligence and cognitive neuroscience in
each chapter.
Key Themes and Ideas
The key themes of this book, discussed in greater detail in Chapter 1, are:
1. nature versus nurture;
2. rationalism versus empiricism;
xvi
To the Instructor
3.
4.
5.
6.
7.
xvii
structures versus processes;
domain generality versus domain specificity;
validity of causal inferences versus ecological validity;
applied versus basic research; and
biological versus behavioral methods.
The key ideas of this book, also discussed at more length in Chapter 1, are as
follows:
1. Empirical data and theories are both important. Data in cognitive psychology
can be fully understood only in the context of an explanatory theory, but theories are empty without empirical data.
2. Cognition is generally adaptive but not in all specific instances.
3. Cognitive processes interact with each other and with non-cognitive processes.
4. Cognition needs to be studied through a variety of scientific methods.
5. All basic research in cognitive psychology may lead to applications, and all
applied research may lead to basic understandings.
Major Organizing and Special Pedagogical Features
Special features, some new and some established, characterize Cognitive Psychology
Sixth Edition. Here are the new features:
• Believe It or Not feature boxes present incredible and exciting information and
facts from the world of cognitive psychology.
• A “Neuroscience and …” section in every chapter.
• An “Intelligence and …” section in every chapter integrates the theme of
intelligence with the chapter topic at hand. The separate intelligence chapter,
formerly Chapter 13, has been eliminated.
• Concept Checks follow each major section to encourage students to quickly
check their comprehension.
And here are some of the established features:
• Practical Applications of Cognitive Psychology feature boxes help students think
about applications of cognitive psychology in their own lives.
• Investigating Cognitive Psychology features present mini-experiments and tasks that
students can complete on their own.
What’s New to the 6th Edition
Cognitive Psychology, 6th edition underwent a major revision to make the book more
comprehensible, accessible, and interesting to students. Revision highlights include:
• Revised In the Lab features, including new profiles of Henry Roediger, III in
Chapter 1; Martha Farah in Chapter 2; Marvin Chun in Chapter 3; and Keith
Rayner in Chapter 10.
• Believe It or Not boxes now appear in every chapter to make cognitive psychology more fascinating and less intimidating to students and to show it can be fun
and surprising.
xviii
To the Instructor
• The Practical Applications boxes now conclude with a critical thinking question.
• Concept Checks now appear after each major section.
• Updated Suggested Readings are now preceded by headings so students can
quickly find what they are interested in.
• Key experiments are now clearly highlighted in Investigating Cognitive Psychology
boxes.
• Thoroughly integrated intelligence coverage (formerly Chapter 13, Intelligence)
now appears throughout the 6th edition.
• Advance organizers added to improve the reading flow and students’ understanding of how things fit together into a larger context.
• Updated chapter organization for greater comprehensibility.
• Reduced coverage of cognitive development and other non-cognitive topics
more accurately reflect the focus of cognitive psychology courses.
• New subheadings increase understanding of content matter and larger context.
Chapter-specific revisions include:
Chapter 1
1. An all new introduction to intelligence in Chapter 1 discusses what intelligence
is, how intelligence relates to cognition, and three cognitive models of intelligence (Carroll, Gardner, Sternberg).
2. New everyday examples include analyzing why companies spend so much money
on advertising products that students use, for example, Apple iPhone and
Windows 7.
3. New example in section on why learning about psychology’s history is important: a discussion on newspapers’ coverage of the success of educational
programs, hardly any which use control groups.
4. New example of how nurture influences cognition by comparing Western and
Asian cultures.
5. Expanded discussion of rationalism vs. empiricism now includes Plato and
Aristotle.
6. Expanded explanation of Descartes’ views.
7. Enhanced introduction to section on early dialectics and explanation of what
dialectics are.
8. Expanded explanation of what being a structuralist means in terms of
psychology.
9. Expanded discussion of introspection.
10. Explanation of Ebbinghaus’s experiment and new Ebbinghaus’s forgetting curve
figure.
11. New example from contemporary times has been added to the section on behaviorism explaining how reward and punishment are used in modern
psychotherapy.
12. New section on criticisms of behaviorism.
13. New Believe It or Not box on scientific “progress” in the first half of the 20th
century and the introduction of prefrontal lobotomies.
14. New explanation of why behaviorists regarded the mind as a “black box”.
15. New In the Lab of Henry L. Roediger, III feature.
16. New coverage of control variables.
17. New explanation of why control over experimental conditions is important.
To the Instructor
xix
18. Expanded section on when to use correlational studies and discuss their potential shortcomings.
19. New section on how other professions and fields benefit from findings in cognitive psychology.
Chapter 2
1. New organization: Now a section on the anatomy and mechanisms of the brain
discusses the structure of the brain first before going into details regarding neuronal structure and function; a second section then discusses research methods/
methods of viewing the brain; a third section discusses brain disorders; and a
fourth (new) section covers intelligence and neuroscience.
2. New In the Lab of Martha Farah box.
3. Updated discussion of the function of brain parts reflects the latest literature.
4. Expanded explanation of how autism relates to the function of the amygdala.
5. Reorganized discussion of the hippocampus.
6. Updated and expanded information on the function of the hypothalamus.
7. New coverage of the evolution of the human brain.
8. Updated and expanded coverage of the lateralization of function.
9. New explanation of vocabulary frequently used to describe brain regions: dorsal,
caudal, rostral, ventral.
10. The concept of “action potential” is now discussed.
11. Expanded coverage of myelin and Nodes of Ranvier.
12. Updated coverage of neurotransmitters to reflect current status of knowledge.
13. New coverage of genetic knockout studies and neurochemical ways to induce
particular lesions in the section on animal studies.
14. New coverage of “noise” in EEG recordings, and how this noise can be dealt
with by averaging recordings.
15. New detailed example of a study using ERP to help students understand the
technique.
16. New explanation of the N400 effect.
17. Updated discussion of research and imaging methods, including new references.
18. Expanded information on CT scans, angiography, and MRIs.
19. More detailed explanation of the subtraction method.
20. New explanation of how DTI works.
21. Expanded section on TMS and introduced concept of rTMS.
22. Brain disorders discussion now begins by explaining why brain disorders are of
importance to finding out how the brain works.
23. New section (part of former Chapter 13, Intelligence) on intelligence and neuroscience that discusses the connection between intelligence and (a) brain size,
(b) neurons, (c) brain metabolism as well as biological bases of intelligence testing and the P-FIT theory of intelligence.
Chapter 3
1. New “hands-on” activity now opens chapter by asking students to look out of
the window to see for themselves how objects that are farther away look small,
even if they are huge.
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To the Instructor
2. Reorganized chapter first presents basics of perception, perceptual illusions, and
how our visual system works; then, the theories of perception, perception of
objects and forms, perceptual constancies; and last, deficits in perception.
3. New introduction to “From Sensation to Perception” discussion illustrates with
two examples how complex perception can be.
4. New In the Lab of Marvin Chun feature box.
5. New coverage of the Ganzfeld effect and experiment to experience the Ganzfeld
effect.
6. New discussion of light as a precondition for vision, and about the spectrum of
light waves and which ones humans can see.
7. Reorganized coverage of how our visual system works.
8. Visual pathways discussion expanded, updated, and now appears near the beginning of the chapter.
9. New introduction to approaches to perception (that is, the part about
theories), and a more thorough explanation of what bottom-up and top-down
approaches are.
10. Direct perception is now discussed as part of bottom-up theories discussion.
11. New sections on the everyday importance of neuroscience and direct perception.
12. New section discusses template theory as an example of a chunk-based theory
and connects visual perception with long-term memory.
13. New section on neuroscience and template theories.
14. New discussion of why it is so hard for computers to read handwriting.
15. Updated coverage of pandemonium model and updated coverage of the localprecedence effect.
16. Expanded coverage of neuroscience and feature-matching theories.
17. New section on neuroscience and recognition-by-components theory.
18. Top-down theories section now includes discussion of intelligence and
perception.
19. Expanded coverage of elaboration/explanation of object-centered versus viewercentered representation.
20. Reorganized discussion of Gestalt approach section.
21. Reorganized discussion of the neuroscience of recognizing faces and patterns.
22. New neuropsychological research on perceptual constancies.
23. New coverage of stereoscopic seeing with just one eye in strabismic eyes.
24. Expanded coverage of neuroscience and depth perception, with new research
results.
25. Reorganized discussion of ataxias and agnosias separately discusses “difficulties in
perceiving the what” and “difficulties in knowing the how”.
26. New section on perception in practice with respect to traffic and accidents.
Chapter 4
1. Reorganized chapter first presents attention (signal detection, vigilance, search,
selective attention, and divided attention), then discusses what happens when
attentional processes fail; habituation and adaptation, as well as automatic and
controlled processes in attention are explored; and last, consciousness.
2. Included new introductory example for introduction to signal detection and vigilance: lifeguard on beach and research psychologist.
3. Expanded coverage of neuroscience and vigilance.
To the Instructor
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
xxi
New research on feature integration theory.
Expanded coverage of the neuroscience of visual search and aging.
Updated discussion of selective attention.
Expanded discussion of neuroscience and selective attention.
Divided attention now integrates information regarding human intelligence.
Updated and reorganized coverage of theories of divided attention.
Revised network model discussion in “Neuroscience and Attention” section.
New section on intelligence and attention includes discussion of reaction time
and inspection time.
Reorganized and updated discussion of section “When our attention fails us”
includes a discussion of Gardner’s theory of intelligence as potentially relevant
to ADHD treatment.
Updated discussion of change blindness and inattentional blindness.
Updated coverage of “extinction” in spatial neglect as well as updated information on neuroscience research in spatial neglect.
“Controlled and Automatic Processes” section has been reorganized and
updated.
Sternberg’s triarchic theory of intelligence now connected to controlled and
automatic processes.
The Stroop effect is now featured in “automatization in daily life”.
Updated discussion of consciousness.
Chapter 5
1. New discussion of intelligence testing and culture that describes problems of
culture-fair testing and how memory abilities may differ across different cultural
groups.
2. New coverage of long-term store and new techniques that are being developed
to help students transfer learned facts into long-term memory.
3. Expanded coverage of how experiments were conducted on the levelsof-processing approach and what their results mean (in particular, why people
with schizophrenia have memory problems).
4. Fisher & Craik (1977) experiment about the effectiveness of acoustic and semantic retrieval has been elaborated more, with examples to make clear the differences between the different kinds of retrieval.
5. Expanded coverage of the phonological loop.
6. New section on intelligence and working memory.
7. New discussion of neuropsychological coverage added to the section on amnesia.
8. New explanation of double dissociation.
9. Updated coverage in section on how memories are stored.
10. Expanded explanation of the term long-term potentiation.
Chapter 6
1.
2.
3.
4.
5.
Updated research on long-term storage.
Expanded neuropsychological coverage of section on long-term storage.
New section explaining the difference between interference and decay.
Expanded coverage of the spacing effect.
Expanded coverage of organization of information.
xxii
To the Instructor
6. Expanded coverage of forcing functions and their use in hospitals.
7. Expanded coverage and new figure on neuropsychological experiments on
retrieval from long-term memory.
8. Expanded coverage of the “recent-probes task”.
9. Expanded coverage of flashbulb memory and the effect of mood on memory.
10. Updated research on memory distortions.
11. Updated research on eyewitness testimony; expanded coverage and new introduction of the post-identification feedback effect.
12. Expanded coverage of children as eyewitnesses and lineups.
13. Updated research on context effects.
Chapter 7
1.
2.
3.
4.
5.
6.
7.
8.
9.
Revised coverage of internal and external representations.
Updated research on mental imagery.
New research on mental rotations.
Updated coverage of gender and mental rotation.
Updated coverage of research on image scanning.
Updated research on section “synthesizing images and propositions”.
Updated coverage of demand characteristics.
Updated discussion of Johnson-Laird’s mental models.
Updated discussion of mental shortcuts.
Chapter 8
1. Updated research on concepts.
2. Updated research on prototypes.
3. New coverage of VAM (varying abstraction model) theory in the exemplars
discussion.
4. New discussion of concepts in different cultures.
5. Updated research on scripts, ACT-R, and the PDP model.
6. Expanded section on criticism of connectionist models.
Chapter 9
1. New discussion of reading and discourse have been added to this chapter (previously chapter 10).
2. New introduction to section “What is language” discusses how many languages
there are in the world, that still new languages are being discovered, etc.
3. Updated research on basic components of words.
4. New introduction to the section on processes of language comprehension.
5. Updated research on section “the view of speech perception as ordinary”.
6. New coverage of new research to explain the phenomenon of phonemic
restoration.
7. Updated discussion of the motor theory of speech perception.
8. Updated section on the McGurk effect with the latest neuropsychological
research.
9. Updated coverage of semantics.
To the Instructor
10.
11.
12.
13.
14.
15.
16.
17.
18.
xxiii
Updated research in the section on syntactical priming.
More in-depth description of the Luka & Barsalou (2005) experiment.
Expanded explanations of phrase-structure grammar.
Expanded explanation of the critique of Chomsky’s theory.
Updated research on dyslexia.
Updated research on lexical processes in reading.
New section on intelligence and lexical access speed (from previous chapter 13).
Updated research on propositional representations.
Updated research on “Representing the Text in Mental Models.”
Chapter 10
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
New coverage of animal language (formerly in Chapter 9).
Reorganized discussion of the neuropsychology of language.
New In the Lab of Keith Rayner boxed feature.
New coverage in colors discussion includes recent research and demonstrates
how one’s language can influence color perception.
New research in section on verbs and grammatical gender features description of
new research experiments on grammatical gender and prepositions.
New neuropsychological research on bilinguals.
Updated research on second language acquisition.
Expanded discussion of Meinzer et al. (2007) study.
Updated research on language mixtures and change.
Extended coverage of neuroscience and bilingualism.
Updated research on slips of the tongue.
New coverage of Steven Pinker’s new theory of indirect speech.
Updated research on gender and language.
Updated and revised coverage of animal language.
New coverage of the brain and word recognition.
New coverage of the brain and semantic processing.
Expanded and updated coverage on the brain and syntax.
Updated and extended coverage of the brain and language acquisition.
Updated and extended coverage on the plasticity of the brain.
New and updated research on the brain and gender difference in language
processing.
Updated research on autism.
Chapter 11
1.
2.
3.
4.
5.
6.
Reorganized discussion of the problem-solving cycle.
Streamlined discussion of well-structured problems.
Updated section on problem representation.
Streamlined discussion of insight.
Streamlined discussion of the early Gestaltist view.
Expanded discussion of the Metcalfe (1986) experiment covered in the section
on the neo-Gestaltist view.
7. Coverage of neuroscience and insight aggregated into a neuroscience section,
expanded, and updated.
8. Streamlined discussion of intentional transfer.
xxiv
To the Instructor
9. Revised discussion of incubation includes new coverage of a meta-analysis.
10. New discussion of intelligence and complex solving (formerly chapter 13).
11. Section on expertise has been updated and an experiment on beer tasting in
experts and novices has been added.
12. Updated discussion of automatic expert processes.
13. Updated coverage of innate talent and acquired skill.
14. New and updated coverage of artificial intelligence and expertise (formerly
chapter 13).
15. Updated and streamlined coverage of creativity.
16. Updated discussion of neuroscience and creativity.
Chapter 12
1. Reorganized discussion of judgment and decision making for improved
comprehension.
2. New explanation of the difference between the model of economic man and
woman and subjective expected utility theory.
3. Streamlined discussion of subjective expected utility theory.
4. Streamlined and updated coverage of satisficing now includes a comparison with
classical decision theory.
5. Updated discussion of framing effects.
6. Updated coverage of gambler’s fallacy and hot hand.
7. Updated discussion of the evaluation of heuristics.
8. Updated section on naturalistic decision making.
9. Expanded discussion of evolution and reasoning.
10. Updated and streamlined coverage of syllogisms.
11. Streamlined discussion of inductive reasoning.
12. Streamlined section on reaching causal inferences.
13. Updated section on categorical inferences.
14. Updated coverage of an alternative view of reasoning.
15. Updated and expanded section on the neuroscience of reasoning.
Ancillaries
As an instructor, you have a multitude of resources available to you to assist you in
the teaching of your class. Student ancillaries are also offered. Available resources
include:
Instructor’s Manual with Test Bank—Written by Donna Dahlgren of Indiana
University Southeast. The Instructor’s Manual portion contains chapter outlines, in-class demonstrations, discussion topics, and suggested websites. The
Test Bank portion consists of approximately 75 multiple choice and 20 shortanswer questions per chapter. Each multiple-choice item is labeled with the
page reference and level of difficulty.
PowerLecture with ExamView—With the one-stop digital library and presentation tool, instructors can assemble, edit, and present custom lectures with ease.
The PowerLecture, contains a selection of digital media from Wadsworth’s latest
titles in introductory psychology, including figures and tables. Create, deliver,
To the Instructor
xxv
and customize printed and online tests and study guides in minutes with ExamView’s easy-to-use assessment and tutorial system. Also included are animations,
video clips, and preassembled Microsoft PowerPoint lecture slides, written by
Lise Abrams of University of Florida, based on each specific text. Instructors
can use the material or add their own material for a truly customized lecture
presentation.
CogLab 3.0—Free with every new copy of this book, CogLab 3.0 lets students
do more than just think about cognition. CogLab 3.0 uses the power of the web
to teach concepts using important classic and current experiments that demonstrate how the mind works. Nothing is more powerful for students than seeing
the effects of these experiments for themselves! CogLab 3.0 includes features
such as simplified student registration, a global database that combines data
from students all around the world, between-subject designs that allow for new
kinds of experiments, and a “quick display” of student summaries. Also included
are trial-by-trial data, standard deviations, and improved instructions.
And when you adopt Sternberg’s COGNITIVE PSYCHOLOGY, you and your students will have access to a rich array of online teaching and learning resources that
you won’t find anywhere else. The outstanding site features tutorial quizzes, a glossary, weblinks, flashcards, and more!
Acknowledgments
We are grateful to a number of reviewers who have contributed to the development
of this book:
Jane L. Pixley, Radford University
Martha J. Hubertz, Florida Atlantic
University
Jeffrey S. Anastasi, Sam Houston
State University
Robert J. Crutcher, University of
Dayton
Eric C. Odgaard, University of
South Florida
Takashi Yamauchi, Texas A & M
University
David C. Somers, Boston University
Michael J. McGuire, Washburn
University
Kimberly Rynearson, Tarleton State
University
A special thank you goes to Gerd Gigerenzer and Julian Marewski for their helpful
review of, and comments on, Chapter 12.
We would also like to thank Ann Greenberger, developmental editor, as well as
all members of our Wadsworth/Cengage Learning editorial and production teams:
Jaime Perkins, Acquisitions Editor; Paige Leeds, Assistant Editor; Lauren Keyes,
Media Editor; Beth Kluckhohn, Senior Project Manager for PreMedia Global;
Tangelique Williams, Developmental Editor; Matt Ballantyne, Senior Content Project Manager; and Jessica Alderman, Editorial Assistant.
To the Student
Why do we remember people whom we met years ago, but sometimes seem to forget
what we learned in a course shortly after we take the final exam (or worse, sometimes right before)? How do we manage to carry on a conversation with one person
at a party and simultaneously eavesdrop on another more interesting conversation
taking place nearby? Why are people so often certain that they are correct in answering a question when in fact they are not? These are just three of the many questions that are addressed by the field of cognitive psychology.
Cognitive psychologists study how people perceive, learn, remember, and think.
Although cognitive psychology is a unified field, it draws on many other fields, most
notably neuroscience, computer science, linguistics, anthropology, and philosophy.
Thus, you will find some of the thinking of all these fields represented in this
book. Moreover, cognitive psychology interacts with other fields within psychology,
such as psychobiology, developmental psychology, social psychology, and clinical
psychology.
For example, it is difficult to be a clinical psychologist today without a solid
knowledge of developments in cognitive psychology because so much of the thinking in the clinical field draws on cognitive ideas, both in diagnosis and in therapy.
Cognitive psychology has also provided a means for psychologists to investigate experimentally some of the exciting ideas that have emerged from clinical theory and
practice, such as notions of unconscious thought.
Cognitive psychology will be important to you not only in its own right, but
also in helping you in all of your work. For example, knowledge of cognitive psychology can help you better understand how best to study for tests, how to read effectively, and how to remember difficult-to-learn material.
Cognitive psychologists study a wide range of psychological phenomena such as
perception, learning, memory, and thinking. In addition, cognitive psychologists
study seemingly less cognitively oriented phenomena, such as emotion and motivation. In fact, almost any topic of psychological interest may be studied from a cognitive perspective. In this textbook we describe some of the preliminary answers to
questions asked by researchers in the main areas of cognitive psychology.
• Chapter 1, Introduction to Cognitive Psychology: What are the origins of cognitive
psychology, and how do people do research in this field?
• Chapter 2, Cognitive Neuroscience: What structures and processes of the human
brain underlie the structures and processes of human cognition?
• Chapter 3, Visual Perception: How does the human mind perceive what the
senses receive? How does the human mind perceive forms and patterns?
• Chapter 4, Attention and Consciousness: What basic processes of the mind govern how information enters our minds, our awareness, and our high-level
processes of information handling?
• Chapter 5, Memory: Models and Research Methods: How are different kinds of
information (e.g., our experiences related to a traumatic event, the names of
U.S. presidents, or the procedure for riding a bicycle) represented in memory?
xxvi
To the Student
xxvii
• Chapter 6, Memory Processes: How do we move information into memory, keep
it there, and retrieve it from memory when needed?
• Chapter 7, The Landscape of Memory: Mental Images, Maps, and Propositions:
How do we mentally represent information in our minds? Do we do so in words,
in pictures, or in some other form representing meaning? Do we have multiple
forms of representation?
• Chapter 8, The Organization of Knowledge in the Mind: How do we mentally
organize what we know?
• Chapter 9, Language: How do we derive and produce meaning through
language?
• Chapter 10, Language in Context: How does our use of language interact with
our ways of thinking? How does our social world interact with our use of
language?
• Chapter 11, Problem Solving and Creativity: How do we solve problems? What
processes aid and impede us in reaching solutions to problems? Why are some
of us more creative than others? How do we become and remain creative?
• Chapter 12, Decision Making and Reasoning: How do we reach important decisions? How do we draw reasonable conclusions from the information we have
available? Why and how do we so often make inappropriate decisions and reach
inaccurate conclusions?
To acquire the knowledge outlined above, we suggest you make use of the following pedagogical features of this book:
1. Chapter outlines, beginning each chapter, summarize the main topics covered and
thus give you an advance overview of what is to be covered in that chapter.
2. Opening questions emphasize the main questions each chapter addresses.
3. Boldface terms, indexed at ends of chapters and defined in the glossary, help you
acquire the vocabulary of cognitive psychology.
4. End-of-chapter summaries return to the questions at the opening of each chapter
and show our current state of knowledge with regard to these questions.
5. End-of-chapter questions help you ensure both that you have learned the basic
material and that you can think in a variety of ways (factual, analytical, creative, and practical) with this material.
6. Suggested readings refer you to other sources that you can consult for further
information on the topics covered in each chapter.
7. Investigating Cognitive Psychology demonstrations, appearing throughout the
chapters, help you see how cognitive psychology can be used to demonstrate
various psychological phenomena.
8. Practical Applications of Cognitive Psychology demonstrations show how you and
others can apply cognitive psychology to your everyday lives.
9. In the Lab of . . . boxes tell you what it really is like to do research in cognitive
psychology. Prominent researchers speak in their own words about their
research—what research problems excite them most and what they are doing
to address these problems.
10. Believe It or Not boxes present incredible and exciting information and facts
from the world of cognitive psychology.
11. Key Themes sections, near the end of each chapter, relate the content of the
chapters to the key themes expressed in Chapter 1. These sections will help
xxviii
To the Student
you see the continuity of the main ideas of cognitive psychology across its various subfields.
12. CogLab, an exciting series of laboratory demonstrations in cognitive psychology
provided by the publisher of this textbook (Wadsworth), is available for purchase with this text. You can actively participate in these demonstrations and
thereby learn firsthand what it is like to be involved in cognitive-psychological
research.
This book contains an overriding theme that unifies all the diverse topics found
in the various chapters: Human cognition has evolved over time as a means of
adapting to our environment, and we can call this ability to adapt to the environment intelligence. Through intelligence, we cope in an integrated and adaptive way
with the many challenges with which the environment presents us.
Although cognitive psychologists disagree about many issues, there is one issue
about which almost all of them agree; namely, cognition enables us to successfully
adapt to the environments in which we find ourselves. Thus, we need a construct
such as that of human intelligence, if only to provide a shorthand way of expressing
this fundamental unity of adaptive skill. We can see this unity at all levels in the
study of cognitive psychology. For example, diverse measures of the psychophysiological functioning of the human brain show correlations with scores on a variety of
tests of intelligence. Selective attention, the ability to tune in certain stimuli and
tune out others, is also related to intelligence, and it has even been proposed that
an intelligent person is one who knows what information to attend to and what information to ignore. Various language and problem-solving skills are also related to
intelligence, pretty much without regard to how it is measured. In brief, then, human intelligence can be seen as an entity that unifies and provides direction to the
workings of the human cognitive system.
We hope you enjoy this book, and we hope you see why we are enthusiastic
about cognitive psychology and proud to be cognitive psychologists.
About the Authors
Robert J. Sternberg is Provost and Senior Vice President as well as Professor of Psychology at Oklahoma State University. Prior to that, he was Dean of the School of
Arts and Sciences and Professor of Psychology at Tufts University, and before that,
IBM Professor of Psychology and Education in the Department of Psychology at Yale
University.
Dr. Sternberg received his B.A. from Yale and his Ph.D. in Psychology from Stanford
University. He also holds 11 honorary doctorates.
He has received numerous awards, including the James McKeen Cattell Award from
the American Psychological Society; the Early Career and McCandless Awards from
the APA; and the Outstanding Book, Research Review, Sylvia Scribner and Palmer
O. Johnson Awards from the AERA.
Dr. Sternberg has served as President of the American Psychological Association and
of the Eastern Psychological Association and is currently President-elect of the Federation of Associations of Brain and Behavioral Sciences. In addition, he has been editor of the Psychological Bulletin and of the APA Review of Books: Contemporary
Psychology and is a member of the Society of Experimental Psychologists. He was the
director of the Center for the Psychology of Abilities, Competencies, and Expertise at
Yale University and then Tufts University.
Karin Sternberg is Adjunct Assistant Professor at Oklahoma State University. She has
a PhD in psychology from the University of Heidelberg, Germany, as well as an MBA
with a specialization in banking from the University of Cooperative Education in
Karlsruhe, Germany. Karin did some of her doctoral research at Yale and her postdoctoral work in psychology at the University of Connecticut. Afterwards, she worked as a
research associate at Harvard University’s Kennedy School of Government and School
of Public Health. In 2008, together with her husband, Robert J. Sternberg, she founded
Sternberg Consulting. The company’s focus is on applying in practice their theories of
intelligence, wisdom, creativity, and leadership, among others. This has led to consulting work and product development based on their theories (e.g., admissions tests for
higher education institutions and schools, training programs, etc.).
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1
C
H
A
P
T
E
R
Introduction to Cognitive Psychology
CHAPTER OUTLINE
Cognitive Psychology Defined
Philosophical Antecedents of Psychology:
Rationalism versus Empiricism
Psychological Antecedents
of Cognitive Psychology
Early Dialectics in the Psychology of Cognition
Understanding the Structure of the Mind:
Structuralism
Understanding the Processes of the Mind:
Functionalism
An Integrative Synthesis: Associationism
It’s Only What You Can See That Counts:
From Associationism to Behaviorism
Proponents of Behaviorism
Criticisms of Behaviorism
Behaviorists Daring to Peek into the Black Box
The Whole Is More Than the Sum of Its Parts:
Gestalt Psychology
Emergence of Cognitive Psychology
Early Role of Psychobiology
Add a Dash of Technology: Engineering,
Computation, and Applied Cognitive Psychology
Three Cognitive Models of Intelligence
Carroll: Three-Stratum Model of Intelligence
Gardner: Theory of Multiple Intelligences
Sternberg: The Triarchic Theory
of Intelligence
Research Methods in Cognitive Psychology
Goals of Research
Distinctive Research Methods
Experiments on Human Behavior
Psychobiological Research
Self-Reports, Case Studies,
and Naturalistic Observation
Computer Simulations and Artificial Intelligence
Putting It All Together
Fundamental Ideas in Cognitive Psychology
Key Themes in Cognitive Psychology
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
Cognition and Intelligence
What Is Intelligence?
1
2
CHAPTER 1 • Introduction to Cognitive Psychology
Here are some of the questions we will explore in this chapter:
1.
2.
3.
4.
What is cognitive psychology?
How did psychology develop as a science?
How did cognitive psychology develop from psychology?
How have other disciplines contributed to the development of theory and research in cognitive
psychology?
5. What methods do cognitive psychologists use to study how people think?
6. What are the current issues and various fields of study within cognitive psychology?
n BELIEVE IT OR NOT
NOW YOU SEE IT, NOW YOU DON’T!
Cognitive psychology yields all kinds of surprising findings. Dan Simons of the University of Illinois is a master
of surprises (see Simons, 2007; Simons & Ambinder,
2005; Simons & Rensink, 2005). Try it out yourself!
Watch the following videos and see if you have any comments on them.
http://viscog.beckman.illinois.edu/flashmovie/23.php
Note: Do not read on before you have watched the video.
Did you notice that the person who answers the phone is
not the same as the one who was at the desk? Note that
they are wearing distinctively different clothing. You have
just seen an example of change blindness—our occasional inability to recognize changes. You will learn
more about this concept in Chapter 3.
Now view the following video. Your task will be to count
the number of times that students in white shirts pass the
basketball. You must not count passes by students wearing black shirts:
http://viscog.beckman.illinois.edu/flashmovie/15.php
Note: Do not read on before you have watched the video.
Well, it doesn’t really matter how many passes there
were. Did you notice the person in the gorilla outfit walk
across the video as the students were throwing the balls?
Most people don’t notice. This video demonstrates a
phenomenon called inattentional blindness. You will
learn more about this concept in Chapter 4. Throughout
this book, we will explore these and many other
phenomena.
Think back to the last time you went to a party or social gathering. There were
probably tens and maybe hundreds of students in a relatively small room. Maybe
music played in the background, and you could hear chatter all around. Yet, when
you talked to your friends, you were able to figure out and even concentrate on what
they said, filtering out all the other conversations that were going on in the
background. Suddenly, however, your attention might have shifted because you
heard someone in another conversation nearby mention your name. What processes
would have been at work in this situation? How were you able to filter out irrelevant
voices in your mind and focus your attention on just one of the many voices you
heard? And why did you notice your name being mentioned, even though you did
3
Kane Skennar/Digital/Vision/Getty Images
Cognitive Psychology Defined
When you are at a party, you are usually able to filter out many irrelevant voice streams in order to
concentrate on the conversation you are leading. However, you will likely notice somebody saying your
name in another conversation even if you were not listening intently to that conversation.
not purposefully listen to the conversations around you? Our ability to focus on one
out of many voices is one of the most striking phenomena in cognitive psychology,
and is known as the “cocktail party effect.”
Cognitive processes are continuously taking place in your mind and in the minds
of the people around you. Whether you pay attention to a conversation, estimate the
speed of an approaching car when crossing the street, or memorize information for a
test at school, you are perceiving information, processing it, and remembering or
thinking about it. This book is about those cognitive processes that are often hidden
in plain sight and that we take for granted because they seem so automatic to us. This
chapter will introduce you to some of the people who helped form the field of cognitive
psychology and make it what it is today. The chapter also will discuss methods used in
cognitive-psychological research.
Cognitive Psychology Defined
What will you study in a textbook about cognitive psychology?
Cognitive psychology is the study of how people perceive, learn, remember, and
think about information. A cognitive psychologist might study how people perceive
various shapes, why they remember some facts but forget others, or how they learn
language. Consider some examples:
• Why do objects look farther away on foggy days than they really are? The discrepancy can be dangerous, even deceiving drivers into having car accidents.
• Why do many people remember a particular experience (e.g., a very happy
moment or an embarrassment during childhood), yet they forget the names of
people whom they have known for many years?
4
CHAPTER 1 • Introduction to Cognitive Psychology
• Why are many people more afraid of traveling in planes than in automobiles?
After all, the chances of injury or death are much higher in an automobile
than in a plane.
• Why do you often well remember people you met in your childhood but not
people you met a week ago?
• Why do marketing executives in large companies spend so much company
money on advertisements?
These are some of the kinds of questions that we can answer through the study
of cognitive psychology.
Consider just the last of these questions: Why does Apple, for example, spend
so much money on advertisements for its iPhone? After all, how many people
remember the functional details of the iPhone, or how those functions are distinguished from the functions of other phones? One reason Apple spends so much is
because of the availability heuristic, which you will study in Chapter 12. Using this
heuristic, we make judgments on the basis of how easily we can call to mind what
we perceive as relevant instances of a phenomenon (Tversky & Kahneman, 1973).
One such judgment is the question of which phone you should buy when you
need a new cell phone. We are much more likely to buy a brand and model of a
phone that is familiar. Similarly, Microsoft paid huge amounts of money to market
its roll-out of Windows 7 in order to make the product cognitively available
to potential customers and thus increase the chances that the potential customers would become actual ones. The bottom line is that understanding
cognitive psychology can help us understand much of what goes on in our everyday lives.
Why study the history of cognitive psychology? If we know where we came
from, we may have a better understanding of where we are heading. In addition,
we can learn from past mistakes. For example, there are numerous newspaper stories
about how one educational program or another has resulted in particular gains in
student achievement. However, it is relatively rare to read that a control group has
been used. A control group would tell us about the achievement of students who did
not have that educational program or who maybe were in an alternative program. It
may be that these students also would show a gain. We need to compare the students in the experimental group to those in the control group to determine whether
the gain of the students in the experimental group was greater than the gain of those
in the control group. We can learn from the history of our field that it is important
to include control groups, but not everyone learns this fact.
In cognitive psychology, the ways of addressing fundamental issues have changed, but many of the fundamental questions remain much the same. Ultimately, cognitive psychologists hope to learn how people think by studying how people have
thoughts about thinking.
The progression of ideas often involves a dialectic. A dialectic is a developmental
process where ideas evolve over time through a pattern of transformation. What is
this pattern? In a dialectic:
• A thesis is proposed. A thesis is a statement of belief. For example, some people
believe that human nature governs many aspects of human behavior (e.g., intelligence or personality; Sternberg, 1999). After a while, however, certain individuals notice apparent flaws in the thesis.
Cognitive Psychology Defined
5
• An antithesis emerges. Eventually, or perhaps even quite soon, an antithesis
emerges. An antithesis is a statement that counters a previous statement of
belief. For example, an alternative view is that our nurture (the environmental
contexts in which we are reared) almost entirely determines many aspects of
human behavior.
• A synthesis integrates the viewpoints. Sooner or later, the debate between the
thesis and the antithesis leads to a synthesis. A synthesis integrates the most
credible features of each of two (or more) views. For example, in the debate
over nature versus nurture, the interaction between our innate (inborn) nature
and environmental nurture may govern human nature.
The dialectic is important because we may be tempted to think that if one view
is right, another seemingly contrasting view must be wrong. For example, in the field
of intelligence, there has been a tendency to believe that intelligence is either all or
mostly genetically determined, or else all or mostly environmentally determined.
A similar debate has raged in the field of language acquisition. Often, we are better
off posing such issues not as either/or questions, but rather as examinations of how
different forces covary and interact with each other. Indeed, the most widely accepted current contention is that the “nature or nurture” view is incomplete. Nature
and nurture work together in our development.
Nurture can work in different ways in different cultures. Some cultures, especially Asian cultures, tend to be more dialectical in their thinking, whereas other
cultures, such as European and North American ones, tend to be more linear
(Nisbett, 2003). In other words, Asians are more likely to be tolerant of holding
beliefs that are contradictory, seeking a synthesis over time that resolves the contradiction. Europeans and Americans expect their belief systems to be consistent
with each other.
Similarly, people from Asian cultures tend to take a different viewpoint than
Westerners when approaching a new object (e.g., a movie of fish in an ocean;
Nisbett & Masuda, 2003). In general, people from Western cultures tend to process
objects independently of the context, whereas people from many Eastern
cultures process objects in conjunction with the surrounding context (Nisbett &
Miyamoto, 2005). Asians may emphasize the context more than the objects embedded in those contexts. So if people see a movie of fish swimming around in the
ocean, Europeans or Americans will tend to pay more attention to the fish, and
Asians may attend to the surround of the ocean in which the fish are swimming.
The evidence suggests that culture influences many cognitive processes, including
intelligence (Lehman, Chiu, & Schaller, 2004).
If a synthesis seems to advance our understanding of a subject, it then serves as a
new thesis. A new antithesis then follows it, then a new synthesis, and so on. Georg
Hegel (1770–1831) observed this dialectical progression of ideas. He was a German
philosopher who came to his ideas by his own dialectic. He synthesized some of the
views of his intellectual predecessors and contemporaries. You will see in this chapter that psychology also evolved as a result of dialectics: Psychologists had ideas
about how the mind works and pursued their line of research; then other psychologists pointed out weaknesses and developed alternatives as a reaction to the earlier
ideas. Eventually, characteristics of the different approaches are often integrated into
a newer and more encompassing approach.
6
CHAPTER 1 • Introduction to Cognitive Psychology
Philosophical Antecedents of Psychology:
Rationalism versus Empiricism
Where and when did the study of cognitive psychology begin? Historians of psychology usually trace the earliest roots of psychology to two approaches to understanding
the human mind:
• Philosophy seeks to understand the general nature of many aspects of the world,
in part through introspection, the examination of inner ideas and experiences
(from intro-, “inward, within,” and -spect, “look”);
• Physiology seeks a scientific study of life-sustaining functions in living matter,
primarily through empirical (observation-based) methods.
Two Greek philosophers, Plato (ca. 428–348 B.C.) and his student Aristotle
(384–322 B.C.), have profoundly affected modern thinking in psychology and
many other fields. Plato and Aristotle disagreed regarding how to investigate ideas.
Plato was a rationalist. A rationalist believes that the route to knowledge is
through thinking and logical analysis. That is, a rationalist does not need any experiments to develop new knowledge. A rationalist who is interested in cognitive processes would appeal to reason as a source of knowledge or justification.
In contrast, Aristotle (a naturalist and biologist as well as a philosopher) was an
empiricist. An empiricist believes that we acquire knowledge via empirical evidence—
that is, we obtain evidence through experience and observation (Figure 1.1). In order
to explore how the human mind works, empiricists would design experiments and
conduct studies in which they could observe the behavior and processes of interest
to them. Empiricism therefore leads directly to empirical investigations of psychology.
In contrast, rationalism is important in theory development. Rationalist theories
without any connection to observations gained through empiricist methods may not
be valid; but mountains of observational data without an organizing theoretical
framework may not be meaningful. We might see the rationalist view of the world
as a thesis and the empirical view as an antithesis. Most psychologists today seek a
synthesis of the two. They base empirical observations on theory in order to explain
(a)
(b)
Figure 1.1 (a) According to the rationalist, the only route to truth is reasoned contemplation; (b) according to the
empiricist, the only route to truth is meticulous observation. Cognitive psychology, like other sciences, depends on the
work of both rationalists and empiricists.
Psychological Antecedents of Cognitive Psychology
7
what they have observed in their experiments. In turn, they use these observations
to revise their theories when they find that the theories cannot account for their
real-world observations.
The contrasting ideas of rationalism and empiricism became prominent with the
French rationalist René Descartes (1596–1650) and the British empiricist John
Locke (1632–1704). Descartes viewed the introspective, reflective method as being
superior to empirical methods for finding truth. The famous expression “cogito, ergo
sum” (I think, therefore I am) stems from Descartes. He maintained that the only
proof of his existence is that he was thinking and doubting. Descartes felt that one
could not rely on one’s senses because those very senses have often proven to be
deceptive (think of optical illusions, for example). Locke, in contrast, had more enthusiasm for empirical observation (Leahey, 2003). Locke believed that humans are
born without knowledge and therefore must seek knowledge through empirical observation. Locke’s term for this view was tabula rasa (meaning “blank slate” in
Latin). The idea is that life and experience “write” knowledge on us. For Locke,
then, the study of learning was the key to understanding the human mind. He believed that there are no innate ideas.
In the eighteenth century, German philosopher Immanuel Kant (1724–1804)
dialectically synthesized the views of Descartes and Locke, arguing that both rationalism and empiricism have their place. Both must work together in the quest for
truth. Most psychologists today accept Kant’s synthesis.
Psychological Antecedents of Cognitive Psychology
Cognitive psychology has roots in many different ideas and approaches. The approaches that will be examined include early approaches such as structuralism and
functionalism, followed by a discussion of associationism, behaviorism, and Gestalt
psychology.
Early Dialectics in the Psychology of Cognition
Only in recent times did psychology emerge as a new and independent field of study.
It developed in a dialectical way. Typically, an approach to studying the mind would
be developed; people then would use it to explore the human psyche. At some point,
however, researchers would find that the approach they learned to use had some weaknesses, or they would disagree with some fundamental assumptions of that approach.
They then would develop a new approach. Future approaches might integrate the
best features of past approaches or reject some or even most of those characteristics.
In the following section, we will explore some of the ways of thinking early psychologists employed and trace the development of psychology through the various schools of
thinking.
Understanding the Structure of the Mind: Structuralism
An early dialectic in the history of psychology is that between structuralism and functionalism (Leahey, 2003; Morawski, 2000). Structuralism was the first major school of
thought in psychology. Structuralism seeks to understand the structure (configuration of elements) of the mind and its perceptions by analyzing those perceptions
into their constituent components (affection, attention, memory, sensation, etc.).
8
CHAPTER 1 • Introduction to Cognitive Psychology
Consider, for example, the perception of a flower.
Structuralists would analyze this perception in terms of its
constituent colors, geometric forms, size relations, and so on.
In terms of the human mind, structuralists sought to deconstruct the mind into its elementary components; they were
also interested in how those elementary components work
together to create the mind.
Wilhelm Wundt (1832–1920) was a German psychologist
whose ideas contributed to the development of structuralism.
Wundt is often viewed as the founder of structuralism in psychology (Structuralism, 2009). Wundt used a variety of methods
in his research. One of these methods was introspection. Introspection is a deliberate looking inward at pieces of information
Image not available due to copyright restrictions
passing through consciousness. The aim of introspection is to
look at the elementary components of an object or process.
The introduction of introspection as an experimental
method was an important change in the field because the
main emphasis in the study of the mind shifted from a rationalist approach to the empiricist approach of trying to
observe behavior in order to draw conclusions about the
subject of study. In experiments involving introspection, individuals reported on their thoughts as they were working on
a given task. Researchers interested in problem solving could
ask their participants to think aloud while they were working
on a puzzle so the researchers could gain insight into the
thoughts that go on in the participants’ minds. In introspection, then, we can analyze our own perceptions.
The method of introspection has some challenges associated with it. First, people may not always be able to say exactly what goes through their mind or may not
be able to put it into adequate words. Second, what they say may not be accurate.
Third, the fact that people are asked to pay attention to their thoughts or to speak
out loud while they are working on a task may itself alter the processes that are
going on.
Wundt had many followers. One was an American student, Edward Titchener
(1867–1927). Titchener (1910) is sometimes viewed as the first full-fledged structuralist.
In any case, he certainly helped bring structuralism to the United States. His experiments relied solely on the use of introspection, exploring psychology from the vantage
point of the experiencing individual. Other early psychologists criticized both the
method (introspection) and the focus (elementary structures of sensation) of structuralism. These critiques gave rise to a new movement—functionalism.
Understanding the Processes of the Mind: Functionalism
An alternative that developed to counter structuralism, functionalism suggested that
psychologists should focus on the processes of thought rather than on its contents.
Functionalism seeks to understand what people do and why they do it. This principal
question about processes was in contrast to that of the structuralists, who had asked
what the elementary contents (structures) of the human mind are. Functionalists
held that the key to understanding the human mind and behavior was to study the
processes of how and why the mind works as it does, rather than to study the
Psychological Antecedents of Cognitive Psychology
9
structural contents and elements of the mind. They were particularly interested in the practical applications of their research.
Functionalists were unified by the kinds of questions
they asked but not necessarily by the answers they found or
by the methods they used for finding those answers. Because
functionalists believed in using whichever methods best answered a given researcher’s questions, it seems natural for
functionalism to have led to pragmatism. Pragmatists
believe that knowledge is validated by its usefulness: What
can you do with it? Pragmatists are concerned not only with
knowing what people do; they also want to know what we
can do with our knowledge of what people do. For example,
Image not available due to copyright restrictions
pragmatists believe in the importance of the psychology of
learning and memory. Why? Because it can help us improve
the performance of children in school. It can also help us
learn to remember the names of people we meet.
A leader in guiding functionalism toward pragmatism
was William James (1842–1910). His chief functional contribution to the field of psychology was a single book: his
landmark Principles of Psychology (1890/1970). Even today,
cognitive psychologists frequently point to the writings of
James in discussions of core topics in the field, such as attention, consciousness, and perception. John Dewey (1859–1952)
was another early pragmatist who profoundly influenced contemporary thinking in cognitive psychology. Dewey is remembered primarily for his pragmatic approach to thinking and schooling.
Although functionalists were interested in how people learn, they did not really
specify a mechanism by which learning takes place. This task was taken up by another group, Associationists.
An Integrative Synthesis: Associationism
Associationism, like functionalism, was more of an influential way of thinking than
a rigid school of psychology. Associationism examines how elements of the mind,
P R A C T I C A L A P P L I C A T I O N S OF CO G N I T I V E P S Y C H O L O G Y
PRAGMATISM
Take a moment right now to put the idea of pragmatism into use. Think about ways to
make the information you are learning in this course more useful to you. Notice that the
chapter begins with questions that make the information more coherent and useful, and
the chapter summary returns to those questions. Come up with your own questions and try
organizing your notes in the form of answers to your questions.
Also, try relating this material to other courses or activities you participate in. For example, you may be called on to explain to a friend how to use a new computer program.
A good way to start would be to ask your friend, “Do you have any questions?” That
way, the information you provide is more directly useful to your friend rather than forcing
your friend to search for the information by listening to a long, one-sided lecture.
How can pragmatism be useful in your life (other than in your college coursework)?
CHAPTER 1 • Introduction to Cognitive Psychology
like events or ideas, can become associated with one another in the mind to result in
a form of learning. For example, associations may result from:
• contiguity (associating things that tend to occur together at about the same
time);
• similarity (associating things with similar features or properties); or
• contrast (associating things that show polarities, such as hot/cold, light/dark, day/
night).
In the late 1800s, associationist Hermann Ebbinghaus (1850–1909) was the
first experimenter to apply associationist principles systematically. Specifically,
Ebbinghaus studied his own mental processes. He made up lists of nonsense
syllables that consisted of a consonant and a vowel followed by another consonant
(e.g., zax). He then took careful note of how long it took him to memorize
those lists. He counted his errors and recorded his response times. Through his
self-observations, Ebbinghaus studied how people learn and remember material
through rehearsal, the conscious repetition of material to be learned (Figure 1.2).
Among other things, he found that frequent repetition can fix mental associations
more firmly in memory. Thus, repetition aids in learning (see Chapter 6).
Another influential associationist, Edward Lee Thorndike (1874–1949), held
that the role of “satisfaction” is the key to forming associations. Thorndike termed
this principle the law of effect (1905): A stimulus will tend to produce a certain response over time if an organism is rewarded for that response. Thorndike believed
that an organism learns to respond in a given way (the effect) in a given situation
if it is rewarded repeatedly for doing so (the satisfaction, which serves as a stimulus
to future actions). Thus, a child given treats for solving arithmetic problems learns
to solve arithmetic problems accurately because the child forms associations between
valid solutions and treats. These ideas were the predecessors of the development of
behaviorism.
Ebbinghaus
Forgetting Curve
% of Data Remembered
100
90
80
70
60
50
0
Photo © Bettmann/CORBIS
10
5th Repetition
20
4th Repetition
30
3rd Repetition
40
1st Repetition
2nd Repetition
10
Figure 1.2 The Ebbinghaus Forgetting Curve shows that the first few repetitions result in
a steep learning curve. Later repetitions result in a slower increase of remembered words.
Psychological Antecedents of Cognitive Psychology
11
It’s Only What You Can See That Counts:
From Associationism to Behaviorism
Other researchers who were contemporaries of Thorndike used animal experiments
to probe stimulus–response relationships in ways that differed from those of Thorndike and his fellow associationists. These researchers straddled the line between
associationism and the emerging field of behaviorism. Behaviorism focuses only on
the relation between observable behavior and environmental events or stimuli. The
idea was to make physical whatever others might have called “mental” (Lycan,
2003). Some of these researchers, like Thorndike and other associationists, studied
responses that were voluntary (although perhaps lacking any conscious thought, as
in Thorndike’s work). Other researchers studied responses that were involuntarily
triggered in response to what appear to be unrelated external events.
In Russia, Nobel Prize–winning physiologist Ivan Pavlov (1849–1936) studied
involuntary learning behavior of this sort. He began with the observation that dogs
salivated in response to the sight of the lab technician who fed them. This response
occurred before the dogs even saw whether the technician had food. To Pavlov, this
response indicated a form of learning (classically conditioned learning), over which
the dogs had no conscious control. In the dogs’ minds, some type of involuntary
learning linked the technician to the food (Pavlov, 1955). Pavlov’s landmark work
paved the way for the development of behaviorism. His ideas were made known in
the United States especially through the work of John B. Watson (see next section).
Classical conditioning involves more than just an association based on temporal
contiguity (e.g., the food and the conditioned stimulus occurring at about the same
time; Ginns, 2006; Rescorla, 1967). Effective conditioning requires contingency (e.g.,
the presentation of food being contingent on the presentation of the conditioned
stimulus; Rescorla & Wagner, 1972; Wagner & Rescorla, 1972). Contingencies in
the form of reward and punishment are still used today, for example, in the treatment of substance abuse (Cameron & Ritter, 2007).
Behaviorism may be considered an extreme version of associationism. It focuses
entirely on the association between the environment and an observable behavior.
According to strict, extreme (“radical”) behaviorists, any hypotheses about internal
thoughts and ways of thinking are nothing more than speculation.
Proponents of Behaviorism
The “father” of radical behaviorism is John Watson (1878–1958). Watson had no
use for internal mental contents or mechanisms. He believed that psychologists
should concentrate only on the study of observable behavior (Doyle, 2000). He dismissed thinking as nothing more than subvocalized speech. Behaviorism also differed
from previous movements in psychology by shifting the emphasis of experimental
research from human to animal participants. Historically, much behaviorist work
has been conducted (and still is) with laboratory animals, such as rats or pigeons,
because these animals allow for much greater behavioral control of relationships
between the environment and the behavior emitted in reaction to it (although
behaviorists also have conducted experiments with humans). One problem with
using nonhuman animals, however, is determining whether the research can be
generalized to humans (i.e., applied more generally to humans instead of just to the
kinds of nonhuman animals that were studied).
B. F. Skinner (1904–1990), a radical behaviorist, believed that virtually all
forms of human behavior, not just learning, could be explained by behavior emitted
12
CHAPTER 1 • Introduction to Cognitive Psychology
in reaction to the environment. Skinner conducted research primarily with nonhuman animals. He rejected mental mechanisms. He believed instead that operant
conditioning—involving the strengthening or weakening of behavior, contingent on
the presence or absence of reinforcement (rewards) or punishments—could explain
all forms of human behavior. Skinner applied his experimental analysis of behavior
to many psychological phenomena, such as learning, language acquisition, and problem solving. Largely because of Skinner’s towering presence, behaviorism dominated
the discipline of psychology for several decades.
Criticisms of Behaviorism
Behaviorism was challenged on many fronts like language acquisition, production,
and comprehension. First, although it seemed to work well to account for certain
kinds of learning, behaviorism did not account as well for complex mental activities
such as language learning and problem solving. Second, more than understanding
people’s behavior, some psychologists wanted to know what went on inside the
head. Third, it often proved easier to use the techniques of behaviorism in studying
nonhuman animals than in studying human ones. Nonetheless, behaviorism continues as a school of psychology, although not one that is particularly sympathetic
to the cognitive approach, which involves metaphorically and sometimes literally
peering inside people’s heads to understand how they learn, remember, think, and
reason. Other criticisms emerged as well, as discussed in the next section.
Behaviorists Daring to Peek into the Black Box
Some psychologists rejected radical behaviorism. They were curious about the contents of the mysterious black box. Behaviorists regarded the mind as a black box that
is best understood in terms of its input and output, but whose internal processes cannot be accurately described because they are not observable. For example, a critic,
Edward Tolman (1886–1959), thought that understanding behavior required taking
into account the purpose of, and the plan for, the behavior. Tolman (1932) believed
n BELIEVE IT OR NOT
SCIENTIFIC PROGRESS!?
The progress of science can take quite unbelievable turns
at times. From the early 1930s to the 1960s, lobotomies
were a popular and accepted means of treating mental
disorders. A lobotomy involves cutting the connections between the frontal lobes of the brain and the thalamus.
Psychiatrist Walter Freeman developed a particular kind
of lobotomy in 1946—the transorbital or “ice pick” lobotomy. In this procedure, he used an instrument that looked
like an ice pick and inserted it through the orbit of the eyes
into the frontal lobes where it was moved back and forth.
The patient had been previously rendered unconscious by
means of a strong electrical shock. By the late 1950s,
tens of thousands of Americans had been subjected to this
“psychosurgery.” According to some accounts, people felt
reduced tension and anxiety after the surgery; however,
there were many people who died or were permanently
incapacitated after the lobotomy. Famous lobotomy
patients include John F. Kennedy’s sister Rosemary. Unbelievably, lobotomy was even performed on patients who
were not aware they were receiving the surgery. The
shocking story of Howard Dully, who was lobotomized
at age 12 and did not find out about the procedure until
much later in life, can be found at
http://www.npr.org/templates/story/story
.php?storyId=5014080 (Helmes & Velamoor,
2009; MSNBC, 2005).
Emergence of Cognitive Psychology
13
that all behavior is directed toward a goal. For example, the goal of a rat in a maze
may be to try to find food in that maze. Tolman is sometimes viewed as a forefather
of modern cognitive psychology.
Bandura (1977b) noted that learning appears to result not merely from direct
rewards for behavior, but it also can be social, resulting from observations of the rewards or punishments given to others. The ability to learn through observation is
well documented and can be seen in humans, monkeys, dogs, birds, and even fish
(Brown & Laland, 2001; Laland, 2004). In humans, this ability spans all ages; it is
observed in both infants and adults (Mejia-Arauz, Rogoff, & Paradise, 2005). This
view emphasizes how we observe and model our own behavior after the behavior
of others. We learn by example. This consideration of social learning opens the
way to considering what is happening inside the mind of the individual.
The Whole Is More Than the Sum
of Its Parts: Gestalt Psychology
Of the many critics of behaviorism, Gestalt psychologists may have been among the
most avid. Gestalt psychology states that we best understand psychological phenomena when we view them as organized, structured wholes. According to this view, we
cannot fully understand behavior when we only break phenomena down into smaller parts. For example, behaviorists tended to study problem solving by looking for
subvocal processing—they were looking for the observable behavior through which
problem solving can be understood. Gestaltists, in contrast, studied insight, seeking
to understand the unobservable mental event by which someone goes from having
no idea about how to solve a problem to understanding it fully in what seems a mere
moment of time.
The maxim “the whole is more than the sum of its parts” aptly sums up the
Gestalt perspective. To understand the perception of a flower, for example, we
would have to take into account the whole of the experience. We could not understand such a perception merely in terms of a description of forms, colors, sizes, and so
on. Similarly, as noted in the previous paragraph, we could not understand problem
solving merely by looking at minute elements of observable behavior (Köhler, 1927,
1940; Wertheimer, 1945/1959). We will have a closer look at Gestalt principles in
Chapter 3.
Emergence of Cognitive Psychology
In the early 1950s, a movement called the “cognitive revolution” took place in response to behaviorism. Cognitivism is the belief that much of human behavior can
be understood in terms of how people think. It rejects the notion that psychologists
should avoid studying mental processes because they are unobservable. Cognitivism
is, in part, a synthesis of earlier forms of analysis, such as behaviorism and Gestaltism. Like behaviorism, it adopts precise quantitative analysis to study how people
learn and think; like Gestaltism, it emphasizes internal mental processes.
14
CHAPTER 1 • Introduction to Cognitive Psychology
Early Role of Psychobiology
Ironically, one of Watson’s former students, Karl Spencer Lashley (1890–1958),
brashly challenged the behaviorist view that the human brain is a passive organ
merely responding to environmental contingencies outside the individual (Gardner,
1985). Instead, Lashley considered the brain to be an active, dynamic organizer of
behavior. Lashley sought to understand how the macro-organization of the human
brain made possible such complex, planned activities as musical performance, game
playing, and using language. None of these activities were, in his view, readily explicable in terms of simple conditioning.
In the same vein, but at a different level of analysis, Donald Hebb (1949)
proposed the concept of cell assemblies as the basis for learning in the brain. Cell
assemblies are coordinated neural structures that develop through frequent stimulation. They develop over time as the ability of one neuron (nerve cell) to stimulate
firing in a connected neuron increases. Behaviorists did not jump at the opportunity
to agree with theorists like Lashley and Hebb. In fact, behaviorist B. F. Skinner
(1957) wrote an entire book describing how language acquisition and usage could
be explained purely in terms of environmental contingencies. This work stretched
Skinner’s framework too far, leaving Skinner open to attack. An attack was indeed
forthcoming. Linguist Noam Chomsky (1959) wrote a scathing review of Skinner’s
ideas. In his article, Chomsky stressed both the biological basis and the creative
potential of language. He pointed out the infinite numbers of sentences we can
produce with ease. He thereby defied behaviorist notions that we learn language
by reinforcement. Even young children continually are producing novel sentences
for which they could not have been reinforced in the past.
Add a Dash of Technology: Engineering, Computation,
and Applied Cognitive Psychology
By the end of the 1950s, some psychologists were intrigued by the tantalizing notion
that machines could be programmed to demonstrate the intelligent processing of information (Rychlak & Struckman, 2000). Turing (1950) suggested that soon it
would be hard to distinguish the communication of machines from that of humans.
He suggested a test, now called the “Turing test,” by which a computer program
would be judged as successful to the extent that its output was indistinguishable, by
humans, from the output of humans (Cummins & Cummins, 2000). In other words,
suppose you communicated with a computer and you could not tell that it was a
computer. The computer then passed the Turing test (Schonbein & Bechtel, 2003).
By 1956 a new phrase had entered our vocabulary. Artificial intelligence (AI) is
the attempt by humans to construct systems that show intelligence and, particularly,
the intelligent processing of information (Merriam-Webster’s Collegiate Dictionary,
2003). Chess-playing programs, which now can beat most humans, are examples of
artificial intelligence. However, experts greatly underestimated how difficult it would
be to develop a computer that can think like a human being. Even today, computers
have trouble reading handwriting and understanding and responding to spoken language with the ease that humans do.
Many of the early cognitive psychologists became interested in cognitive psychology through applied problems. For example, according to Berry (2002), Donald
Broadbent (1926–1993) claimed to have developed an interest in cognitive
15
Harris, S./www.CartoonStock.com
Emergence of Cognitive Psychology
psychology through a puzzle regarding AT6 aircraft. The planes had two almost
identical levers under the seat. One lever was to pull up the wheels and the other
to pull up the flaps. Pilots apparently regularly mistook one for the other, thereby
crashing expensive planes upon take-off. During World War II, many cognitive
psychologists, including one of the senior author’s advisors, Wendell Garner, consulted
with the military in solving practical problems of aviation and other fields that arose
out of warfare against enemy forces. Information theory, which sought to understand
people’s behavior in terms of how they process the kinds of bits of information
processed by computers (Shannon & Weaver, 1963), also grew out of problems in
engineering and informatics.
Applied cognitive psychology also has had great use in advertising. John
Watson, after he left Johns Hopkins University as a professor, became an extremely successful executive in an advertising firm and applied his knowledge of
psychology to reach his success. Indeed, much of advertising has directly used principles from cognitive psychology to attract customers to products (Benjamin & Baker,
2004).
By the early 1960s, developments in psychobiology, linguistics, anthropology,
and artificial intelligence, as well as the reactions against behaviorism by many
mainstream psychologists, converged to create an atmosphere ripe for revolution.
16
CHAPTER 1 • Introduction to Cognitive Psychology
Early cognitivists (e.g., Miller, Galanter, & Pribram, 1960; Newell, Shaw, & Simon,
1957b) argued that traditional behaviorist accounts of behavior were inadequate precisely because they said nothing about how people think. One of the most famous
early articles in cognitive psychology was, oddly enough, on “the magic number
seven.” George Miller (1956) noted that the number seven appeared in many different places in cognitive psychology, such as in the literature on perception and
memory, and he wondered whether there was some hidden meaning in its frequent
reappearance. For example, he found that most people can remember about seven
items of information. In this work, Miller also introduced the concept of channel
capacity, the upper limit with which an observer can match a response to information given to him or her. For example, if you can remember seven digits presented
to you sequentially, your channel capacity for remembering digits is seven. Ulric
Neisser’s book Cognitive Psychology (Neisser, 1967) was especially critical in bringing
cognitivism to prominence by informing undergraduates, graduate students, and
academics about the newly developing field.
Neisser defined cognitive psychology as the study of how people learn, structure,
store, and use knowledge. Subsequently, Allen Newell and Herbert Simon (1972)
proposed detailed models of human thinking and problem solving from the most
basic levels to the most complex. By the 1970s cognitive psychology was recognized
widely as a major field of psychological study with a distinctive set of research
methods.
In the 1970s, Jerry Fodor (1973) popularized the concept of the modularity of
mind. He argued that the mind has distinct modules, or special-purpose systems, to
deal with linguistic and, possibly, other kinds of information. Modularity implies
that the processes that are used in one domain of processing, such as the linguistic
(Fodor, 1973) or the perceptual domain (Marr, 1982), operate independently of
processes in other domains. An opposing view would be one of domain-general processing, according to which the processes that apply in one domain, such as perception or language, apply in many other domains as well. Modular approaches are
useful in studying some cognitive phenomena, such as language, but have proven
less useful in studying other phenomena, such as intelligence, which seems to draw
upon many different areas of the brain in complex interrelationships.
Curiously, the idea of the mind as modular goes back at least to phrenologist
Franz-Joseph Gall (see Boring, 1950), who in the late eighteenth century believed
that the pattern of bumps and swells on the skull was directly associated with one’s
pattern of cognitive skills. Although phrenology itself was not a scientifically valid
technique, the practice of mental cartography lingered and eventually gave rise to
ideas of modularity based on modern scientific techniques.
CONCEPT CHECK
1. What is pragmatism, and how is it related to functionalism?
2. How are associationism and behaviorism both similar and different?
3. What is the fundamental idea behind Gestalt psychology?
4. What is the meaning of modularity of mind?
5. How does cognitivism incorporate elements of the schools that preceded it?
Cognition and Intelligence
17
Cognition and Intelligence
Human intelligence can be viewed as an integrating, or “umbrella” psychological
construct for a great deal of theory and research in cognitive psychology. Intelligence is the capacity to learn from experience, using metacognitive processes to
enhance learning, and the ability to adapt to the surrounding environment. It may
require different adaptations within different social and cultural contexts. People
who are more intelligent tend to be superior in processes such as divided and selective attention, working memory, reasoning, problem solving, decision making, and
concept formation. So when we come to understand the mental processes involved
in each of these cognitive functions, we also better understand the bases of individual differences in human intelligence.
What Is Intelligence?
Before you read about how cognitive psychologists view intelligence, test your own
intelligence with the tasks in Investigating Cognitive Psychology: Intelligence.
Each of the tasks in Investigating Cognitive Psychology is believed, at least by some
cognitive psychologists, to require some degree of intelligence. (The answers are at
the end of this section.) Intelligence is a concept that can be viewed as tying together all of cognitive psychology. Just what is intelligence, beyond the basic definition? In a recent article, researchers identified approximately 70 different definitions
of intelligence (Legg & Hutter, 2007). In 1921, when the editors of the Journal of
INVESTIGATING COGNITIVE PSYCHOLOGY
Intelligence
1.
Candle is to tallow as tire is to (a) automobile, (b) round, (c) rubber, (d) hollow.
2.
Complete this series: 100%, 0.75, 1/2; (a) whole, (b) one eighth, (c) one fourth.
3.
The first three items form one series. Complete the analogous second series that
starts with the fourth item:
(a)
4.
(b)
(c)
(d)
You are at a party of truth-tellers and liars. The truth-tellers always tell the truth, and
the liars always lie. You meet someone new. He tells you that he just heard a conversation in which a girl said she was a liar. Is the person you met a liar or a truthteller?
18
CHAPTER 1 • Introduction to Cognitive Psychology
Educational Psychology asked 14 famous psychologists that question, the responses
varied but generally embraced these two themes. Intelligence involves:
1. the capacity to learn from experience, and
2. the ability to adapt to the surrounding environment.
Sixty-five years later, 24 cognitive psychologists with expertise in intelligence research
were asked the same question (Sternberg & Detterman, 1986). They, too, underscored the
importance of learning from experience and adapting to the environment. They also
broadened the definition to emphasize the importance of metacognition—people’s understanding and control of their own thinking processes. Contemporary experts also more
heavily emphasized the role of culture. They pointed out that what is considered intelligent in one culture may be considered stupid in another culture (Serpell, 2000).
There are actually a number of cultural differences in the definition of intelligence.
These differences have led to a field of study within intelligence research that examines
understanding of cultural differences in the definition of intelligence. This field explores
what is termed cultural intelligence, or CQ. This term is used to describe a person’s ability
to adapt to a variety of challenges in diverse cultures (Ang et al., 2010; Sternberg &
Grigorenko, 2006; Triandis, 2006). Research also shows that personality variables are
related to intelligence (Ackerman, 1996, 2010). Taken together, this evidence suggests
that a comprehensive definition of intelligence incorporates many facets of intellect.
Definitions of intelligence also frequently take on an assessment-oriented focus.
In fact, some psychologists have been content to define intelligence as whatever it is
that the tests measure (Boring, 1923). This definition, unfortunately, is circular. According to it, the nature of intelligence is what is tested. But what is tested must
necessarily be determined by the nature of intelligence. Moreover, what different
tests of intelligence measure is not always the same thing. Different tests measure
somewhat different constructs (Daniel, 1997, 2000; Kaufman, 2000; Kaufman &
Lichtenberger, 1998). So it is not feasible to define intelligence by what tests measure, as though they all measured the same thing. By the way, the answers to the
questions in Investigating Cognitive Psychology: Intelligence are:
1. Rubber. Candles are frequently made of tallow, just as tires are frequently made
of (c) rubber.
2. 100%, 0.75, and 1/2 are quantities that successively decrease by 1/4; to complete
the series, the answer is (c) one fourth, which is a further decrease by 1/4.
3. The first series was a circle and a square, followed by two squares and a circle,
followed by three circles and a square; the second series was three triangles and a
square, which would be followed by (b), four squares and a triangle.
4. The person you met is clearly a liar. If the girl about whom this person was talking were a truth-teller, she would have said that she was a truth-teller. If she
were a liar, she would have lied and said that she was a truth-teller also. Thus,
regardless of whether the girl was a truth-teller or a liar, she would have said
that she was a truth-teller. Because the man you met has said that she said she
was a liar, he must be lying and hence must be a liar.
Three Cognitive Models of Intelligence
There have been many models of intelligence. Three models are particularly useful
when linking human intelligence to cognition: the three-stratum model, the theory
of multiple intelligences, and the triarchic theory of intelligence.
Cognition and Intelligence
19
Carroll: Three-Stratum Model of Intelligence
According to the three-stratum model of intelligence, intelligence comprises a hierarchy of cognitive abilities comprising three strata (Carroll, 1993):
• Stratum I includes many narrow, specific abilities (e.g., spelling ability, speed of
reasoning).
• Stratum II includes various broad abilities (e.g., fluid intelligence, crystallized
intelligence, short-term memory, long-term storage and retrieval, informationprocessing speed).
• Stratum III is just a single general intelligence (sometimes called g).
Of these strata, the most interesting is the middle stratum, which is neither too
narrow nor too all-encompassing.
In the middle stratum are fluid ability and crystallized ability. Fluid ability is speed
and accuracy of abstract reasoning, especially for novel problems. Crystallized ability is
accumulated knowledge and vocabulary (Cattell, 1971). In addition to fluid intelligence and crystallized intelligence, Carroll includes several other abilities in the
middle stratum. They are learning and memory processes, visual perception, auditory
perception, facile production of ideas (similar to verbal fluency), and speed (which
includes both sheer speed of response and speed of accurate responding). Carroll’s
model is probably the most widely accepted of the measurement-based models of intelligence. You will learn about these processes in later chapters.
Gardner: Theory of Multiple Intelligences
Howard Gardner (1983, 1993b, 1999, 2006) has proposed a theory of multiple
intelligences, in which intelligence comprises multiple independent constructs, not
just a single, unitary construct. However, instead of speaking of multiple abilities
that together constitute intelligence (e.g., Thurstone, 1938), this theory distinguishes
eight distinct intelligences that are relatively independent of each other (Table 1.1).
Each is a separate system of functioning, although these systems can interact to produce what we see as intelligent performance. Looking at Gardner’s list of intelligences, you might want to evaluate your own intelligences, perhaps rank ordering
your strengths in each.
Gardner does not entirely dismiss the use of psychometric tests. But the base of
evidence used by Gardner (e.g., the existence of exceptional individuals in one area,
brain lesions that destroy a particular kind of intelligence, or core operations that are
essential to performance of a particular intelligence) does not rely on the factor analysis of various psychometric tests alone. Take a moment to reflect:
• In thinking about your own intelligences, how fully integrated do you believe
them to be?
• How much do you perceive each type of intelligence as depending on any of the
others?
Gardner’s view of the mind is modular. Modularity theorists believe that different abilities—such as Gardner’s intelligences—can be isolated as emanating from
distinct portions or modules of the brain. Thus, a major task of existing and future
research on intelligence is to isolate the portions of the brain responsible for each of
the intelligences. Gardner has speculated as to at least some of these locales, but
hard evidence for the existence of these separate intelligences has yet to be produced. Furthermore, some scientists question the strict modularity of Gardner’s theory (Nettelbeck & Young, 1996). Consider the phenomenon of preserved specific
20
CHAPTER 1 • Introduction to Cognitive Psychology
Table 1.1
Gardner’s Eight Intelligences
On which of Howard Gardner’s eight intelligences do you show the greatest ability? In what
contexts can you use your intelligences most effectively? (After Gardner, 1999.)
Type of Intelligence
Tasks Reflecting This Type of Intelligence
Linguistic intelligence
Used in reading a book; writing a paper, a novel, or a
poem; and understanding spoken words
Logical-mathematical intelligence
Used in solving math problems, in balancing a checkbook, in solving a mathematical proof, and in logical
reasoning
Spatial intelligence
Used in getting from one place to another, in reading
a map, and in packing suitcases in the trunk of a car
so that they all fit into a compact space
Musical intelligence
Used in singing a song, composing a sonata, playing
a trumpet, or even appreciating the structure of a piece
of music
Bodily-kinesthetic intelligence
Used in dancing, playing basketball, running a mile,
or throwing a javelin
Interpersonal intelligence
Used in relating to other people, such as when we try
to understand another person’s behavior, motives, or
emotions
Intrapersonal intelligence
Used in understanding ourselves—the basis for understanding who we are, what makes us tick, and how
we can change ourselves, given our existing
constraints on our abilities and our interests
Naturalist intelligence
Used in understanding patterns in nature
From Multiple Intelligences by Howard Gardner. Copyright © 1993 by Howard Gardner. Reprinted by
permission of Basic Books, a member of Perseus Books, L.L.C.
cognitive functioning in autistic savants. Savants are people with severe social and
cognitive deficits but with corresponding high ability in a narrow domain. They suggest that such preservation fails as evidence for modular intelligences. The narrow
long-term memory and specific aptitudes of savants may not really be intelligent
(Nettelbeck & Young, 1996). Thus, there may be reason to question the intelligence
of inflexible modules.
Sternberg: The Triarchic Theory of Intelligence
Whereas Gardner emphasizes the separateness of the various aspects of intelligence,
Robert Sternberg tends to emphasize the extent to which they work together in his
triarchic theory of human intelligence (Sternberg, 1985a, 1988, 1996b, 1999).
According to the triarchic theory of human intelligence, intelligence comprises
three aspects: creative, analytical, and practical.
• Creative abilities are used to generate novel ideas.
• Analytical abilities ascertain whether your ideas (and those of others) are good
ones.
Cognition and Intelligence
21
• Practical abilities are used to implement the ideas and persuade others of their
value.
Figure 1.3 illustrates the parts of the theory and the interrelationships of the
three parts.
According to the theory, cognition is at the center of intelligence. Information
processing in cognition can be viewed in terms of three different kinds of components. First are metacomponents—higher-order executive processes (i.e., metacognition) used to plan, monitor, and evaluate problem solving. Second are performance
components—lower-order processes used for implementing the commands of the
metacomponents. And third are knowledge-acquisition components—the processes
used for learning how to solve the problems in the first place. The components are
highly interdependent.
Suppose that you were asked to write a term paper. You would use metacomponents for higher-order decisions. Thus, you would use them to decide on a topic,
plan the paper, monitor the writing, and evaluate how well your finished product
succeeds in accomplishing your goals for it. You would use knowledge-acquisition
components for research to learn about the topic. You would use performance components for the actual writing.
Sternberg and his colleagues performed a comprehensive study testing the validity of the triarchic theory and its usefulness in improving performance. They
predicted that matching students’ instruction and assessment to their abilities
would lead to improved performance (Sternberg et al., 1996; Sternberg et al.,
1999). Students were selected for one of five ability patterns: high only in analytical ability, high only in creative ability, high only in practical ability, high in all
three abilities, or not high in any of the three abilities. Then students were assigned at random to one of four instructional groups. Instruction in the groups
emphasized either memory-based, analytical, creative, or practical learning. Then the
memory-based, analytical, creative, and practical achievement of all students was
“Apply…”
“Use…”
“Utilize…”
PRACTICAL
“Analyze…”
“Create…”
“Compare…”
“Invent…”
“Evaluate…”
ANALYTICAL
CREATIVE “Design…”
Figure 1.3 According to Robert Sternberg, intelligence comprises analytical, creative,
and practical abilities. In analytical thinking, we solve familiar problems by using strategies
that manipulate the elements of a problem or the relationships among the elements (e.g.,
comparing, analyzing). In creative thinking, we solve new kinds of problems that require us
to think about the problem and its elements in a new way (e.g., inventing, designing). In
practical thinking, we solve problems that apply what we know to everyday contexts
(i.e., applying, using).
22
CHAPTER 1 • Introduction to Cognitive Psychology
assessed. The researchers found that students who were placed in an instructional
condition that matched their strength in terms of pattern of ability outperformed students who were mismatched. Thus, the prediction of the experiment was confirmed.
For example, a high-analytical student being placed in an instructional condition
that emphasized analytical thinking outperformed a high-analytical student being
placed in an instructional condition that emphasized practical thinking.
Teaching students to use all of their analytic, creative, and practical abilities has
resulted in improved school achievement for every student, whatever their ability
pattern (Grigorenko, Jarvin, & Sternberg, 2002; Sternberg & Grigorenko, 2004;
Sternberg, Torff, & Grigorenko, 1998). One important consideration in light of
such findings is the need for changes in the assessment of intelligence (Sternberg
& Kaufman, 1996). Current measures of intelligence are somewhat one-sided. They
measure mostly analytical abilities. They involve little or no assessment of creative
and practical aspects of intelligence (Sternberg et al., 2000; Wagner, 2000). A more
well-rounded assessment and instruction system could lead to greater benefits of
education for a wider variety of students—a nominal goal of education.
One attempt to accomplish this goal can be seen through the Rainbow Project.
In the Rainbow Project, students completed the SAT and additional assessments.
These additional assessments included measures of creative and practical as well as
of analytical abilities (Sternberg & the Rainbow Project Collaborators, 2006). The
addition of these supplemental assessments resulted in superior prediction of college
grade point average (GPA) as compared with scores on the SAT and high school
GPA. In fact, the new tests doubled the prediction of first-year college GPA obtained just by the SAT. Moreover, the new assessments substantially reduced differences in scores among members of diverse ethnic groups.
We have discussed how human intelligence provides a conceptual base for understanding phenomena in cognitive psychology. What methods do we use to study
these phenomena?
Research Methods in Cognitive Psychology
Researchers employ a variety of research methods. These methods include laboratory
or other controlled experiments, psychobiological research, self-reports, case studies,
naturalistic observation, and computer simulations and artificial intelligence. Each of
these methods will be discussed in detail in this section. To better understand the
specific methods used by cognitive psychologists, one must first grasp the goals of
research in cognitive psychology.
Goals of Research
Briefly, research goals include data gathering, data analysis, theory development,
hypothesis formulation, hypothesis testing, and perhaps even application to settings
outside the research environment. Often researchers simply seek to gather as much
information as possible about a particular phenomenon. They may or may not have
preconceived notions regarding what they may find while gathering the data. Their
research focuses on describing particular cognitive phenomena, such as how people
recognize faces or how they develop expertise.
Data gathering reflects an empirical aspect of the scientific enterprise. Once there
are sufficient data on the cognitive phenomenon of interest, cognitive psychologists
Research Methods in Cognitive Psychology
23
use various methods for drawing inferences from the data. Ideally, they use multiple
converging types of evidence to support their hypotheses. Sometimes, just a quick
glance at the data leads to intuitive inferences regarding patterns that emerge from
those data. More commonly, however, researchers use various statistical means of analyzing the data.
Data gathering and statistical analysis aid researchers in describing cognitive
phenomena. No scientific pursuit could get far without such descriptions. However,
most cognitive psychologists want to understand more than the what of cognition;
most also seek to understand the how and the why of thinking. That is, researchers
seek ways to explain cognition as well as to describe it. To move beyond descriptions, cognitive psychologists must leap from what is observed directly to what can
be inferred regarding observations.
Suppose that we wish to study one particular aspect of cognition. An example
would be how people comprehend information in textbooks. We usually start with a
theory. A theory is an organized body of general explanatory principles regarding a
phenomenon, usually based on observations. We seek to test a theory and thereby to
see whether it has the power to predict certain aspects of the phenomena with
which it deals. In other words, our thought process is, “If our theory is correct,
then whenever x occurs, outcome y should result.” This process results in the generation of hypotheses, tentative proposals regarding expected empirical consequences
of the theory, such as the outcomes of research.
Next, we test our hypotheses through experimentation. Even if particular
findings appear to confirm a given hypothesis, the findings must be subjected to
statistical analysis to determine their statistical significance. Statistical significance
indicates the likelihood that a given set of results would be obtained if only chance
factors were in operation. For example, a statistical significance level of .05 would
mean that the likelihood of a given set of data would be a mere 5% if only chance
factors were operating. Therefore, the results are not likely to be due merely to
chance. Through this method we can decide to retain or reject hypotheses.
Once our hypothetical predictions have been experimentally tested and statistically analyzed, the findings from those experiments may lead to further work. For
example, the psychologist may engage in further data gathering, data analysis, theory
development, hypothesis formulation, and hypothesis testing. Based on the hypotheses that were retained and/or rejected, the theory may have to be revised. In addition, many cognitive psychologists hope to use insights gained from research to help
people use cognition in real-life situations. Some research in cognitive psychology is
applied from the start. It seeks to help people improve their lives and the conditions
under which they live their lives. Thus, basic research may lead to everyday
applications. For each of these purposes, different research methods offer different
advantages and disadvantages.
Distinctive Research Methods
Cognitive psychologists use various methods to explore how humans think. These
methods include (a) laboratory or other controlled experiments, (b) psychobiological
research, (c) self-reports, (d) case studies, (e) naturalistic observation, and (f) computer simulations and artificial intelligence. See Table 1.2 for descriptions and examples of each method. As the table shows, each method offers distinctive advantages
and disadvantages.
24
CHAPTER 1 • Introduction to Cognitive Psychology
IN THE LAB OF HENRY L. ROEDIGER
The Science of the Mind
My students and I have been studying
the possible validity of Bacon’s claim in a
In 1620 Sir Francis Bacon wrote: “If you
variety of experimental contexts (although,
read a piece of text through twenty times,
truth be told, we found the quotation after
you will not learn it by heart so easily as if
the studies were well under way). In our exyou read it ten times while attempting to reperiments, students learn materials (either
cite from time to time and consulting the text
simple sets of words or more complex textwhen your memory fails.” How did he know
book passages—the material does not matthat? The answer is that he did not know, for
HENRY L. ROEDIGER
ter) by various combinations of studying and
sure, but based his judgment on his own
testing the material. The general finding is
personal experience. The case is interesting because Bathat retrieval (or reciting, as Bacon called it) during a test
con was one of the originators of the scientific method and
provides a great boost to later retention, much more so
laid out the framework for experimental science.
than repeated studying (Roediger & Karpicke, 2006).
Science in Bacon’s time was applied to the natural
Let’s consider just one experiment here to make the
world, what today would be called the physical
point. Zaromb and Roediger (2011) gave students lists
sciences (chiefly, physics and chemistry). The idea that
of words to remember in preparation for a test that would
scientific methods could be applied to people was not
be given two days later. Students in one condition studeven dreamt of and, had the notion been raised, it
ied the material eight times with short breaks, but students
would have been hooted down. Human beings were
in two other conditions received either two or four tests in
not dross stuff; they had souls, they had free will—surely
place of some of the study trials. If S denotes a study trial
they could not be studied scientifically! It took another
and T denotes a test (or recitation), the three conditions
250 years before pioneers would question this assumpcan be labeled SSSSSSSS, STSSSTSS, or STSTSTSTST.
tion and take the brave step to create a science of psyIf studying determines later recall, then the three condichology, the study of the mind. The date usually given is
tions just listed should be ordered in terms of decreasing
1879, when Wilhelm Wundt founded the first psycholeffectiveness (from eight to six to four study trials). Howogy laboratory in Leipzig, Germany.
ever, if Bacon is right, the conditions should be ordered
Edwin G. Boring, the great historian of psychology,
in increasing effectiveness for later retention (from zero to
wrote that the “application of the experimental method to
two to four test trials). The result: the proportion recalled
the problem of mind is the great outstanding event in the
two days later was .17, .25 and .39 for the three constudy of the mind, an event to which no other is compaditions in the order listed above.
rable” (1929, p. 659). Boring is right, and the textbook
Sir Francis Bacon was right: Reciting is more effecyou hold relates the fascinating story of cognitive psytive than studying (although of course some studying is
chology, today’s experimental study of mind.
required). To my knowledge, no one has done the actual
But what about Bacon’s assertion? Does reciting
experiment he suggested (20 trials), but it would make a
material really help one learn it more than studying it?
fine class project with 20 study trials for one condition or
This idea seems odd, because in education we think of
10 study and 10 test trials for the other. By the way, selfstudying as being how we learn; and of testing as only
testing on material is a good way to study for your
measuring what has been learned.
courses (Roediger, McDermott & McDaniel, 2011).
Experiments on Human Behavior
In controlled experimental designs, an experimenter will usually conduct research in a
laboratory setting. The experimenter controls as many aspects of the experimental situation as possible. There are basically two kinds of variables in any given experiment.
Independent variables are aspects of an investigation that are individually
Research Methods in Cognitive Psychology
25
manipulated, or carefully regulated, by the experimenter, while other aspects of the investigation are held constant (i.e., not subject to variation). Dependent variables are
outcome responses, the values of which depend on how one or more independent
variables influence or affect the participants in the experiment. When you tell some
student research participants that they will do very well on a task, but you do not say
anything to other participants, the independent variable is the amount of information
that the students are given about their expected task performance. The dependent
variable is how well both groups actually perform the task—that is, their score on
the math test.
When the experimenter manipulates the independent variables, he or she
controls for the effects of irrelevant variables and observes the effects on the dependent variables (outcomes). These irrelevant variables that are held constant are
called control variables. For example, when you conduct an experiment on people’s
ability to concentrate when subjected to different kinds of background music, you
should make sure that the lighting in the room is always the same, and not sometimes extremely bright and other times dim. The variable of light needs to be held
constant.
Another type of variable is the confounding variable. Confounding variables are a
type of irrelevant variable that has been left uncontrolled in a study. For example,
imagine you want to examine the effectiveness of two problem-solving techniques.
You train and test one group under the first strategy at 6 A.M. and a second group
under the second strategy at 6 P.M. In this experiment, time of day would be a confounding variable. In other words, time of day may be causing differences in performance that have nothing to do with the problem-solving strategy. Obviously, when
conducting research, we must be careful to avoid the influence of confounding
variables.
In implementing the experimental method, experimenters must use a representative and random sample of the population of interest. They must exert rigorous
control over the experimental conditions so that they know that the observed effects
can be attributed to variations in the independent variable and nothing else. For
example, in the above mentioned experiment, people’s ability to concentrate did
not depend on the general lighting conditions in the room, per se, because during
a few sessions, the sun shone directly into the eyes of the subjects so that they had
trouble seeing.
The experimenter also must randomly assign participants to the treatment and
control conditions. For example, you would not want to end up in an experiment on
concentration with lots of people with ADD—Attention Deficit Disorder—in your
experimental group, but no such people in your control group. If those requisites for
the experimental method are fulfilled, the experimenter may be able to infer probable causality. This inference is of the effects of the independent variable or variables
(the treatment) on the dependent variable (the outcome) for the given population.
Many different dependent variables are used in cognitive-psychological research.
Two common variables are percent correct (or its additive inverse, error rate) and
reaction time. These measures are popular because they can tell the investigator, respectively, the accuracy and speed of mental processing. Independent and dependent
variables must be chosen with great care, because no matter what processes one is
observing, what is learned from an experiment will depend almost exclusively on
the variables one chooses to isolate from the often complex behavior one is
observing.
26
CHAPTER 1 • Introduction to Cognitive Psychology
Table 1.2
Research Methods
Cognitive psychologists use controlled experiments, psychobiological research, self-reports, case studies, naturalistic
observation, and computer simulations and artificial intelligence when studying cognitive phenomena.
Method
Controlled Laboratory
Experiments
Psychobiological Research
Self-Reports, such as
Verbal Protocols,
Self-Rating, Diaries
Description of method
Obtain samples of performance at
a particular time and place
Study animal brains and human
brains, using postmortem studies
and various psychobiological
measures or imaging techniques
(see Chapter 2)
Obtain participants’ reports
of own cognition in
progress or as recollected
Random assignment of
subjects
Usually
Not usually
Not applicable
Experimental control of
independent variables
Usually
Varies widely, depending on the
particular technique
Probably not
Sample size
May be any size
Often small
Probably small
Sample representativeness
May be representative
Often not representative
May be representative
Ecological validity
Not unlikely; depends on the
task and the context to which it is
being applied
Unlikely under some circumstances
Maybe; see strengths
and weaknesses
Information about
individual differences
Usually de-emphasized
Yes
Yes
Strengths
• Easy to administer, score, and do
statistical analyses
• “Hard” evidence of cognitive
functions through physiological
activity
• Access to introspective
insights from participants’
point of view
• High probability of drawing valid
causal inferences
• Alternative view of cognitive processes
• Possibility to develop treatments
for cognitive deficits
Weaknesses
• Difficulty in generalizing results
beyond a specific place, time,
and task setting
• Discrepancies between behavior
in real life and in the laboratory
Examples
Karpicke (2009) developed a
laboratory task in which participants
had to learn and recall
Swahili-English word pairs. After
subjects first recalled the meaning
of a word, that pair was either
dropped, presented twice more in a
study period, or presented twice
more in test periods. Subjects took
a final recall test one week later.
• Limited accessibility for most
researchers (need appropriate
subjects and expensive equipment)
• Inability to report on
processes occurring
outside conscious
awareness
• Small samples
• Decreased generalizability when
abnormal brains or animal brains
are investigated
• Verbal protocols &
self-ratings: May
influence cognitive
process being reported
• Recollections:
Discrepancies between
actual cognition and
recollected cognitive
processes and products
New and colleagues (New et al.,
2009) have found that Borderline
patients with Intermittent Explosive
Disorder responded more aggressively to a provocation than did
normal control subjects. The patients
particularly showed an increase in
glucose consumption in brain areas
associated with emotion like the
amygdala and less activity in
dorsal brain regions that serve to
control aggression.
In a study about the relation
between cortisol levels
(which are stress-dependent)
and sleep, self-rated health,
and stress, participants kept
diaries and collected saliva
samples over four weeks
(Dahlgren et al., 2009).
Research Methods in Cognitive Psychology
Computer Simulations and
Artificial Intelligence
Case Studies
Naturalistic Observations
Engage in intensive study of single
individuals, drawing general
conclusions about behavior
Observe real-life situations, as in
classrooms, work settings, or homes
Simulations: Attempt to make
computers simulate human cognitive
performance on various tasks
AI: Attempt to make computers
demonstrate intelligent cognitive
performance, regardless of whether
the process resembles human
cognitive processing
Highly unlikely
Not applicable
Not applicable
Highly unlikely
No
Full control of variables of interest
Almost certain to be small
Probably small
Not applicable
Not likely to be representative
May be representative
Not applicable
High ecological validity for individual
cases; lower generalizability to others
Yes
Not applicable
Yes; richly detailed information regarding individuals
Possible, but emphasis is on
environmental distinctions, not on
individual differences
Not applicable
• Access to detailed information about
individuals, including historical and
current contexts
• Access to rich contextual information
• Exploration of possibilities for
modeling cognitive processes
• Allows clear hypothesis testing
• May lead to specialized applications for special groups (e.g., prodigies, persons with brain damage)
• Wide range of practical applications
(e.g., robotics for performing
dangerous tasks)
• Applicability to other persons
• Limited generalizability due to small
sample size and nonrepresentativeness of sample
• Lack of experimental control
• Possible influence on behavior due
to presence of observer
A case study with a breast cancer
patient showed that a new technique
(problem-solving therapy) can reduce
anxiety and depression in cancer
patients (Carvalho & Hopko, 2009).
A study using questionnaires and
observation found that Mexicans on
average consider themselves less
sociable than U.S. Americans consider
themselves; however, Mexicans
behave much more sociably than
U.S. Americans in their everyday lives
(Ramirez-Esparza et al., 2009).
• Limitations imposed by the hardware
(i.e., the computer circuitry) and the
software (i.e., the programs written
by the researchers)
• Simulations may imperfectly model
the way that the human brain thinks
Simulations: Through detailed
computations, David Marr (1982)
attempted to simulate human visual
perception and proposed a theory of
visual perception based on his
computer models.
AI: Various AI programs have been
written that can demonstrate expertise
(e.g., playing chess), but they probably
do so via different processes than those
used by human experts.
27
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CHAPTER 1 • Introduction to Cognitive Psychology
Psychologists who study cognitive processes with reaction time often use the
subtraction method, which involves estimating the time a cognitive process takes by
subtracting the amount of time information processing takes with the process from
the time it takes without the process (Donders, 1868/1869). If you are asked to scan
the words dog, cat, mouse, hamster, chipmunk and to say whether the word chipmunk
appears in it, and then are asked to scan dog, cat, mouse, hamster, chipmunk, lion and
to say whether lion appears, the difference in the reaction times might be taken, by
some models of mental processing, roughly to indicate the amount of time it takes to
process each stimulus.
Suppose the outcomes in the treatment condition show a statistically significant
difference from the outcomes in the control condition. The experimenter then can
infer the likelihood of a causal link between the independent variable(s) and the
dependent variable. Because the researcher can establish a likely causal link between
the given independent variables and the dependent variables, controlled laboratory
experiments offer an excellent means of testing hypotheses.
Suppose that we wanted to see whether loud, distracting noises influence the
ability to perform well on a particular cognitive task (e.g., reading a passage from a
textbook and responding to comprehension questions). Ideally, we first would select
a random sample of participants from within our total population of interest. We
then would randomly assign each participant to a treatment condition or a control
condition. Then we would introduce some distracting loud noises to the participants
in our treatment condition. The participants in our control condition would not receive this treatment. We would present the cognitive task to participants in both
the treatment condition and the control condition and then measure their performance by some means (e.g., speed and accuracy of responses to comprehension questions). Finally, we would analyze our results statistically. We thereby would examine
whether the difference between the two groups reached statistical significance.
Suppose the participants in the treatment condition showed poorer performance
at a statistically significant level than the participants in the control condition. We
might infer that loud, distracting noises influenced the ability to perform well on this
particular cognitive task.
In cognitive-psychological research, though the dependent variables may be quite
diverse, they often involve various outcome measures of accuracy (e.g., frequency of
errors), of response times, or of both. Among the myriad possibilities for independent
variables are characteristics of the situation, of the task, or of the participants.
For example, characteristics of the situation may involve the presence versus the
absence of particular stimuli or hints during a problem-solving task. Characteristics of
the task may involve reading versus listening to a series of words and then responding
to comprehension questions. Characteristics of the participants may include age differences, differences in educational status, or differences based on test scores.
On the one hand, characteristics of the situation or task may be manipulated
through random assignment of participants to either the treatment or the control
group. On the other hand, characteristics of the participant are not easily manipulated experimentally. For example, suppose the experimenter wants to study the
effects of aging on speed and accuracy of problem solving. The researcher cannot
randomly assign participants to various age groups because people’s ages cannot be
manipulated (although participants of various age groups can be assigned at random
to various experimental conditions). In such situations, researchers often use other
kinds of studies, for example, studies involving correlation (a statistical relationship
Research Methods in Cognitive Psychology
29
James Stevenson/www.Cartoonbank.com
between two or more attributes, such as characteristics of the participants or of a
situation). Correlations are usually expressed through a correlation coefficient
known as Pearson’s r. Pearson’s r is a number that can range from –1.00 (a negative
correlation) to 0 (no correlation) to 1.00 (a positive correlation).
A correlation is a description of a relationship. The correlation coefficient describes the strength of the relationship. The closer the coefficient is to 1 (either
positive or negative), the stronger the relationship between the variables is. The
sign (positive or negative) of the coefficient describes the direction of the relationship. A positive relationship indicates that as one variable increases (e.g., vocabulary size), another variable also increases (e.g., reading comprehension). A
negative relationship indicates that as the measure of one variable increases (e.g.,
fatigue), the measure of another decreases (e.g., alertness). No correlation—that is,
when the coefficient is 0—indicates that there is no pattern or relationship in the
change of two variables (e.g., intelligence and earlobe length). In this final case,
both variables may change, but the variables do not vary together in a consistent
pattern.
Correlational studies are often the method of choice when researchers do not
want to deceive their subjects by using manipulations in an experiment or when
they are interested in factors that cannot be manipulated ethically (e.g., lesions in
specific parts of the human brain). However, because researchers do not have any
control over the experimental conditions, causality cannot be inferred from correlational studies.
Findings of statistical relationships are highly informative. Their value should
not be underrated. Also, because correlational studies do not require the random
assignment of participants to treatment and control conditions, these methods may
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CHAPTER 1 • Introduction to Cognitive Psychology
be applied flexibly. However, correlational studies generally do not permit unequivocal inferences regarding causality. As a result, many cognitive psychologists strongly
prefer experimental data to correlational data.
Psychobiological Research
Through psychobiological research, investigators study the relationship between cognitive performance and cerebral events and structures. Chapter 2 describes various specific techniques used in psychobiological research. These techniques generally fall
into three categories:
• techniques for studying an individual’s brain postmortem (after the death of an
individual), relating the individual’s cognitive function prior to death to observable features of the brain;
• techniques for studying images showing structures of or activities in the brain of
an individual who is known to have a particular cognitive deficit;
• techniques for obtaining information about cerebral processes during the normal
performance of a cognitive activity.
Postmortem studies offered some of the first insights into how specific lesions
(areas of injury in the brain) may be associated with particular cognitive deficits.
Such studies continue to provide useful insights into how the brain influences cognitive function. Recent technological developments also increasingly enable researchers to study individuals with known cognitive deficits in vivo (while the individual is
alive). The study of individuals with abnormal cognitive functions linked to cerebral
damage often enhances our understanding of normal cognitive functions.
Psychobiological researchers also study normal cognitive functioning by studying
cerebral activity in animal participants. Researchers often use animals for experiments involving neurosurgical procedures that cannot be performed on humans
because such procedures would be difficult, unethical, or impractical. For example,
studies mapping neural activity in the cortex have been conducted on cats and
monkeys (e.g., psychobiological research on how the brain responds to visual stimuli;
see Chapter 3).
Can cognitive and cerebral functioning of animals and of abnormal humans be
generalized to apply to the cognitive and cerebral functioning of normal humans?
Psychobiologists have responded to these questions in various ways. For some kinds
of cognitive activity, the available technology permits researchers to study the
dynamic cerebral activity of normal human participants during cognitive processing
(see the brain-imaging techniques described in Chapter 2).
Self-Reports, Case Studies, and Naturalistic Observation
Individual experiments and psychobiological studies often focus on precise specification of discrete aspects of cognition across individuals. To obtain richly textured
information about how particular individuals think in a broad range of contexts,
researchers may use other methods. These methods include:
• self-reports (an individual’s own account of cognitive processes);
• case studies (in-depth studies of individuals); and
• naturalistic observation (detailed studies of cognitive performance in everyday
situations and nonlaboratory contexts).
Research Methods in Cognitive Psychology
31
BSIP / Photo Researchers, Inc.
Experimental research is most useful for testing hypotheses; however, research
based on self-reports, case studies, and naturalistic observation is often particularly useful for the formulation of hypotheses. These methods are also useful to
generate descriptions of rare events or processes that we have no other way to
measure.
In very specific circumstances, these methods may provide the only way to
gather information. An example is the case of Genie, a girl who was locked in a
room until the age of 13 and thus provided with severely limited social and sensory
experiences. As a result of her imprisonment, Genie had severe physical impairments
and no language skills. Through case-study methods, information was collected
about how she later began to learn language (Fromkin et al., 1974; Jones, 1995; LaPointe, 2005). It would have been unethical experimentally to deny a person any
language experience for the first 13 years of life. Therefore, case-study methods are
the only reasonable way to examine the results of someone being denied language
and social exposure.
Similarly, traumatic brain injury cannot be manipulated in humans in the
laboratory. Therefore, when traumatic brain injury occurs, case studies are the
only way to gather information. For example, consider the case of Phineas
Gage, a railroad worker who, in 1848, had a large metal spike driven through
his frontal lobes in a freak accident (Torregrossa, Quinn, & Taylor, 2008; see
also Figure 1.4). Surprisingly, Mr. Gage survived. His behavior and mental processes were drastically changed by the accident, however. Obviously, we cannot
insert large metal rods into the brains of experimental participants. Therefore, in
the case of traumatic brain injury, we must rely on case-study methods to gather
information.
The reliability of data based on self-reports depends on the candor of the
participants. A participant may misreport information about his or her cognitive
processes for a variety of reasons. These reasons can be intentional or unintentional.
Intentional misreports can include trying to edit out unflattering information.
Figure 1.4 When an explosion forced an iron rod through his head, Phineas Gage sustained frontal lobe damage. Gage was the subject of case studies both during his life and
after his death.
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CHAPTER 1 • Introduction to Cognitive Psychology
Unintentional misreports may involve not understanding the question or not remembering the information accurately. For example, when a participant is asked about the
problem-solving strategies he or she used in high school, the participant may not
remember. The participant may try to be completely truthful in his or her reports. But
reports involving recollected information (e.g., diaries, retrospective accounts, questionnaires, and surveys) are notably less reliable than reports provided during the cognitive processing under investigation. The reason is that participants sometimes forget
what they did.
In studying complex cognitive processes, such as problem solving or decision
making, researchers often use a verbal protocol. In a verbal protocol, the participants describe aloud all their thoughts and ideas during the performance of a given
cognitive task (e.g., “I like the apartment with the swimming pool better, but I
can’t really afford it, so I might have to choose the one without the swimming
pool.”).
An alternative to a verbal protocol is for participants to report specific information regarding a particular aspect of their cognitive processing. For example,
consider a study of insightful problem solving (see Chapter 11). Participants were
asked at 15-second intervals to report numerical ratings indicating how close they
felt they were to reaching a solution to a given problem. Unfortunately, even these
methods of self-reporting have their limitations. What kind of limitations? Cognitive processes may be altered by the act of giving the report (e.g., processes involving brief forms of memory; see Chapter 5). Or, cognitive processes may occur
outside of conscious awareness (e.g., processes that do not require conscious attention or that take place so rapidly that we fail to notice them; see Chapter 4). To
get an idea of some of the difficulties with self-reports, carry out the following Investigating Cognitive Psychology: Self-Reports tasks. Reflect on your experiences with
self-reports.
Case studies (e.g., an in-depth study of individuals who are exceptionally gifted)
and naturalistic observations (such as detailed observations of the performance of
employees operating in nuclear power plants) may be used to complement findings
from laboratory experiments. These two methods of cognitive research offer high
ecological validity, the degree to which particular findings in one environmental
INVESTIGATING COGNITIVE PSYCHOLOGY
Self-Reports
1.
Without looking at your shoes, try reporting aloud the various steps involved in tying
your shoe.
2.
Recall aloud what you did on your last birthday.
3.
Now, actually tie your shoe (or something else, such as a string tied around a table
leg), reporting aloud the steps you take. Do you notice any differences between
task 1 and task 3?
4.
Report aloud how you pulled into consciousness the steps involved in tying your
shoe or your memories of your last birthday. Can you report exactly how you pulled
the information into conscious awareness? Can you report which part of your brain
was most active during each of these tasks?
Research Methods in Cognitive Psychology
33
context may be considered relevant outside of that context. As you probably know,
ecology is the study of the interactive relationship between an organism (or organisms) and its environment. Many cognitive psychologists seek to understand the interactive relationship between human thought processes and the environments in
which humans are thinking. Sometimes, cognitive processes that are commonly
observed in one setting (e.g., in a laboratory) are not identical to those observed in
another setting (e.g., in an air-traffic control tower or a classroom).
Computer Simulations and Artificial Intelligence
Digital computers played a fundamental role in the emergence of the study of
cognitive psychology. One kind of influence is indirect—through models of human
cognition based on models of how computers process information. Another kind is
direct—through computer simulations and artificial intelligence.
In computer simulations, researchers program computers to imitate a given human
function or process. Examples are performance on particular cognitive tasks (e.g., manipulating objects within three-dimensional space) and performance of particular
cognitive processes (e.g., pattern recognition). Some researchers have attempted to
create computer models of the entire cognitive architecture of the human mind. Their
models have stimulated heated discussions regarding how the human mind may function as a whole (see Chapter 8). Sometimes the distinction between simulation and
artificial intelligence is blurred. For example, certain programs are designed to simulate
human performance and to maximize functioning simultaneously.
Consider a computer program that plays chess. There are two entirely different
ways to conceptualize how to write such a program. One is known as brute force:
A researcher constructs an algorithm that considers extremely large numbers of
moves in a very short time, potentially beating human players simply by virtue of
the number of moves it considers and the future potential consequences of these
moves. The program would be viewed as successful to the extent that it beat the
best humans. This kind of artificial intelligence does not seek to represent how
humans function, but done well, it can produce a program that plays chess at the
highest possible level.
An alternative approach, simulation, looks at how chess grand masters solve
chess problems and then seeks to function the way they do. The program would be
successful if it chose, in a sequence of moves in a game, the same moves that the
grand master would choose. It is also possible to combine the two approaches, producing a program that generally simulates human performance but can use brute
force as necessary to win games.
Putting It All Together
Cognitive psychologists often broaden and deepen their understanding of cognition
through research in cognitive science. Cognitive science is a cross-disciplinary field
that uses ideas and methods from cognitive psychology, psychobiology, artificial intelligence, philosophy, linguistics, and anthropology (Nickerson, 2005; Von Eckardt,
2005). Cognitive scientists use these ideas and methods to focus on the study of how
humans acquire and use knowledge.
Cognitive psychologists also profit from collaborations with other kinds of psychologists. Examples are social psychologists (e.g., in the cross-disciplinary field of
social cognition), psychologists who study motivation and emotion, and engineering
psychologists (i.e., psychologists who study human-machine interactions), but also
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CHAPTER 1 • Introduction to Cognitive Psychology
clinical psychologists who are interested in psychological disorders. There is also
close exchange and collaboration with a number of other related fields. Psychiatrists
are interested in how the brain works and how it influences our thinking, feeling,
and reasoning. Anthropologists in turn may explore how reasoning and perception
processes differ from one culture to the next. Computer specialists try to develop
computer interfaces that are highly efficient, given the way humans perceive and
process information. Traffic planners can use information from cognitive psychology
to plan and construct traffic situations that result in a maximal overview for traffic
participants and therefore, hopefully, fewer accidents.
CONCEPT CHECK
1. What is the meaning of “statistical significance”?
2. How do independent and dependent variables differ?
3. Why is the experimental method uniquely suited to drawing causal inferences?
4. What are some of the advantages and disadvantages of the case-study method?
5. How does a theory differ from a hypothesis?
Fundamental Ideas in Cognitive Psychology
Certain fundamental ideas keep emerging in cognitive psychology, regardless of the
particular phenomenon one studies. Here are what might be considered five fundamental ideas. These ideas crosscut some of the Key Themes listed at the end of this
chapter.
1. Empirical data and theories are both important—data in cognitive psychology can be
fully understood only in the context of an explanatory theory, and theories are empty
without empirical data.
Theories give meaning to data. Suppose that we know that people’s ability to
recognize information that they have seen is better than their ability to recall
such information. As an example, they are better at recognizing whether they
heard a word said on a list than they are at recalling the word without the
word being given. This is an interesting empirical generalization, but it does
not, in the absence of an underlying theory, provide explanation. Another
important goal of science is also prediction. Theory can suggest under which
circumstances limitations to the generalization should occur. Theory thus assists
both in explanation and in prediction.
At the same time, theory without data is empty. Almost anyone can sit in an
armchair and propose a theory—even a plausible-sounding one. Science, however, requires empirical testing of such theories. Thus, theories and data depend
on each other. Theories generate data collections, which help correct theories,
which then lead to further data collections, and so forth.
2. Cognition is generally adaptive, but not in all specific instances.
We can perceive, learn, remember, reason, and solve problems with great accuracy. And we do so even though we are constantly distracted by a plethora of
stimuli. The same processes, however, that lead us to perceive, remember, and
Fundamental Ideas in Cognitive Psychology
35
reason accurately in most situations also can lead us astray. Our memories and
reasoning processes, for example, are susceptible to certain well-identified, systematic errors. For example, we tend to overvalue information that is easily
available to us. While this tendency generally helps us to make cognitive processes more efficient, we do this even when this information is not optimally
relevant to the problem at hand.
3. Cognitive processes interact with each other and with noncognitive processes.
Although cognitive psychologists try to study and often to isolate the functioning of specific cognitive processes, they know that these processes work together.
For example, memory processes depend on perceptual processes. What you
remember depends in part on what you perceive. But noncognitive processes
also interact with cognitive ones. For example, you learn better when you are
motivated to learn. Cognitive psychologists therefore seek to study cognitive
processes not only in isolation but also in their interactions with each other
and with noncognitive processes.
One of the most exciting areas of cognitive psychology today is at the interface between cognitive and biological levels of analysis. In recent years, it has
become possible to localize activity in the brain associated with various kinds
of cognitive processes. However, one has to be careful about assuming that the
biological activity is causal of the cognitive activity. Research shows that learning that causes changes in the brain—in other words, cognitive processes—can
affect biological structures just as biological structures can affect cognitive processes. The cognitive system does not operate in isolation. It works in interaction with other systems.
4. Cognition needs to be studied through a variety of scientific methods.
There is no one right way to study cognition. All cognitive processes need to be
studied through a variety of methods. The more different kinds of techniques
that lead to the same conclusion, the higher the confidence one can have in
that conclusion. For example, suppose studies of reaction times, error rates, and
patterns of individual differences all lead to the same conclusion. Then one can
have much more confidence in the conclusion than if only one method led to
that conclusion.
All these methods, however, must be scientific. They enable us to disconfirm our
expectations when those expectations are wrong. Nonscientific methods do not
have this feature. For example, methods of inquiry that simply rely on faith or authority to determine truth may have value in our lives, but they are not scientific.
5. All basic research in cognitive psychology may lead to applications, and all applied
research may lead to basic understandings.
But the truth is, the distinction between basic and applied research often is not
clear at all. Research that seems like it will be basic often leads to immediate applications. Similarly, research that seems like it will be applied sometimes leads
quickly to basic understandings. For example, a basic finding from research on
memory is that learning is superior when it is spaced out over time rather than
crammed into a short time interval. This basic finding has an immediate application to study strategies. At the same time, research on eyewitness testimony,
which seems on its face to be very applied, has enhanced our basic understanding
of memory systems and of the extent to which humans construct their own
memories.
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CHAPTER 1 • Introduction to Cognitive Psychology
In this book, we emphasize the underlying common ideas and organizing themes
across cognitive psychology, rather than simply to state the facts. We follow this
path to help you perceive large, meaningful patterns within the domain of cognitive
psychology. We also try to give you some idea of how cognitive psychologists think
and how they structure their field in their day-to-day work. We hope that this approach will help you to contemplate problems in cognitive psychology at a deeper
level than might otherwise be possible. Ultimately, the goal of cognitive psychologists is to understand not only how people may think in their laboratories but also
how they think in their everyday lives.
Key Themes in Cognitive Psychology
If we review the important ideas in this chapter, we discover some of the major
themes that underlie cognitive psychology, such as nature vs. nurture and rationalism vs. empiricism. These, and the other key themes listed here, address the core of
the nature of the human mind. These themes appear again and again in the study of
cognitive psychology.
As you read each chapter, think of the topics in terms of how they relate to the
major themes in cognitive psychology. You will be encountering these themes
throughout this text and can review them in each chapter’s Key Themes section.
Note that these questions can be posed in the “either/or” form of thesis/antithesis or in the “both/and” form of a synthesis of views or methods. The synthesis
view often proves more useful than one extreme position or another. For example,
our nature may provide an inherited framework for our distinctive characteristics
and patterns of thinking and acting. But our nurture may shape the specific ways
in which we flesh out that framework.
We may use empirical methods for gathering data and for testing hypotheses.
But we may use rationalist methods for interpreting data, constructing theories, and
formulating hypotheses based on theories. Our understanding of cognition deepens
when we consider both basic research into fundamental cognitive processes and applied research regarding effective uses of cognition in real-world settings. Syntheses
are constantly evolving. What today may be viewed as a synthesis may be viewed
tomorrow as an extreme position or vice versa.
Remember, each of the topics in this text (perception, memory, and so on) can
be examined using these seven major themes in cognitive psychology:
1. Nature versus nurture
Thesis/Antithesis: Which is more influential in human cognition—nature or
nurture? If we believe that innate characteristics of human cognition are more
important, we might focus our research on studying innate characteristics of cognition. If we believe that the environment plays an important role in cognition,
we might conduct research exploring how distinctive characteristics of the environment seem to influence cognition.
Synthesis: We can explore how covariations and interactions in the environment
(e.g., an impoverished environment) adversely affect someone whose genes otherwise might have led to success in a variety of tasks.
2. Rationalism versus empiricism
Thesis/Antithesis: How should we discover the truth about ourselves and about
the world around us? Should we do so by trying to reason logically, based on
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Key Themes in Cognitive Psychology
Nature vs. nurture: Both our genes and our environment may influence what we are, how we behave,
and how we think.
what we already know? Or should we do so by observing and testing our
observations of what we can perceive through our senses?
Synthesis: We can combine theory with empirical methods to learn the most we
can about cognitive phenomena.
3. Structures versus processes
Thesis/Antithesis: Should we study the structures (contents, attributes, and products) of the human mind? Or should we focus on the processes of human
thinking?
Synthesis: We can explore how mental processes operate on mental structures.
4. Domain generality versus domain specificity
Thesis/Antithesis: Are the processes we observe limited to single domains, or are
they general across a variety of domains? Do observations in one domain apply
also to all domains, or do they apply only to the specific domains observed?
Synthesis: We can explore which processes might be domain-general and which
might be domain-specific.
5. Validity of causal inferences versus ecological validity
Thesis/Antithesis: Should we study cognition by using highly controlled experiments that increase the probability of valid inferences regarding causality? Or
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CHAPTER 1 • Introduction to Cognitive Psychology
should we use more naturalistic techniques, which increase the likelihood of
obtaining ecologically valid findings but possibly at the expense of experimental
control?
Synthesis: We can combine a variety of methods, including laboratory methods
and more naturalistic ones, so as to converge on findings that hold up, regardless
of the method of study.
6. Applied versus basic research
Thesis/Antithesis: Should we conduct research into fundamental cognitive processes? Or should we study ways in which to help people use cognition effectively in practical situations?
Synthesis: We can combine the two kinds of research dialectically so that basic
research leads to applied research, which leads to further basic research, and
so on.
7. Biological versus behavioral methods
Thesis/Antithesis: Should we study the brain and its functioning directly, perhaps
even scanning the brain while people are performing cognitive tasks? Or should
we study people’s behavior in cognitive tasks, looking at measures such as percent correct and reaction time?
Synthesis: We can try to synthesize biological and behavioral methods so that we
understand cognitive phenomena at multiple levels of analysis.
Summary
1. What is cognitive psychology? Cognitive psychology is the study of how people perceive,
learn, remember, and think about information.
2. How did psychology develop as a science? Beginning with Plato and Aristotle, people have
contemplated how to gain understanding of the
truth. Plato held that rationalism offers the clear
path to truth, whereas Aristotle espoused empiricism as the route to knowledge. Centuries later,
Descartes extended Plato’s rationalism, whereas
Locke elaborated on Aristotle’s empiricism.
Kant offered a synthesis of these apparent opposites. Decades after Kant proposed his synthesis,
Hegel observed how the history of ideas seems to
progress through a dialectical process.
3. How did cognitive psychology develop from
psychology? By the twentieth century, psychology had emerged as a distinct field of study.
Wundt focused on the structures of the mind
(leading to structuralism), whereas James and Dewey focused on the processes of the mind
(functionalism).
Emerging from this dialectic was associationism, espoused by Ebbinghaus and Thorndike. It
paved the way for behaviorism by underscoring
the importance of mental associations. Another
step toward behaviorism was Pavlov’s discovery
of the principles of classical conditioning.
Watson, and later Skinner, were the chief proponents of behaviorism. It focused entirely on
observable links between an organism’s behavior and particular environmental contingencies
that strengthen or weaken the likelihood that
particular behaviors will be repeated. Most
behaviorists dismissed entirely the notion that
there is merit in psychologists trying to understand what is going on in the mind of the individual engaging in the behavior.
However, Tolman and subsequent behaviorist researchers noted the role of cognitive processes in influencing behavior. A convergence
of developments across many fields led to the
emergence of cognitive psychology as a discrete
discipline, spearheaded by such notables as
Neisser.
4. How have other disciplines contributed to the
development of theory and research in cognitive psychology? Cognitive psychology has
Thinking about Thinking
roots in philosophy and physiology. They merged
to form the mainstream of psychology. As a discrete field of psychological study, cognitive psychology also profited from cross-disciplinary
investigations.
Relevant fields include linguistics (e.g., How
do language and thought interact?), biological
psychology (e.g., What are the physiological
bases for cognition?), anthropology (e.g.,
What is the importance of the cultural context
for cognition?), and technological advances like
artificial intelligence (e.g., How do computers
process information?).
5. What methods do cognitive psychologists use to
study how people think? Cognitive psychologists
use a broad range of methods, including experiments, psychobiological techniques, self-reports,
case studies, naturalistic observation, and computer simulations and artificial intelligence.
6. What are the current issues and various fields
of study within cognitive psychology? Some of
the major issues in the field have centered on
how to pursue knowledge. Psychological work
can be done:
• by using both rationalism (which is the basis
for theory development) and empiricism
(which is the basis for gathering data);
39
• by underscoring the importance of cognitive
structures and of cognitive processes;
• by emphasizing the study of domain-general
and of domain-specific processing;
• by striving for a high degree of experimental
control (which better permits causal inferences) and for a high degree of ecological
validity (which better allows generalization
of findings to settings outside of the
laboratory);
• by conducting basic research seeking fundamental insights about cognition and applied
research seeking effective uses of cognition
in real-world settings.
Although positions on these issues may appear to
be diametrical opposites, often apparently antithetical views may be synthesized into a form that offers
the best of each of the opposing viewpoints.
Cognitive psychologists study biological bases
of cognition as well as attention, consciousness,
perception, memory, mental imagery, language,
problem solving, creativity, decision making,
reasoning, developmental changes in cognition
across the life span, human intelligence, artificial intelligence, and various other aspects of
human thinking.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe the major historical schools of psychological thought leading up to the development of cognitive psychology.
2. Describe some of the ways in which philosophy,
linguistics, and artificial intelligence have contributed to the development of cognitive
psychology.
3. Compare and contrast the influences of Plato
and Aristotle on psychology.
4. Analyze how various research methods in cognitive psychology reflect empiricist and rationalist approaches to gaining knowledge.
5. Design a rough sketch of a cognitivepsychological investigation involving one of the
research methods described in this chapter.
Highlight both the advantages and the disadvantages of using this particular method for your
investigation.
6. This chapter describes cognitive psychology as
the field is at present. How might you speculate
that the field will change in the next 50 years?
7. How might an insight gained from basic
research lead to practical uses in an everyday
setting?
8. How might an insight gained from applied
research lead to a deepened understanding of
the fundamental features of cognition?
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CHAPTER 1 • Introduction to Cognitive Psychology
Key Terms
artificial intelligence (AI), p. 14
associationism, p. 9
behaviorism, p. 11
cognitive psychology, p. 3
cognitive science, p. 33
cognitivism, p. 13
dependent variables, p. 25
ecological validity, p. 32
empiricist, p. 6
functionalism, p. 8
Gestalt psychology, p. 13
hypotheses, p. 23
independent variables, p. 24
intelligence, p. 17
introspection, p. 8
pragmatists, p. 9
rationalist, p. 6
statistical significance, p. 23
structuralism, p. 7
theory, p. 23
theory of multiple intelligences,
p. 19
three-stratum model of
intelligence, p. 19
triarchic theory of human
intelligence, p. 20
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines and, more.
2
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Cognitive Neuroscience
CHAPTER OUTLINE
Cognition in the Brain: The Anatomy
and Mechanisms of the Brain
Gross Anatomy of the Brain: Forebrain, Midbrain,
Hindbrain
The Forebrain
The Midbrain
The Hindbrain
Cerebral Cortex and Localization of Function
Hemispheric Specialization
Lobes of the Cerebral Hemispheres
Neuronal Structure and Function
Receptors and Drugs
Viewing the Structures and Functions
of the Brain
Postmortem Studies
Studying Live Nonhuman Animals
Studying Live Humans
Electrical Recordings
Static Imaging Techniques
Metabolic Imaging
Brain Disorders
Stroke
Brain Tumors
Head Injuries
Intelligence and Neuroscience
Intelligence and Brain Size
Intelligence and Neurons
Intelligence and Brain Metabolism
Biological Bases of Intelligence Testing
The P-FIT Theory of Intelligence
Key Themes
Summary
Thinking about Thinking: Analytical,
Creative, and Practical Questions
Key Terms
Media Resources
41
42
CHAPTER 2 • Cognitive Neuroscience
Here are some of the questions we will explore in this chapter:
1. What are the fundamental structures and processes within the brain?
2. How do researchers study the major structures and processes of the brain?
3. What have researchers found as a result of studying the brain?
n BELIEVE IT OR NOT
DOES YOUR BRAIN USE LESS POWER THAN YOUR DESK LAMP?
The brain is one of the premier users of energy in the
human body. As much as 20% of the energy in your
body is consumed by your brain, although it accounts
only for about 2% of your body mass. This may come
as no surprise, given that you need your brain for almost
anything you do, from moving your legs to walk to reading this book, to talking to your friend on the phone. Even
seeing what is right in front of your eyes takes a huge
amount of processing by the brain, as you will see in
Chapter 3. And yet, for all the amazing things your brain
achieves, it does not use much more energy than your
computer and monitor when they are “asleep.” It is estimated that your brain uses about 12–20 watts of power.
Your sleeping computer consumes about 10 watts when
it’s on, and 150 watts together with its monitor or even
more. Even the lamp on your desk uses more power than
your brain. Your brain performs many more tasks than
your desk lamp or computer. Just think about all you’d
have to eat if your brain consumed as much energy as
those devices (Drubach, 1999). You’ll learn more about
how your brain works in this chapter.
Our brains are a central processing unit for everything we do. But how do our brains
relate to our bodies? Are they connected or separate? Do our brains define who we are?
An ancient legend from India (Rosenzweig & Leiman, 1989) tells of Sita. She marries
one man but is attracted to another. These two frustrated men behead themselves. Sita,
bereft of them both, desperately prays to the goddess Kali to bring the men back to life.
Sita is granted her wish. She is allowed to reattach the heads to the bodies. In her rush
to bring the two men back to life, Sita mistakenly switches their heads. She attaches
them to the wrong bodies. Now, to whom is she married? Who is who?
The mind–body issue has long interested philosophers and scientists. Where is the
mind located in the body, if at all? How do the mind and body interact? How are we
able to think, speak, plan, reason, learn, and remember? What are the physical bases
for our cognitive abilities? These questions all probe the relationship between
cognitive psychology and neurobiology. Some cognitive psychologists seek to answer
such questions by studying the biological bases of cognition. Cognitive psychologists
are especially concerned with how the anatomy (physical structures of the body) and
the physiology (functions and processes of the body) of the nervous system affect and
are affected by human cognition.
Cognitive neuroscience is the field of study linking the brain and other aspects of
the nervous system to cognitive processing and, ultimately, to behavior. The brain is
the organ in our bodies that most directly controls our thoughts, emotions, and
motivations (Gloor, 1997; Rockland, 2000; Shepherd, 1998). Figure 2.1 shows
photos of what the brain actually looks like. We usually think of the brain as being
at the top of the body’s hierarchy—as the boss, with various other organs responding
to it. Like any good boss, however, it listens to and is influenced by its subordinates,
the other organs of the body. Thus, the brain is reactive as well as directive.
43
(a)
(b)
© A. Glauberman/Photo Researchers, Inc.
Harvard University Gazette photo by Jon Chase
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
Figure 2.1 The Brain.
What does a brain actually look like? Here you can see side (a) and top (b) views of a human brain. Subsequent
figures and schematic pictures (i.e., simplified diagrams) point out in more detail some of the main features of the brain.
A major goal of present research on the brain is to study localization of function.
Localization of function refers to the specific areas of the brain that control specific
skills or behaviors. Facts about particular brain areas and their function are
interspersed throughout this chapter and also throughout the whole book.
Our exploration of the brain starts with the anatomy of the brain. We will look at
the gross anatomy of the brain as well as at neurons and the ways in which
information is transmitted in the brain. Then we will explore the methods scientists
use to examine the brain, its structures, and functions. And finally, we will learn about
brain disorders and how they inform cognitive psychology.
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
The nervous system is the basis for our ability to perceive, adapt to, and interact
with the world around us (Gazzaniga, 1995, 2000; Gazzaniga, Ivry, & Mangun,
1998). Through this system we receive, process, and then respond to information
from the environment (Pinker, 1997a; Rugg, 1997). In the following section, we
will focus on the supreme organ of the nervous system—the brain—paying special
attention to the cerebral cortex, which controls many of our thought processes. In
a later section, we consider the basic building block of the nervous system—the neuron. We will examine in detail how information moves through the nervous system
at the cellular level. Then we will consider the various levels of organization within
the nervous system and how drugs interact with the nervous system. For now, let’s
look at the structure of the brain.
Gross Anatomy of the Brain: Forebrain, Midbrain, Hindbrain
What have scientists discovered about the human brain? The brain has three major
regions: forebrain, midbrain, and hindbrain. These labels do not correspond exactly
to locations of regions in an adult or even a child’s head. Rather, the terms come
44
CHAPTER 2 • Cognitive Neuroscience
from the front-to-back physical arrangement of these parts in the nervous system of a
developing embryo. Initially, the forebrain is generally the farthest forward, toward
what becomes the face. The midbrain is next in line. And the hindbrain is generally
farthest from the forebrain, near the back of the neck [Figure 2.2 (a)]. In development, the relative orientations change so that the forebrain is almost a cap on top of
the midbrain and hindbrain. Nonetheless, the terms still are used to designate areas
Midbrain
Midbrain
Cerebellum
and pons
Hindbrain
Forebrain
Medulla
Spinal cord
Neural
tube
Armbud
(a) 5 weeks (in utero)
Cerebral
hemispheres
(b) 8 weeks (in utero)
Cerebral
hemispheres
Midbrain
Cerebellum
Medulla
Spinal cord
(c) 7 months (in utero)
Figure 2.2 Fetal Brain Development.
Over the course of embryonic and fetal development, the brain becomes more highly specialized and the locations and
relative positions of the hindbrain, the midbrain, and the forebrain change from conception to term.
Source: From In Search of the Human Mind by Robert J. Sternberg, copyright © 1995 by Harcourt Brace & Company. Reproduced by
permission of the publisher.
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
45
of the fully developed brain. Figures 2.2 (b) and (c) show the changing locations
and relationships of the forebrain, the midbrain, and the hindbrain over the course
of development of the brain. You can see how they develop, from an embryo a few
weeks after conception to a fetus of seven months of age.
The Forebrain
The forebrain is the region of the brain located toward the top and front of the
brain. It comprises the cerebral cortex, the basal ganglia, the limbic system, the thalamus, and the hypothalamus (Figure 2.3). The cerebral cortex is the outer layer of
the cerebral hemispheres. It plays a vital role in our thinking and other mental processes. It therefore merits a special section in this chapter, which follows the present
Cerebral cortex
(controls thinking and sensing
functions, voluntary movement)
Corpus callosum
(relays information
between the two
cerebral hemispheres)
Septum
(influences
anger
and fear)
Hippocampus
(influences learning
and memory)
Thalamus
(relays sensory information
to cerebral cortex)
Hypothalamus
(regulates temperature,
eating, sleeping, and
endocrine system)
Basal ganglia
Amygdala
(influences
anger and
aggression)
Pituitary gland
(master gland
of the endocrine
system)
Midbrain
(reticular activating system:
carries messages about
sleep and arousal)
Pons
(relays information
between cerebral
cortex and cerebellum)
Cerebellum
(coordinates fine muscle
movement, balance)
Medulla
(regulates heartbeat, breathing)
Spinal cord
(relays nerve impulses between
brain and body, controls
simple reflexes)
Figure 2.3 Structures of the Brain.
The forebrain, the midbrain, and the hindbrain contain structures that perform essential functions for survival and for
high-level thinking and feeling.
Source: From Psychology: In Search of the Human Mind by Robert J. Sternberg, copyright © 2000 by Harcourt Brace & Company, reproduced
by permission of the publisher.
46
CHAPTER 2 • Cognitive Neuroscience
discussion of the major structures and functions of the brain. The basal ganglia
(singular: ganglion) are collections of neurons crucial to motor function. Dysfunction of the basal ganglia can result in motor deficits. These deficits include tremors,
involuntary movements, changes in posture and muscle tone, and slowness of movement. Deficits are observed in Parkinson’s disease and Huntington’s disease. Both
these diseases entail severe motor symptoms (Rockland, 2000; Lerner & Riley,
2008; Lewis & Barker, 2009).
The limbic system is important to emotion, motivation, memory, and learning.
Animals such as fish and reptiles, which have relatively undeveloped limbic systems,
respond to the environment almost exclusively by instinct. Mammals and especially
humans have relatively more developed limbic systems. Our limbic system allows us to
suppress instinctive responses (e.g., the impulse to strike someone who accidentally
causes us pain). Our limbic systems help us to adapt our behaviors flexibly in response
to our changing environment. The limbic system comprises three central interconnected cerebral structures: the septum, the amygdala, and the hippocampus.
The septum is involved in anger and fear. The amygdala plays an important role
in emotion as well, especially in anger and aggression (Adolphs, 2003; Derntl et al.,
2009). Stimulation of the amygdala commonly results in fear. It can be evidenced in
various ways, such as through palpitations, fearful hallucinations, or frightening
flashbacks in memory (Engin & Treit, 2008; Gloor, 1997; Rockland, 2000).
Damage to (lesions in) or removal of the amygdala can result in maladaptive
lack of fear. In the case of lesions to the animal brain, the animal approaches
potentially dangerous objects without hesitation or fear (Adolphs et al., 1994;
Frackowiak et al., 1997). The amygdala also has an enhancing effect for the perception of emotional stimuli. In humans, lesions to the amygdala prevent this enhancement (Anderson & Phelps, 2001; Tottenham, Hare, & Casey, 2009).
Additionally, persons with autism display limited activation in the amygdala. A
well-known theory of autism suggests that the disorder involves dysfunction of the
amygdala, which leads to the social impairment that is typical of persons with
autism, for example, difficulties in evaluating people’s trustworthiness or recognizing emotions in faces (Adolphs, Sears, & Piven, 2001; Baron-Cohen et al., 2000;
Howard et al., 2000; Kleinhans et al., 2009) Two other effects of lesions to the
amygdala can be visual agnosia (inability to recognize objects) and hypersexuality
(Steffanaci, 1999).
The hippocampus plays an essential role in memory formation (Eichenbaum,
1999, 2002; Gluck, 1996; Manns & Eichenbaum, 2006; O’Keefe, 2003). It gets its
name from the Greek word for “seahorse,” its approximate shape. The hippocampus
is essential for flexible learning and for seeing the relations among items learned as
well as for spatial memory (Eichenbaum, 1997; Squire, 1992). The hippocampus also
appears to keep track of where things are and how these things are spatially related
to each other. In other words, it monitors what is where (Cain, Boon, & Corcoran,
2006; Howland et al., 2008; McClelland et al., 1995; Tulving & Schacter, 1994).
We return to the role of the hippocampus in Chapter 5.
People who have suffered damage to or removal of the hippocampus still can
recall existing memories—for example, they can recognize old friends and places—
but they are unable to form new memories (relative to the time of the brain
damage). New information—new situations, people, and places—remain forever
new. A disease that produces loss of memory function is Korsakoff’s syndrome.
Other symptoms include apathy, paralysis of muscles controlling the eye, and tremor.
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
47
IN THE LAB OF MARTHA FARAH
Cognitive Neuroscience
and Childhood Poverty
collaborator. In first graders and in
middle-school students, we again found
striking SES disparities in language and
Around the time I had my daughter,
executive function, as well as in declaraI shifted my research focus to developtive memory. Assuming that these disparimental cognitive neuroscience. People natties are the result of different early life
urally assumed that these two life changes
experiences, what is it about growing up
were related, and they were—but not in
poor that would interfere with the developthe way people thought. What captured
ment of these specific systems?
MARTHA FARAH
my interest in brain development was not
In one study, we made use of data
principally watching my daughter grow, as wondrous
collected earlier on the middle-school children just mena process as that was. Rather, it was getting to know
tioned. We found that their language ability in middle
the babysitters who entered our lives, and learning about
school was predicted by the amount of cognitive stimutheirs.
lation they experienced as four-year olds—being read
These babysitters were young women of low socioto, taken on trips, and so on. In contrast, we found that
economic status (SES), who grew up in families depentheir declarative memory ability in middle school was
dent on welfare and supported their own young
predicted by the quality of parental nurturance that they
children with a combination of state assistance supplereceived as young children—being held close, being
mented with cash wages from babysitting. As carepaid attention to, and so on. The latter finding might
givers for my child, they were not merely hired help;
seem an odd association. Why would affectionate parthey were people I liked, trusted, and grew to care
enting have anything to do with memory? Yet research
about. And as we became closer, and I spent more
with animals shows that when a young animal is
time with their families, I learned about a world very
stressed, the resulting stress hormones can damage
different from my own.
the hippocampus, a brain area important for both stress
The children of these inner-city families started life
regulation and memory. This research has also shown
with the same evident potential as my own child, learnthat more nurturing maternal behavior can buffer the
ing words, playing games, asking questions, and grapyoung animal’s hippocampus against the effects of
pling with the challenges of cooperation, discipline,
stress. It would appear that children living in the stressful
and self-control. But they soon found their way onto
environment of poverty benefit in a similar way from
the same dispiriting life trajectories as their parents,
attentive and affectionate parenting.
with limited skills, options, hope. As a mother, I found it
Our most recent work, with graduate student
heart-breaking. As a scientist, I wanted to understand.
Daniel Hackman and radiology colleague Hengyi
This led to a series of studies in which my collaRao, has tested these hypothesized mechanisms more
borators and I tried first to simply document the effects
directly. Brain imaging has confirmed that hippocamof childhood poverty in terms of cognitive neurospal size is affected by early life parental nurturance in
cience’s description of the mind, and then to explain
low SES individuals, and direct measures of hormonal
the effects of poverty in terms of more specific, mecharesponses to stress indicate that both SES and parenting
nistic causes. With Kim Noble, then a graduate student
in early childhood program later life stress response.
in my lab, we assessed the functioning of five different
Our ultimate goal is to understand the complex web
neurocognitive systems in kindergarteners of low and
of social, psychological and physiological influences
middle SES. We found the most pronounced effects in
that act upon children in low SES families and to use
language and executive function systems. These results
that understanding to help them achieve their true
were replicated and expanded upon in additional studpotential.
ies with Noble and with Hallam Hurt, a pediatrician
48
CHAPTER 2 • Cognitive Neuroscience
This loss is believed to be associated with deterioration of the hippocampus and is
caused by a lack of thiamine (Vitamin B-1) in the brain. The syndrome can result
from excessive alcohol use, dietary deficiencies, or eating disorders.
There is a renowned case of a patient known as H.M., who after brain surgery
retained his memory for events that transpired before the surgery but had no memory
for events after the surgery. This case is another illustration of the resulting problems
with memory formation due to hippocampus damage (see Chapter 5 for more on
H.M.). Disruption in the hippocampus appears to result in deficits in declarative
memory (i.e., memory for pieces of information), but it does not result in deficits
in procedural memory (i.e., memory for courses of action) (Rockland, 2000).
The thalamus relays incoming sensory information through groups of neurons
that project to the appropriate region in the cortex. Most of the sensory input into
the brain passes through the thalamus, which is approximately in the center of the
brain, at about eye level. To accommodate all the types of information that must be
sorted out, the thalamus is divided into a number of nuclei (groups of neurons of
similar function). Each nucleus receives information from specific senses. The information is then relayed to corresponding specific areas in the cerebral cortex. The
thalamus also helps in the control of sleep and waking. When the thalamus malfunctions, the result can be pain, tremor, amnesia, impairment of language, and
disruptions in waking and sleeping (Rockland, 2000; Steriade, Jones, & McCormick,
1997). In cases of schizophrenia, imaging and in vivo studies reveal abnormal
changes in the thalamus (Clinton & Meador-Woodruff, 2004). These abnormalities
result in difficulties in filtering stimuli and focusing attention, which in turn can
explain why people suffering from schizophrenia experience symptoms such as hallucinations and delusions.
The hypothalamus regulates behavior related to species survival: fighting, feeding, fleeing, and mating. The hypothalamus also is active in regulating emotions and
reactions to stress (Malsbury, 2003). It interacts with the limbic system. The small
size of the hypothalamus (from Greek hypo-, “under”; located at the base of the forebrain, beneath the thalamus) belies its importance in controlling many bodily functions (Table 2.1). The hypothalamus plays a role in sleep: Dysfunction and neural
loss within the hypothalamus are noted in cases of narcolepsy, whereby a person falls
asleep often and at unpredictable times (Lodi et al., 2004; Mignot, Taheri, &
Nishino, 2002). The hypothalamus also is important for the functioning of the endocrine system. It is involved in the stimulation of the pituitary glands, through
which a range of hormones are produced and released. These hormones include
growth hormones and oxytocin (which is involved in bonding processes and sexual
arousal; Gazzaniga, Ivry, & Mangun, 2009).
The forebrain, midbrain, and hindbrain contain structures that perform essential
functions for survival as well as for high-level thinking and feeling. For a summary
of the major structures and functions of the brain, as discussed in this section, see
Table 2.1.
The Midbrain
The midbrain helps to control eye movement and coordination. The midbrain is
more important in nonmammals where it is the main source of control for visual
and auditory information. In mammals these functions are dominated by the forebrain. Table 2.1 lists several structures and corresponding functions of the midbrain.
By far the most indispensable of these structures is the reticular activating system
(RAS; also called the “reticular formation”), a network of neurons essential to the
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
Table 2.1
49
Major Structures and Functions of the Brain
Region of
the Brain
Major Structures within
the Regions
Forebrain
Cerebral cortex (outer
layer of the cerebral
hemispheres)
Involved in receiving and processing sensory information, thinking, other cognitive
processing, and planning and sending
motor information
Basal ganglia (collections
of nuclei and neural fibers)
Crucial to the function of the motor system
Limbic systems (hippocampus, amygdala, and
septum)
Involved in learning, emotions, and motivation (in particular, the hippocampus influences learning and memory, the
amygdala influences anger and aggression, and the septum influences anger and
fear)
Thalamus
Primary relay station for sensory information coming into the brain; transmits information to the correct regions of the
cerebral cortex through projection fibers
that extend from the thalamus to specific
regions of the cortex; comprises several
nuclei (groups of neurons) that receive
specific kinds of sensory information and
project that information to specific regions
of the cerebral cortex, including four key
nuclei for sensory information: (1) from the
visual receptors, via optic nerves, to the
visual cortex, permitting us to see; (2) from
the auditory receptors, via auditory nerves,
to the auditory cortex, permitting us to
hear; (3) from sensory receptors in the somatic nervous system, to the primary somatosensory cortex, permitting us to sense
pressure and pain; and (4) from the cerebellum (in the hindbrain) to the primary
motor cortex, permitting us to sense physical balance and equilibrium
Hypothalamus
Controls the endocrine system; controls the
autonomic nervous system, such as internal
temperature regulation, appetite and thirst
regulation, and other key functions; involved in regulation of behavior related to
species survival (in particular, fighting,
feeding, fleeing, and mating); plays a role
in controlling consciousness (see reticular
activating system); involved in emotions,
pleasure, pain, and stress reactions
Superior colliculi (on top)
Involved in vision (especially visual
reflexes)
Inferior colliculi (below)
Involved in hearing
Midbrain
Functions of the Structures
(continued )
50
CHAPTER 2 • Cognitive Neuroscience
Table 2.1
Region of
the Brain
Hindbrain
Continued
Major Structures within
the Regions
Functions of the Structures
Reticular activating system
(also extends into the
hindbrain)
Important in controlling consciousness
(sleep arousal), attention, cardiorespiratory function, and movement
Gray matter, red nucleus,
substantia nigra, ventral
region
Important in controlling movement
Cerebellum
Essential to balance, coordination, and
muscle tone
Pons (also contains part of
the RAS)
Involved in consciousness (sleep and
arousal); bridges neural transmissions from
one part of the brain to another; involved
with facial nerves
Medulla oblongata
Serves as juncture at which nerves cross
from one side of the body to opposite side
of the brain; involved in cardiorespiratory
function, digestion, and swallowing
regulation of consciousness (sleep; wakefulness; arousal; attention to some extent;
and vital functions such as heartbeat and breathing; Sarter, Bruno, & Berntson,
2003).
The RAS also extends into the hindbrain. Both the RAS and the thalamus are
essential to our having any conscious awareness of or control over our existence.
The brainstem connects the forebrain to the spinal cord. It comprises the hypothalamus, the thalamus, the midbrain, and the hindbrain. A structure called the periaqueductal gray (PAG) is in the brainstem. This region seems to be essential for
certain kinds of adaptive behaviors. Injections of small amounts of excitatory amino
acids or, alternatively, electrical stimulation of this area results in any of several responses: an aggressive, confrontational response; avoidance or flight response;
heightened defensive reactivity; or reduced reactivity as is experienced after a defeat,
when one feels hopeless (Bandler & Shipley, 1994; Rockland, 2000).
Physicians make a determination of brain death based on the function of the
brainstem. Specifically, a physician must determine that the brainstem has been
damaged so severely that various reflexes of the head (e.g., the pupillary reflex) are
absent for more than 12 hours, or the brain must show no electrical activity or cerebral circulation of blood (Berkow, 1992).
The Hindbrain
The hindbrain comprises the medulla oblongata, the pons, and the cerebellum.
The medulla oblongata controls heart activity and largely controls breathing,
swallowing, and digestion. The medulla is also the place at which nerves from the
right side of the body cross over to the left side of the brain and nerves from the left
side of the body cross over to the right side of the brain. The medulla oblongata is
an elongated interior structure located at the point where the spinal cord enters the
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
51
skull and joins with the brain. The medulla oblongata, which contains part of the
RAS, helps to keep us alive.
The pons serves as a kind of relay station because it contains neural fibers that
pass signals from one part of the brain to another. Its name derives from the Latin
for “bridge,” as it serves a bridging function. The pons also contains a portion of the
RAS and nerves serving parts of the head and face. The cerebellum (from Latin,
“little brain”) controls bodily coordination, balance, and muscle tone, as well as
some aspects of memory involving procedure-related movements (see Chapters 7
and 8) (Middleton & Helms Tillery, 2003). The prenatal development of the human brain within each individual roughly corresponds to the evolutionary development of the human brain within the species as a whole. Specifically, the hindbrain is
evolutionarily the oldest and most primitive part of the brain. It also is the first part
of the brain to develop prenatally. The midbrain is a relatively newer addition to the
brain in evolutionary terms. It is the next part of the brain to develop prenatally.
Finally, the forebrain is the most recent evolutionary addition to the brain. It is
the last of the three portions of the brain to develop prenatally.
Additionally, across the evolutionary development of our species, humans have
shown an increasingly greater proportion of brain weight in relation to body weight.
However, across the span of development after birth, the proportion of brain weight
to body weight declines. For cognitive psychologists, the most important of these
evolutionary trends is the increasing neural complexity of the brain. The evolution
of the human brain has offered us the enhanced ability to exercise voluntary control
over behavior. It has also strengthened our ability to plan and to contemplate alternative courses of action. These ideas are discussed in the next section with respect to
the cerebral cortex.
Cerebral Cortex and Localization of Function
The cerebral cortex plays an extremely important role in human cognition. It forms
a 1- to 3-millimeter layer that wraps the surface of the brain somewhat like the bark
of a tree wraps around the trunk. In human beings, the many convolutions, or
creases, of the cerebral cortex comprise three elements. Sulci (singular, sulcus) are
small grooves. Fissures are large grooves. And gyri (singular, gyrus) are bulges
between adjacent sulci or fissures. These folds greatly increase the surface area of the
cortex. If the wrinkly human cortex were smoothed out, it would take up about 2
square feet. The cortex comprises 80% of the human brain (Kolb & Whishaw, 1990).
The volume of the human skull has more than doubled over the past 2 million
years, allowing for the expansion of the brain, and especially the cortex (Toro et al.,
2008). The complexity of brain function increases with the cortical area. The human cerebral cortex enables us to think. Because of it, we can plan, coordinate
thoughts and actions, perceive visual and sound patterns, and use language. Without
it, we would not be human. The surface of the cerebral cortex is grayish. It is sometimes referred to as gray matter. This is because it primarily comprises the grayish
neural-cell bodies that process the information that the brain receives and sends. In
contrast, the underlying white matter of the brain’s interior comprises mostly white,
myelinated axons.
The cerebral cortex forms the outer layer of the two halves of the brain—the
left and right cerebral hemispheres (Davidson & Hugdahl, 1995; Galaburda &
Rosen, 2003; Gazzaniga & Hutsler, 1999; Levy, 2000). Although the two hemispheres appear to be quite similar, they function differently. The left cerebral
52
CHAPTER 2 • Cognitive Neuroscience
hemisphere is specialized for some kinds of activity whereas the right cerebral hemisphere is specialized for other kinds. For example, receptors in the skin on the right
side of the body generally send information through the medulla to areas in the left
hemisphere in the brain. The receptors on the left side generally transmit information to the right hemisphere. Similarly, the left hemisphere of the brain directs the
motor responses on the right side of the body. The right hemisphere directs responses on the left side of the body.
However, not all information transmission is contralateral—from one side to
another (contra-, “opposite”; lateral, “side”). Some ipsilateral transmission—on the
same side—occurs as well. For example, odor information from the right nostril goes
primarily to the right side of the brain. About half the information from the right
eye goes to the right side of the brain, the other half goes to the left side of the brain.
In addition to this general tendency for contralateral specialization, the hemispheres
also communicate directly with one another. The corpus callosum is a dense aggregate of neural fibers connecting the two cerebral hemispheres (Witelson, Kigar, &
Walter, 2003). It allows transmission of information back and forth. Once information
has reached one hemisphere, the corpus callosum transfers it to the other hemisphere.
If the corpus callosum is cut, the two cerebral hemispheres—the two halves of the
brain—cannot communicate with each other (Glickstein & Berlucchi, 2008). Although some functioning, like language, is highly lateralized, most functioning—even
language—depends in large part on integration of the two hemispheres of the brain.
Hemispheric Specialization
How did psychologists find out that the two hemispheres have different responsibilities? The study of hemispheric specialization in the human brain can be traced back
to Marc Dax, a country doctor in France. By 1836, Dax had treated more than
40 patients suffering from aphasia—loss of speech—as a result of brain damage. Dax
noticed a relationship between the loss of speech and the side of the brain in which
damage had occurred. In studying his patients’ brains after death, Dax saw that in
every case there had been damage to the left hemisphere of the brain. He was not
able to find even one case of speech loss resulting from damage to the right hemisphere only.
In 1861, French scientist Paul Broca claimed that an autopsy revealed that an
aphasic stroke patient had a lesion in the left cerebral hemisphere of the brain. By
1864, Broca was convinced that the left hemisphere of the brain is critical in speech,
a view that has held up over time. The specific part of the brain that Broca identified, now called Broca’s area, contributes to speech (Figure 2.4).
Another important early researcher, German neurologist Carl Wernicke, studied
language-deficient patients who could speak but whose speech made no sense. Like
Broca, he traced language ability to the left hemisphere. He studied a different precise location, now known as Wernicke’s area, which contributes to language comprehension (Figure 2.4).
Karl Spencer Lashley, often described as the father of neuropsychology, started
studying localization in 1915. He found that implantations of crudely built electrodes in apparently identical locations in the brain yielded different results. Different
locations sometimes paradoxically yielded the same results (e.g., see Lashley, 1950).
Subsequent researchers, using more sophisticated electrodes and measurement
procedures, have found that specific locations do correlate with specific motor
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
53
Sensory cortex
Motor cortex
Association
cortex
Association
cortex
Auditory
cortex
Broca’s area
(speech)
Visual
cortex
Wernicke’s area
(understanding
language)
Figure 2.4 Functional Areas of the Cortex.
Strangely, although people with lesions in Broca’s area cannot speak fluently, they can use their voices to sing or shout.
Source: From Introduction to Psychology, 11/e, by Richard Atkinson, Rita Atkinson, Daryl Bem, Ed Smith, and Susan Nolen Hoeksema,
copyright © 1995 by Harcourt Brace & Company, reproduced by permission of the publisher.
responses across many test sessions. Apparently, Lashley’s research was limited by the
technology available to him at the time.
Despite the valuable early contributions by Broca, Wernicke, and others, the
individual most responsible for modern theory and research on hemispheric specialization was Nobel Prize–winning psychologist Roger Sperry. Sperry (1964) argued
that each hemisphere behaves in many respects like a separate brain. In a classic
experiment that supports this contention, Sperry and his colleagues severed the corpus callosum connecting the two hemispheres of a cat’s brain. They then proved
that information presented visually to one cerebral hemisphere of the cat was not
recognizable to the other hemisphere. Similar work on monkeys indicated the same
discrete performance of each hemisphere (Sperry, 1964).
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CHAPTER 2 • Cognitive Neuroscience
Some of the most interesting information about how the human brain works,
and especially about the respective roles of the hemispheres, has emerged from studies of humans with epilepsy in whom the corpus callosum has been severed. Surgically severing this neurological bridge prevents epileptic seizures from spreading from
one hemisphere to another. This procedure thereby drastically reduces the severity of
the seizures. However, this procedure also results in a loss of communication between the two hemispheres. It is as if the person has two separate specialized brains
processing different information and performing separate functions.
Split-brain patients are people who have undergone operations severing the corpus callosum. Split-brain research reveals fascinating possibilities regarding the ways
we think. Many in the field have argued that language is localized in the left hemisphere. Spatial visualization ability appears to be largely localized in the right hemisphere (Farah, 1988a, 1988b; Gazzaniga, 1985). Spatial-orientation tasks also seem
to be localized in the right hemisphere (Vogel, Bowers, & Vogel, 2003). It appears
that roughly 90% of the adult population has language functions that are predominantly localized within the left hemisphere. There are indications, however, that the
lateralization of left-handers differs from that of right-handers, and that for females,
the lateralization may not be as pronounced as for males (Vogel, Bowers, & Vogel,
2003). More than 95% of right-handers and about 70% of left-handers have lefthemisphere dominance for language. In people who lack left-hemisphere processing,
language development in the right hemisphere retains phonemic and semantic abilities, but it is deficient in syntactic competence (Gazzaniga & Hutsler, 1999).
The left hemisphere is important not only in language but also in movement.
People with apraxia—disorders of skilled movements—often have had damage to
the left hemisphere. Such people have lost the ability to carry out familiar purposeful
movements like forming letters when writing by hand (Gazzaniga & Hutsler, 1999;
Heilman, Coenen, & Kluger, 2008). Another role of the left hemisphere is to examine past experiences to find patterns. Finding patterns is an important step in the
generation of hypotheses (Wolford, Miller, & Gazzaniga, 2000). For example, while
observing an airport, you may notice that planes often approach the landing strip
from different directions. However, you may soon find that at any given time, all
planes approach from the same direction. You then might hypothesize that the direction of their approach may have to do with the wind direction and speed. Thus,
you have observed a pattern and generated ideas about what causes this pattern with
the help of your left hemisphere.
The right hemisphere is largely “mute” (Levy, 2000). It has little grammatical or
phonetic understanding. But it does have very good semantic knowledge. It also is
involved in practical language use. People with right-hemisphere damage tend to
have deficits in following conversations or stories. They also have difficulties in
making inferences from context and in understanding metaphorical or humorous
speech (Levy, 2000). The right hemisphere also plays a primary role in selfrecognition. In particular, the right hemisphere seems to be responsible for the identification of one’s own face (Platek et al., 2004).
In studies of split-brain patients, the patient is presented with a composite
photograph that shows a face that is made up of the left and right side of the faces
of two different persons (Figure 2.5). They are typically unaware that they saw
conflicting information in the two halves of the picture. When asked to give an answer about what they saw in words, they report that they saw the image in the right
half of the picture. When they are asked to use the fingers of the left hand (which
contralaterally sends and receives information to and from the right hemisphere) to
point to what they saw, participants choose the image from the left half of the
(a)
“Whom did you see?”
“It was Cher.”
“Point to the person you saw”
(b)
(c)
55
Madonna: Dino de Laurentiis/The Kobal Collection/The Picture Desk; Oprah Winfrey: Dima Gavrysh/AP Photo; Angelina Jolie: w38/Zuma/Photoshot; Cher: The Kobal Collection/The Picture Desk
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
Figure 2.5 A Study with Split-brain Patients.
In one study, the participant is asked to focus his or her gaze on the center of the screen. Then a chimeric face (a face
showing the left side of the face of one person and the right side of another) is flashed on the screen. The participant then
is asked to identify what he or she saw, either by speaking or by pointing to one of several normal (not chimeric) faces.
picture. Recall the contralateral association between hemisphere and side of the
body. Given this, it seems that the left hemisphere is controlling their verbal processing (speaking) of visual information. The right hemisphere appears to control spatial processing (pointing) of visual information. Thus, the task that the participants
are asked to perform is crucial in determining what image the participant thinks
was shown.
56
CHAPTER 2 • Cognitive Neuroscience
Gazzaniga (Gazzaniga & LeDoux, 1978) does not believe that the two hemispheres function completely independently but rather that they serve complementary roles. For instance, there is no language processing in the right hemisphere
(except in rare cases of early brain damage to the left hemisphere). Rather, only visuospatial processing occurs in the right hemisphere. As an example, Gazzaniga has
found that before split-brain surgery, people can draw three-dimensional representations of cubes with each hand (Gazzaniga & LeDoux, 1978). After surgery, however,
they can draw a reasonable-looking cube only with the left hand. In each patient,
the right hand draws pictures unrecognizable either as cubes or as threedimensional objects. This finding is important because of the contralateral association between each side of the body and the opposite hemisphere of the brain. Recall
that the right hemisphere controls the left hand. The left hand is the only one that
a split-brain patient can use for drawing recognizable figures. This experiment thus
supports the contention that the right hemisphere is dominant in our comprehension and exploration of spatial relations.
Gazzaniga (1985) argues that the brain, and especially the right hemisphere of the
brain, is organized into relatively independent functioning units that work in parallel.
According to Gazzaniga, each of the many discrete units of the mind operates relatively independently of the others. These operations are often outside of conscious
awareness. While these various independent and often subconscious operations are
taking place, the left hemisphere tries to assign interpretations to these operations.
Sometimes the left hemisphere perceives that the individual is behaving in a way
that does not intrinsically make any particular sense. For example, if you see an adult
staggering along a sidewalk at night in a way that does not initially make sense, you
may conclude he is drunk or otherwise not in full control of his senses. The brain thus
finds a way to assign some meaning to that behavior.
In addition to studying hemispheric differences in language and spatial relations,
researchers have tried to determine whether the two hemispheres think in ways that
differ from one another. Levy (1974) has found some evidence that the left hemisphere tends to process information analytically (piece-by-piece, usually in a sequence).
She argues that the right hemisphere tends to process it holistically (as a whole).
Lobes of the Cerebral Hemispheres
For practical purposes, four lobes divide the cerebral hemispheres and cortex into
four parts. They are not distinct units. Rather, they are largely arbitrary anatomical
regions divided by fissures. Particular functions have been identified with each lobe,
but the lobes also interact. The four lobes, named after the bones of the skull lying
directly over them (Figure 2.6), are the frontal, parietal, temporal, and occipital
lobes. The lobes are involved in numerous functions. Our discussion of them here
describes only part of what they do.
The frontal lobe, toward the front of the brain, is associated with motor processing and higher thought processes, such as abstract reasoning, problem solving,
planning, and judgment (Stuss & Floden, 2003). It tends to be involved when
sequences of thoughts or actions are called for. It is critical in producing speech.
The prefrontal cortex, the region toward the front of the frontal lobe, is involved in
complex motor control and tasks that require integration of information over time
(Gazzaniga, Ivry, & Mangun, 2002).
The parietal lobe, at the upper back portion of the brain, is associated with somatosensory processing. It receives inputs from the neurons regarding touch, pain,
temperature sense, and limb position when you are perceiving space and your
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
57
Dorsal
(superior)
Parietal
lobe
Central fissure
Left
hemisphere
Right
hemisphere
Lateral
fissure
Frontal
lobe
Rostral
(anterior)
Frontal lobe
Longitudinal
fissure
Caudal
(posterior)
Central fissure
Occipital
lobe
Ventral
(interior)
Ventral
Dorsal
Parietal lobe
Temporal
lobe
Occipital
lobe
(a) Anatomical areas (left lateral view)
Longitudinal
fissure
(b) Anatomical areas (top view)
Figure 2.6 Four Lobes of the Brain.
The cortex is divided into the frontal, parietal, temporal, and occipital lobes. The lobes have specific functions but also
interact to perform complex processes.
Source: From Psychology: In Search of the Human Mind by Robert J. Sternberg, copyright © 2000 by Harcourt Brace & Company, reproduced
by permission of the publisher.
relationship to it—how you are situated relative to the space you are occupying
(Culham, 2003; Gazzaniga, Ivry, & Mangun, 2002). The parietal lobe is also involved in consciousness and paying attention. If you are paying attention to what
you are reading, your parietal lobe is activated.
The temporal lobe, directly under your temples, is associated with auditory
processing (Murray, 2003) and comprehending language. It is also involved in your
retention of visual memories. For example, if you are trying to keep in memory
Figure 2.6, then your temporal lobe is involved. The temporal lobe also matches
new things you see to what you have retained in visual memory.
The occipital lobe is associated with visual processing (De Weerd, 2003b). The
occipital lobe contains numerous visual areas, each specialized to analyze specific aspects of a scene, including color, motion, location, and form (Gazzaniga, Ivry, &
Mangun, 2002). When you go to pick strawberries, your occipital lobe is involved
in helping you find the red strawberries in between the green leaves.
Projection areas are the areas in the lobes in which sensory processing occurs.
These areas are referred to as projection areas because the nerves contain sensory
information going to (projecting to) the thalamus. It is from here that the sensory
information is communicated to the appropriate area in the relevant lobe. Similarly,
the projection areas communicate motor information downward through the spinal
cord to the appropriate muscles via the peripheral nervous system (PNS). Now let us
consider the lobes, and especially the frontal lobe in more detail.
The frontal lobe, located toward the front of the head (the face), plays a role
in judgment, problem solving, personality, and intentional movement. It contains the
primary motor cortex, which specializes in the planning, control, and execution of
58
CHAPTER 2 • Cognitive Neuroscience
Hip
er
Should
ow
Elb
ist
Ha
Wr
nd
L
R ittle
M ing fing
In idd fin e
Th de le ger r
u x fin
Ne mb fing ge
ck
er r
Bro
w
Eyeli
d
Face and eyeb
all
Trunk
movement, particularly of movement involving any kind of delayed response. If your
motor cortex were electrically stimulated, you would react by moving a corresponding
body part. The nature of the movement would depend on where in the motor cortex
your brain had been stimulated. Control of the various kinds of body movements is
located contralaterally on the primary motor cortex. A similar inverse mapping occurs
from top to bottom. The lower extremities of the body are represented on the upper
(toward the top of the head) side of the motor cortex, and the upper part of the body
is represented on the lower side of the motor cortex.
Information going to neighboring parts of the body also comes from neighboring
parts of the motor cortex. Thus, the motor cortex can be mapped to show where
and in what proportions different parts of the body are represented in the brain
(Figure 2.7). Maps of this kind are called “homunculi” (homunculus is Latin for “little
person”) because they depict the body parts of a person mapped on the brain.
The three other lobes are located farther away from the front of the head. These
lobes specialize in sensory and perceptual activity. For example, in the parietal lobe,
the primary somatosensory cortex receives information from the senses about pressure, texture, temperature, and pain. It is located right behind the frontal lobe’s
primary motor cortex. If your somatosensory cortex were electrically stimulated, you
probably would report feeling as if you had been touched.
ee
Kn
Ankle
Toes
(Motor
cortex)
Jaw
Sw
al
lo
wi
e
gu
Ton
ng
Lips
(Sensory
cortex)
Figure 2.7 (part 1) Homunculus of the Primary Motor Cortex.
This map of the primary motor cortex is often termed a homunculus (from Latin, “little person”) because it is drawn
as a cross section of the cortex surrounded by the figure of a small upside-down person whose body parts map out a
proportionate correspondence to the parts of the cortex.
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
59
From looking at the homunculus (see Figure 2.7), you can see that the relationship of function to form applies in the development of the motor cortex. The same
holds true for the somatosensory cortex regions. The more need we have for use,
sensitivity, and fine control in a particular body part, the larger the area of cortex
generally devoted to that part. For example, we humans are tremendously reliant
on our hands and faces in our interactions with the world. We show correspondingly
large proportions of the cerebral cortex devoted to sensation in, and motor response
by, our hands and face. Conversely, we rely relatively little on our toes for both
movement and information gathering. As a result, the toes represent a relatively
small area on both the primary motor and somatosensory cortices.
The region of the cerebral cortex pertaining to hearing is located in the temporal lobe, below the parietal lobe. This lobe performs complex auditory analysis. This
kind of analysis is needed in understanding human speech or listening to a symphony. The lobe also is specialized—some parts are more sensitive to sounds of
higher pitch, others to sounds of lower pitch. The auditory region is primarily contralateral, although both sides of the auditory area have at least some representation
from each ear. If your auditory cortex were stimulated electrically, you would report
having heard some sort of sound.
Trunk
H
Le ip
g
Neck
Head er
Upper
Arm w
Elbo
In idd
d l
Th ex e fi
n
u
Ey mb finge ger
e
r
No
s
Fac e
e
Should
arm
Fore
st
Wri
nd
r
Ha
ge
fin ger
tle fin
ng
Ri
Lit
M
(Motor
cortex)
Foot
Toes
Genitals
lip
Lips
Lower lip
jaw
In
tra
-a
bd
o
m
in
al
, and
ums
h, g
Teet e
u
g
Ton ynx
ar
Ph
(Sensory
cortex)
Figure 2.7 (part 2) Homunculus of the Somatosensory Cortex.
As with the primary motor cortex in the frontal lobe, a homunculs of the somatosensory cortex maps, in inverted form,
the parts of the body from which the cortex receives information.
Source: From In Search of the Human Mind by Robert J. Sternberg, Copyright © 1995 by Harcourt Brace & Company, reproduced by permission of the publisher.
60
CHAPTER 2 • Cognitive Neuroscience
The visual cortex is primarily in the occipital lobe. Some neural fibers carrying
visual information travel ipsilaterally from the left eye to the left cerebral hemisphere and from the right eye to the right cerebral hemisphere. Other fibers cross
over the optic chiasma (from Greek, “visual X” or “visual intersection”) and go contralaterally to the opposite hemisphere (Figure 2.8). In particular, neural fibers go
from the left side of the visual field for each eye to the right side of the visual cortex.
Complementarily, the nerves from the right side of each eye’s visual field send
information to the left side of the visual cortex.
The brain is a very complex structure, and researchers use a variety of expressions to describe which part of the brain they are speaking of. Figure 2.6 explains
some other words that are frequently used to describe different brain regions. These
Primary visual
cortex
Optic chiasma
Optic nerve
Right eye
Left eye
Figure 2.8 The Optic Tract and Pathways to the Primary Visual Cortex.
Some nerve fibers carry visual information ipsilaterally from each eye to each cerebral hemisphere; other fibers cross the optic chiasma and carry visual information contralaterally to the
opposite hemisphere.
Source: From Psychology: In Search of the Human Mind by Robert J. Sternberg, copyright © 2000 by Harcourt
Brace & Company, reproduced by permission of the publisher.
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
61
are the words rostral, ventral, caudal, and dorsal. They are all derived from Latin
words and indicate the part of the brain with respect to other body parts.
•
•
•
•
Rostral refers to the front part of the brain (literally the “nasal region”).
Ventral refers to the bottom surface of the body/brain (the side of the stomach).
Caudal literally means “tail” and refers to the back part of the body/brain.
Dorsal refers to the upside of the brain (it literally means “back,” and in animals
the back is on the upside of the body).
The brain typically makes up only one fortieth of the weight of an adult human
body. Nevertheless, it uses about one fifth of the circulating blood, one fifth of the
available glucose, and one fifth of the available oxygen. It is, however, the supreme
organ of cognition. Understanding both its structure and function, from the neural
to the cerebral levels of organization, is vital to an understanding of cognitive psychology. The recent development of the field of cognitive neuroscience, with its
focus on localization of function, reconceptualizes the mind–body question discussed
in the beginning of this chapter. The question has changed from “Where is the
mind located in the body?” to “Where are particular cognitive operations located
in the nervous system?” Throughout the text, we return to these questions in reference to particular cognitive operations and discuss these operations in more detail.
Neuronal Structure and Function
To understand how the entire nervous system processes information, we need to examine the structure and function of the cells that constitute the nervous system. Individual
neural cells, called neurons, transmit electrical signals from one location to another in
the nervous system (Carlson, 2006; Shepherd, 2004). The greatest concentration of
neurons is in the neocortex of the brain. The neocortex is the part of the brain associated with complex cognition. This tissue can contain as many as 100,000 neurons per
cubic millimeter (Churchland & Sejnowski, 2004). The neurons tend to be arranged in
the form of networks, which provide information and feedback to each other within
various kinds of information processing (Vogels, Rajan, & Abbott, 2005).
Neurons vary in their structure, but almost all neurons have four basic parts, as
illustrated in Figure 2.9. These include a soma (cell body), dendrites, an axon, and
terminal buttons.
The soma, which contains the nucleus of the cell (the center portion that performs metabolic and reproductive functions for the cell), is responsible for the life of
the neuron and connects the dendrites to the axon. The many dendrites are branchlike structures that receive information from other neurons, and the soma integrates
the information. Learning is associated with the formation of new neuronal connections. Hence, it occurs in conjunction with increased complexity or ramification in
the branching structure of dendrites in the brain. The single axon is a long, thin
tube that extends (and sometimes splits) from the soma and responds to the information, when appropriate, by transmitting an electrochemical signal, which travels
to the terminus (end), where the signal can be transmitted to other neurons.
Axons are of two basic, roughly equally occurring kinds, distinguished by the
presence or absence of myelin. Myelin is a white, fatty substance that surrounds
some of the axons of the nervous system, which accounts for some of the whiteness
of the white matter of the brain. Some axons are myelinated (in that they are surrounded by a myelin sheath). This sheath, which insulates and protects longer axons
from electrical interference by other neurons in the area, also speeds up the
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CHAPTER 2 • Cognitive Neuroscience
Dendrite
Axon
terminal
button
Soma (cell body)
Nucleus
Axon
Myelin sheath
Figure 2.9 The Composition of a Neuron.
The image shows a neuron with its various components. The information arrives at the dendrites and then is transferred
through the axon to the terminal buttons.
conduction of information. In fact, transmission in myelinated axons can reach 100
meters per second (equal to about 224 miles per hour). Moreover, myelin is not distributed continuously along the axon. It is distributed in segments broken up by nodes
of Ranvier. Nodes of Ranvier are small gaps in the myelin coating along the axon,
which serve to increase conduction speed even more by helping to create electrical
signals, also called action potentials, which are then conducted down the axon. The
degeneration of myelin sheaths along axons in certain nerves is associated with multiple sclerosis, an autoimmune disease. It results in impairments of coordination and
balance. In severe cases this disease is fatal. The second kind of axon lacks the myelin
coat altogether. Typically, these unmyelinated axons are smaller and shorter (as well as
slower) than the myelinated axons. As a result, they do not need the increased conduction velocity myelin provides for longer axons (Giuliodori & DiCarlo, 2004).
The terminal buttons are small knobs found at the ends of the branches of an
axon that do not directly touch the dendrites of the next neuron. Rather, there is a
very small gap, the synapse. The synapse serves as a juncture between the terminal
buttons of one or more neurons and the dendrites (or sometimes the soma) of one or
more other neurons (Carlson, 2006). Synapses are important in cognition. Rats
show increases in both the size and the number of synapses in the brain as a result
of learning (Federmeier, Kleim & Greenough, 2002). Decreased cognitive functioning, as in Alzheimer’s disease, is associated with reduced efficiency of synaptic transmission of nerve impulses (Selkoe, 2002). Signal transmission between neurons
occurs when the terminal buttons release one or more neurotransmitters at the synapse. These neurotransmitters are chemical messengers for transmission of information across the synaptic gap to the receiving dendrites of the next neuron (von
Bohlen und Halbach & Dermietzel, 2006).
Although scientists have identified more than 100 transmitter substances, it seems
likely that more remain to be discovered. Medical and psychological researchers are
working to discover and understand neurotransmitters. In particular, they wish to
Cognition in the Brain: The Anatomy and Mechanisms of the Brain
63
understand how the neurotransmitters interact with drugs, moods, abilities, and perceptions. We know much about the mechanics of impulse transmission in nerves. But
we know relatively little about how the nervous system’s chemical activity relates to
psychological states. Despite the limits on present knowledge, we have gained some insight into how several of these substances affect our psychological functioning.
At present, it appears that three types of chemical substances are involved in
neurotransmission:
• monoamine neurotransmitters are synthesized by the nervous system through enzymatic actions on one of the amino acids (constituents of proteins, such as choline,
tyrosine, and tryptophan) in our diet (e.g., acetylcholine, dopamine, and serotonin);
• amino-acid neurotransmitters are obtained directly from the amino acids in our
diet without further synthesis (e.g., gamma-aminobutyric acid, or GABA);
• neuropeptides are peptide chains (molecules made from the parts of two or more
amino acids).
Table 2.2 lists some examples of neurotransmitters, together with their typical
functions in the nervous system and their associations with cognitive processing.
Table 2.2
Neurotransmitters
Neurotransmitters
Description
General Function
Specific Examples
Acetylcholine (Ach)
Monoamine neurotransmitter synthesized
from choline
Excitatory in brain and either
excitatory (at skeletal muscles) or inhibitory (at heart
muscles) elsewhere in the
body
Believed to be involved in memory
because of high concentration found
in the hippocampus (McIntyre et al.,
2002)
Dopamine (DA)
Monoamine neurotransmitter synthesized
from tyrosine
Influences movement, attention, and learning; mostly inhibitory but some excitatory
effects
Parkinson’s disease, characterized by
tremors and limb rigidity, results from
too little DA; some schizophrenia
symptoms are associated with too
much DA
Epinephrine and
norepinephrine
Monoamine neurotransmitter synthesized
from tyrosine
Hormones (also known as
adrenaline and noradrenaline) involved in regulation of
alertness
Involved in diverse effects on body
related to fight-or-flight reactions,
anger, and fear
Serotonin
Monoamine neurotransmitter synthesized
from tryptophan
Involved in arousal, sleep
and dreaming, and mood;
usually inhibitory but some
excitatory effects
Normally inhibits dreaming; defects in
serotonin system are linked to severe
depression
GABA (gammaaminobutyric acid)
Amino acid
neurotransmitter
General neuromodulatory
effects resulting from inhibitory influences on presynaptic axons
Currently believed to influence certain
mechanisms for learning and memory
(Izquierdo & Medina, 1997)
Glutamate
Amino acid
neurotransmitter
General neuromodulatory
effects resulting from excitatory influences on presynaptic axons
Currently believed to influence certain
mechanisms for learning and memory
(Riedel, Platt, & Micheau, 2003)
Neuropeptides
Peptide chains serving
as neurotransmitters
General neuromodulatory
effects resulting from influences on postsynaptic
membranes
Endorphins play a role in pain relief.
Neuromodulating neuropeptides
sometimes are released to enhance
the effects of Ach
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CHAPTER 2 • Cognitive Neuroscience
Acetylcholine is associated with memory functions, and the loss of acetylcholine
through Alzheimer’s disease has been linked to impaired memory functioning in
Alzheimer’s patients (Hasselmo, 2006). Acetylcholine also plays an important role in
sleep and arousal. When someone awakens, there is an increase in the activity of
so-called cholinergic neurons in the basal forebrain and the brainstem (Rockland, 2000).
Dopamine is associated with attention, learning, and movement coordination.
Dopamine also is involved in motivational processes, such as reward and reinforcement. Schizophrenics show very high levels of dopamine. This fact has led to the “dopamine theory of schizophrenia” which suggests that high levels of dopamine may be
partially responsible for schizophrenic conditions. Drugs used to combat schizophrenia
often inhibit dopamine activity (von Bohlen und Halbach & Dermietzel, 2006).
In contrast, patients with Parkinson’s disease show very low dopamine levels,
which leads to the typical trembling and movement problems associated with Parkinson’s. When patients receive medication that increases their dopamine level, they (as
well as healthy people who receive dopamine) sometimes show an increase in pathological gambling. Gambling is a compulsive disorder that results from impaired impulse
control. When dopamine treatment is suspended, these patients no longer exhibit this
behavior (Drapier et al., 2006; Voon et al., 2007; Abler et al., 2009). These findings
support the role of dopamine in motivational processes and impulse control.
Serotonin plays an important role in eating behavior and body-weight regulation.
High serotonin levels play a role in some types of anorexia. Specifically, serotonin
seems to play a role in the types of anorexia resulting from illness or treatment of
illness. For example, patients suffering from cancer or undergoing dialysis often experience a severe loss of appetite (Agulera et al., 2000; Davis et al., 2004). This loss of
appetite is related, in both cases, to high serotonin levels. Serotonin is also involved
in aggression and regulation of impulsivity (Rockland, 2000). Drugs that block serotonin tend to result in an increase in aggressive behavior.
The preceding description drastically oversimplifies the intricacies of constant neuronal communication. Such complexities make it difficult to understand what is happening in the normal brain when we are thinking, feeling, and interacting with our
environment. Many researchers seek to understand the normal information processes
of the brain by investigating what is going wrong in the brains of people affected by neurological and psychological disorders. In the case of depression, for example, in the early
1950s a drug (iproniazid, a monoamine oxidase inhibitor) intended to treat tuberculosis
was found to have a mood-improving effect. This finding led to some early research on
the chemical causes of depression. Perhaps if we can understand what has gone awry—
what chemicals are out of balance—we can figure out how processes normally work and
how to put things back into balance. One way of doing so might be by providing needed
neurotransmitters or by inhibiting the effects of overabundant neurotransmitters.
Receptors and Drugs
Receptors in the brain that normally are occupied by the standard neurotransmitters
can be hijacked by psychopharmacologically active drugs, legal or illegal. In such
cases, the molecules of the drugs enter into receptors that normally would be for
neurotransmitter substances endogenous in (originating in) the body.
When people stop using the drugs, withdrawal symptoms arise. Once a user has
formed narcotic dependence, for example, the form of treatment differs for acute toxicity (the damage done from a particular overdose) versus chronic toxicity (the damage
done by long-term drug addiction). Acute toxicity is often treated with naloxone or
Viewing the Structures and Functions of the Brain
65
related drugs. Naloxone (as well as a related drug, naltrexone) occupies opiate receptors in the brain better than the opiates themselves occupy those sites; thus, it blocks
all effects of narcotics. In fact, naloxone has such a strong affinity for the endorphin
receptors in the brain that it actually displaces molecules of narcotics already in
these receptors and then moves into the receptors. Naloxone is not addictive,
however. Even though it binds to receptors, it does not activate them. Although
naloxone can be a life-saving drug for someone who has overdosed on opiates, its
effects are short-lived. Thus, it is a poor long-term treatment for drug addiction.
In narcotic detoxification, methadone often is substituted for the narcotic (typically, heroin). Methadone binds to endorphin receptor sites in a similar way to naloxone and reduces the heroin cravings and withdrawal symptoms of addicted
persons. After the substitution, gradually decreasing dosages are administered to the
patient until he or she is drug-free. Unfortunately, the usefulness of methadone is
limited by the fact that it is addictive.
CONCEPT CHECK
1. Name some of the major structures in each part of the brain (forebrain, midbrain, and
hindbrain) and their functions.
2. What does localization of function mean?
3. Why do researchers believe that the brain exhibits some level of hemispheric specialization?
4. What are the four lobes of the brain and some of the functions associated with them?
5. How do neurons transmit information?
Viewing the Structures and Functions of the Brain
Scientists can use many methods for studying the human brain. These methods include
both postmortem (from Latin, “after death”) studies and in vivo (from Latin, “living”)
techniques on both humans and animals. Each technique provides important information about the structure and function of the human brain. Even some of the earliest
postmortem studies still influence our thinking about how the brain performs certain
functions. However, the recent trend is to focus on techniques that provide information about human mental functioning as it is occurring. This trend is in contrast to the
earlier trend of waiting to find people with disorders and then studying their brains after they died. Because postmortem studies are the foundation for later work, we discuss
them first. We then move on to the more modern in vivo techniques.
Postmortem Studies
Postmortem studies and the dissection of brains have been done for centuries. Even
today, researchers often use dissection to study the relation between the brain and
behavior. In the ideal case, studies start during the lifetime of a person. Researchers
observe and document the behavior of people who show signs of brain damage while
they are alive (Wilson, 2003). Later, after the patients die, the researchers examine
the patients’ brains for lesions—areas where body tissue has been damaged, such as
from injury or disease. Then the researchers infer that the lesioned locations may
be related to the behavior that was affected. The case of Phineas Gage, discussed
in Chapter 1, was explored through these methods.
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CHAPTER 2 • Cognitive Neuroscience
Through such investigations, researchers may be able to trace a link between an
observed type of behavior and anomalies in a particular location in the brain. An
early example is Paul Broca’s (1824–1880) famous patient, Tan (so named because
that was the only syllable he was capable of uttering). Tan had severe speech
problems. These problems were linked to lesions in an area of the frontal lobe
(Broca’s area). This area is involved in certain functions of speech production. In
more recent times, postmortem examinations of victims of Alzheimer’s disease (an
illness that causes devastating losses of memory; see Chapter 5) have led researchers
to identify some of the brain structures involved in memory (e.g., the hippocampus,
described earlier in this chapter). These examinations also have identified some of
the microscopic aberrations associated with the disease process (e.g., distinctive tangled fibers in the brain tissue). Although lesioning techniques provide the basic
foundation for understanding the relation of the brain to behavior, they are limited
in that they cannot be performed on the living brain. As a result, they do not offer
insights into more specific physiological processes of the brain. For this kind of information, we need to study live nonhuman animals.
Studying Live Nonhuman Animals
Scientists also want to understand the physiological processes and functions of the
living brain. To study the changing activity of the living brain, scientists must use
in vivo research. Many early in vivo techniques were performed exclusively on animals. For example, Nobel Prize–winning research on visual perception arose from in
vivo studies investigating the electrical activity of individual cells in particular regions of the brains of animals (Hubel & Wiesel, 1963, 1968, 1979; see Chapter 3).
To obtain single-cell recordings, researchers insert a very thin electrode next to a
single neuron in the brain of an animal (usually a monkey or a cat). They then record the changes in electrical activity that occur in the cell when the animal is exposed to a stimulus. In this way, scientists can measure the effects of certain kinds of
stimuli, such as visually presented lines, on the activity of individual neurons. Neurons fire constantly, even if no stimuli are present, so the task of the researcher is to
find stimuli that produce a consistent change in the activity of the neuron. This
technique can be used only in laboratory animals, not in humans, because no safe
way has yet been devised to perform such recordings in humans.
A second group of animal studies includes selective lesioning—surgically removing
or damaging part of the brain—to observe resulting functional deficits (Al’bertin,
Mulder, & Wiener, 2003; Mohammed, Jonsson, & Archer, 1986). In recent years,
researchers have found neurochemical ways to induce lesions in animals’ brains by administering drugs that destroy only cells that use a particular neurotransmitter. Some
drugs’ effects are reversible, so that conductivity in the brain is disrupted only for a
limited amount of time (Gazzaniga, Ivry, & Mangun, 2009).
A third way of doing research with animals is by employing genetic knockout
procedures. By using genetic manipulations, animals can be created that lack certain
kinds of cells or receptors in the brain. Comparisons with normal animals then indicate what the function of the missing receptors or cells may be.
Studying Live Humans
Obviously, many of the techniques used to study live animals cannot be used on human participants. Generalizations to humans based on these studies are therefore
Viewing the Structures and Functions of the Brain
67
somewhat limited. However, an array of less invasive imaging techniques for use
with humans has been developed. These techniques—electrical recordings, static
imaging, and metabolic imaging—are described in this section.
Electrical Recordings
The transmission of signals in the brain occurs through electrical potentials. When
recorded, this activity appears as waves of various widths (frequencies) and heights
(intensities). Electroencephalograms (EEGs) are recordings of the electrical frequencies and intensities of the living brain, typically recorded over relatively long
periods (Picton & Mazaheri, 2003). Through EEGs, it is possible to study brainwave activity indicative of changing mental states such as deep sleep or dreaming.
To obtain EEG recordings, electrodes are placed at various points along the surface
of the scalp. The electrical activity of underlying brain areas is then recorded. Therefore, the information is not localized to specific cells. However, the EEG is very sensitive to changes over time. For example, EEG recordings taken during sleep reveal
changing patterns of electrical activity involving the whole brain. Different patterns
emerge during dreaming versus deep sleep. EEGs are also used as a tool in the diagnosis of epilepsy because they can indicate whether seizures appear in both sides of
the brain at the same time, or whether they originate in one part of the brain and
then spread.
To relate electrical activity to a particular event or task (e.g., seeing a flash of
light or listening to sentences), EEG waves can be measured when participants are
exposed to a particular stimulus. An event-related potential (ERP) is the record of a
small change in the brain’s electrical activity in response to a stimulating event. The
fluctuation typically lasts a mere fraction of a second. ERPs provide very good information about the time-course of task-related brain activity. In any one EEG recording, there is a great deal of “noise”—that is, irrelevant electrical activity going on in
the brain. ERPs cancel out the effects of noise by averaging out activity that is not
task-related. Therefore, the EEG waves are averaged over a large number (e.g., 100)
of trials to reveal the event-related potentials (ERPs). The resulting wave forms
show characteristic spikes related to the timing of electrical activity, but they reveal
only very general information about the location of that activity (because of low
spatial resolution as a result of the placement of scalp electrodes).
The ERP technique has been used in a wide variety of studies. Some studies of
mental abilities like selective attention have investigated individual differences by
using event-related potentials (e.g., Troche et al., 2009). ERP methods are also
used to examine language processing. One study examined children who suffered
from developmental language impairment and compared them with those who did
not. The children were presented with pictures and a sound or word, and then had
to decide whether the picture, on the one hand, and the sound or word, on the
other, matched. For example, in a matching pair, a picture of a rooster would be
presented with either the sound “cockadoodledoo” or the spoken word “crowing.”
A mismatch would be the picture of the rooster presented with the sound “ding
dong” or the spoken word “chiming.” There was no difference between the two
groups when they had to match the picture with the sound. The children with language impairment had greater difficulty matching the picture with the spoken word
and exhibited a delayed N400 effect (the N400 is a component of ERPs that occurs
especially when people are presented with meaningful stimuli). The results confirmed the hypothesis that the language networks of the children with language
impairment may be weakened (Cummings & Ceponiene, 2010).
CHAPTER 2 • Cognitive Neuroscience
ERP can be used to examine developmental changes in cognitive abilities.
These experiments provide a more complete understanding of the relationship
between brain and cognitive development (Taylor & Baldeweg, 2002).
The high degree of temporal resolution afforded by ERPs can be used to complement other techniques. For example, ERPs and positron emission tomography (PET)
were used to pinpoint areas involved in word association (Posner & Raichle, 1994).
Using ERPs, the investigators found that participants showed increased activity in
certain parts of the brain (left lateral frontal cortex, left posterior cortex, and right
insular cortex) when they made rapid associations to given words. Another study
showed that decreases in electrical potentials are twice as great for tones that are
attended to as for tones that are ignored (see Phelps, 1999). As with any technique,
EEGs and ERPs provide only a glimpse of brain activity. They are most helpful when
used in conjunction with other techniques to identify particular brain areas involved
in cognition.
Static Imaging Techniques
Psychologists use still images to reveal the structures of the brain (see Figure 2.10
and Table 2.3). The techniques include angiograms, computed tomography (CT)
scans, and magnetic resonance imaging scans (MRI). The X-ray–based techniques
(angiogram and CT scan) allow for the observation of large abnormalities of the
brain, such as damage resulting from strokes or tumors. However, they are limited
(a) Brain angiogram: A brain angiogram highlights the blood vessels of the brain.
(b) CT scan: A CT image of a brain uses a series of rotating scans to produce a three-dimensional
view of brain structures.
3
2
1
1
2
Detectors
3
Moving
X-ray
source
Figure 2.10 Brain Imaging Techniques.
Various techniques have been developed to picture the structures—and sometimes the
processes—of the brain.
Angiogram © CNRI/SPL/Photo Researchers, Inc. CT scan © Ohio Nuclear/SPL/Photo Researchers, Inc.
68
Viewing the Structures and Functions of the Brain
69
in their resolution and cannot provide much information about smaller lesions and
aberrations.
Computed tomography (CT or CAT). Unlike conventional X-ray methods that
only allow a two-dimensional view of an object, a CT scan consists of several
X-ray images of the brain taken from different vantage points that, when combined,
result in a three-dimensional image.
The aim of an angiography is not to look at the structures in the brain, but rather
to examine the blood flow. When the brain is active, it needs energy, which is
transported to the brain in the form of oxygen and glucose by means of the blood.
In angiography, a dye is injected into an artery that leads to the brain, and then
an X-ray image is taken. The image shows the circulatory system, and it is possible
(c) MRI scan: A rotating series of MRI scans shows a clearer three-dimensional picture of brain
structures than CT scans show.
Coil
Magnetic
rings
(d) PET scan: These still photographs of PET scans of a brain show different metabolic processes
during different activities. PET scans permit the study of brain physiology.
(e) TMS (Transcranial magnetic stimulation): TMS temporarily disrupts normal brain activity to
investigate cognitive functioning when particular areas are disrupted.
Coil with
electric
current
Figure 2.10
Continued
MRI © CNRI/SPL/Photo Researchers, Inc. PET scan © Simon Fraser/University of Durham/Photo Researchers, Inc.
Detectors
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CHAPTER 2 • Cognitive Neuroscience
Table 2.3
Cognitive Neuropsychological Methods for Studying Brain Functioning
Suitable for
Humans?
Method
Procedure
Advantages
Disadvantages
Single-cell
recording
Very thin electrode is inserted next to a
single neuron. Changes in electrical activity occurring in the cell are then recorded.
No
Rather precise recording of electrical activity
Cannot be used
with humans
EEG
Changes in electrical potentials are recorded via electrodes attached to scalp.
Yes
Relatively
noninvasive
Imprecise
ERP
Changes in electrical potentials are recorded via electrodes attached to scalp.
Yes
Relatively
noninvasive
Does not show actual brain images
PET
Participants ingest a mildly radioactive
form of oxygen that emits positrons as it is
metabolized. Changes in concentration of
positrons in targeted areas of the brain are
then measured.
Yes
Shows images of
the brain in action
Less useful for fast
processes
fMRI
Creates magnetic field that induces
changes in the particles of oxygen atoms.
More active areas draw more oxygenated
blood than do less active areas in the
brain. The differences in the amounts of
oxygen consumed form the basis for fMRI
measurements.
Yes
Shows images of
the brain in action;
more precise than
PET
Requires individual
to be placed in uncomfortable scanner for some time
TMS
Involves placing a coil on a person’s head
and then allowing an electrical current to
pass through it. The current generates a
magnetic field. This field disrupts the small
area (usually no more than a cubic centimeter) beneath it. The researcher can then
look at cognitive functioning when the
particular area is disrupted.
Yes
Enables researcher
to pinpoint how
disruption of a particular area of brain
affects cognitive
functioning
Potentially dangerous if misused
MEG
Involves measuring brain activity through
detection of magnetic fields by placing a
device over the head.
Yes
Extremely precise
spatial and temporal resolution
Requires expensive
machine not readily available to
researchers
to detect strokes (disruption of the blood flow often caused by the blockage of
the arteries through a foreign substance) or aneurysms (abnormal ballooning of an
artery), or arteriosclerosis (a hardening of arteries that makes them inflexible and
narrow).
The magnetic resonance imaging (MRI) scan is of great interest to cognitive
psychologists (Figure 2.11). The MRI reveals high-resolution images of the structure
of the living brain by computing and analyzing magnetic changes in the energy of
the orbits of nuclear particles in the molecules of the body. There are two kinds of
71
Scott Hirko/iStockphoto.com
Viewing the Structures and Functions of the Brain
Figure 2.11
Magnetic Resonance Imaging (MRI).
An MRI machine can provide data that show what areas of the brain are involved in different
kinds of cognitive processing.
MRIs—structural MRIs and functional MRIs. Structural MRIs provide images of the
brain’s size and shape whereas functional MRIs visualize the parts of the brain that
are activated when a person is engaged in a particular task. MRIs allow for a much
clearer picture of the brain than CT scans. A strong magnetic field is passed through
the brain of a patient. A scanner detects various patterns of electromagnetic changes
in the atoms of the brain. These molecular changes are analyzed by a computer to
produce a three-dimensional picture of the brain. This picture includes detailed
information about brain structures. For example, MRI has been used to show that
musicians who play string instruments such as the violin or the cello tend to have
an expansion of the brain in an area of the right hemisphere that controls left-hand
movement (because control of hands is contralateral, with the right side of the brain
controlling the left hand, and vice versa; Münte, Altenmüller, & Jäncke, 2002). We
tend to view the brain as controlling what we can do. This study is a good example
of how what we do—our experience—can affect the development of the brain. MRI
also facilitates the detection of lesions, such as lesions associated with particular disorders of language use, but does not provide much information about physiological
processes. However, the two techniques discussed in the following section do provide such information.
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CHAPTER 2 • Cognitive Neuroscience
Metabolic Imaging
Metabolic imaging techniques rely on changes that take place within the brain as
a result of increased consumption of glucose and oxygen in active areas of the
brain. The basic idea is that active areas in the brain consume more glucose and
oxygen than do inactive areas during some tasks. An area specifically required by
one task ought to be more active during that task than during more generalized
processing and thus should require more glucose and oxygen. Scientists attempt
to pinpoint specialized areas for a task by using the subtraction method. This
method uses two different measurements: one that was taken while the subject
was involved in a more general or control activity, and one that was taken when
the subject was engaged in the task of interest. The difference between these two
measurements equals the additional activation recorded while the subject is engaged in the target task as opposed to the control task. The subtraction method
thus involves subtracting activity during the control task from activity during the
task of interest. The resulting difference in activity is analyzed statistically. This analysis determines which areas are responsible for performance of a particular task above
and beyond the more general activity. For example, suppose the experimenter wishes
to determine which area of the brain is most important for retrieval of word meanings.
The experimenter might subtract activity during a task involving reading of words
from activity during a task involving the physical recognition of the letters of the
words. The difference in activity would be presumed to reflect the additional resources
used in retrieval of meaning.
There is one important caveat to remember about these techniques: Scientists
have no way of determining whether the net effect of this difference in activity is
excitatory or inhibitory (because some neurons are activated by, and some are inhibited by, other neurons’ neurotransmitters). Therefore, the subtraction technique
reveals net brain activity for particular areas. It cannot show whether the area’s effect is positive or negative. Moreover, the method assumes that activation is purely
additive—that it can be discovered through a subtraction method without taking
into account interactions among elements.
This description greatly oversimplifies the subtraction method. But it shows at a
general level how scientists assess physiological functioning of particular areas using
imaging techniques.
Positron emission tomography (PET) scans measure increases in oxygen consumption in active brain areas during particular kinds of information processing
(O’Leary et al., 2007; Raichle, 1998, 1999). To track their use of oxygen, participants are given a mildly radioactive form of oxygen that emits positrons as it is metabolized (positrons are particles that have roughly the same size and mass as
electrons, but that are positively rather than negatively charged). Next, the brain is
scanned to detect positrons. A computer analyzes the data to produce images of the
physiological functioning of the brain in action.
PET scans can assist in the diagnosis of disorders of cognitive decline like
Alzheimer’s by searching for abnormalities in the brain (Patterson et al., 2009).
PET scans have been used to show that blood flow increases to the occipital
lobe of the brain during visual processing (Posner et al., 1988). PET scans also
are used for comparatively studying the brains of people who score high versus
low on intelligence tests. When high-scoring people are engaged in cognitively
demanding tasks, their brains seem to use glucose more efficiently—in highly
Viewing the Structures and Functions of the Brain
73
task-specific areas of the brain. The brains of people with lower scores appear to
use glucose more diffusely, across larger regions of the brain (Haier et al., 1992).
Likewise, a study has shown that Broca’s area as well as the left anterior temporal
area and the cerebellum are involved in the learning of new words (Groenholm et
al., 2005).
PET scans have been used to illustrate the integration of information from
various parts of the cortex (Castelli et al, 2005; Posner et al., 1988). Specifically,
PET scans were used to study regional cerebral blood flow during several activities
involving the reading of single words. When participants looked at a word on a
screen, areas of their visual cortex showed high levels of activity. When they
spoke a word, their motor cortex was highly active. When they heard a word
spoken, their auditory cortex was activated. When they produced words related
to the words they saw (requiring high-level integration of visual, auditory, and
motor information), the relevant areas of the cortex showed the greatest amount
of activity.
PET scans are not highly precise because they require a minimum of about half
a minute to produce data regarding glucose consumption. If an area of the brain
shows different amounts of activity over the course of time measurement, the
activity levels are averaged, potentially leading to conclusions that are less than
precise.
Functional magnetic resonance imaging (fMRI) is a neuroimaging technique
that uses magnetic fields to construct a detailed representation in three dimensions
of levels of activity in various parts of the brain at a given moment in time. This
technique builds on MRI, but it uses increases in oxygen consumption to construct
images of brain activity. The basic idea is the same as in PET scans. However,
the fMRI technique does not require the use of radioactive particles. Rather, the
participant performs a task while placed inside an MRI machine. This machine typically looks like a tunnel. When someone is wholly or partially inserted in the tunnel, he or she is surrounded by a donut-shaped magnet. Functional MRI creates a
magnetic field that induces changes in the particles of oxygen atoms. More active
areas draw more oxygenated blood than do less active areas in the brain. So shortly
after a brain area has been active, a reduced amount of oxygen should be detectable
in this area. This observation forms the basis for fMRI measurements. These measurements then are computer analyzed to provide the most precise information currently available about the physiological functioning of the brain’s activity during
task performance.
This technique is less invasive than PET. It also has higher temporal resolution—
measurements can be taken for activity lasting fractions of a second, rather than only
for activity lasting minutes to hours. One major drawback is the expense of fMRI.
Relatively few researchers have access to the required machinery and testing of participants is very time consuming.
The fMRI technique can identify regions of the brain active in many areas, such
as vision (Engel et al., 1994; Kitada et al., 2010), attention (Cohen et al.; 1994;
Samanez-Larkin et al., 2009), language (Gaillard et al., 2003; Stein et al., 2009),
and memory (Gabrieli et al., 1996; Wolf, 2009). For example, fMRI has shown
that the lateral prefrontal cortex is essential for working memory. This is a part of
memory that processes information that is actively in use at a given time (McCarthy
et al., 1994). Also, fMRI methods have been applied to the examination of brain
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CHAPTER 2 • Cognitive Neuroscience
changes in patient populations, including persons with schizophrenia and epilepsy
(Detre, 2004; Weinberger et al., 1996).
A related procedure is pharmacological MRI (phMRI). The phMRI combines
fMRI methods with the study of psychopharmacological agents. These studies examine the influence and role of particular psychopharmacological agents on the brain.
They have allowed the examination of the role of agonists (which strengthen
responses) and antagonists (which weaken responses) on the same receptor cells.
These studies have allowed for the examination of drugs used for treatment. The
investigators can predict the responses of patients to neurochemical treatments
through examination of the person’s brain makeup. Overall, these methods aid in
the understanding of brain areas and the effects of psychopharmacological agents
on brain functioning (Baliki et al., 2005; Easton et al., 2007; Honey & Bullmore,
2004; Kalisch et al., 2004).
Another procedure related to fMRI is diffusion tensor imaging (DTI). Diffusion
tensor imaging examines the restricted dispersion of water in tissue and, of special
interest, in axons. Water in the brain cannot move freely, but rather, its movement
is restricted by the axons and their myelin sheaths. DTI measures how far protons
have moved in a particular direction within a specific time interval. This technique
has been useful in the mapping of the white matter of the brain and in examining
neural circuits. Some applications of this technique include examination of traumatic brain injury, schizophrenia, brain maturation, and multiple sclerosis (Ardekani
et al., 2003; Beyer, Ranga, & Krishnan, 2002; Ramachandra et al., 2003; Sotak,
2002; Sundgren et al., 2004).
A recently developed technique for studying brain activity bypasses some of
the problems with other techniques (Walsh & Pascual-Leone, 2005). Transcranial magnetic stimulation (TMS) temporarily disrupts the normal activity of the
brain in a limited area. Therefore, it can imitate lesions in the brain or stimulate
brain regions. TMS requires placing a coil on a person’s head and then allowing
an electrical current to pass through it (Figure 2.10). The current generates a
magnetic field. This field disrupts the small area (usually no more than a cubic
centimeter) beneath it. The researcher can then look at cognitive functioning
when the particular area is disrupted. This method is restricted to brain regions
that lie close to the surface of the head. An advantage to TMS is that it is possible to examine causal relationships with this method because the brain activity in
a particular area is disrupted and then its influence on task-performance is observed; most other methods allow the investigator to examine only correlational
relationships by the observation of brain function (Gazzaniga, Ivry, & Mangun,
2009). TMS has been used, for example, to produce “virtual lesions” and investigate which areas of the brain are involved when people grasp or reach for an object (Koch & Rothwell, 2009). It is even hypothesized that repeated magnetic
impulses (rTMS) can serve as a therapeutic means in the treatment of neuropsychological disorders like depression or anxiety disorders (Pallanti & Bernardi,
2009).
Magnetoencephalography (MEG) measures activity of the brain from outside
the head (similar to EEG) by picking up magnetic fields emitted by changes in
brain activity. This technique allows localization of brain signals so that it is possible to know what different parts of the brain are doing at different times. It is one
of the most precise of the measuring methods. MEG is used to help surgeons locate
pathological structures in the brain (Baumgartner, 2000). A recent application of
Brain Disorders
75
MEG involved patients who reported phantom limb pain. In cases of phantom
limb pain, a patient reports pain in a body part that has been removed, for example, a missing foot. When certain areas of the brain are stimulated, phantom limb
pain is reduced. MEG has been used to examine the changes in brain activity
before, during, and after electrical stimulation. These changes in brain activity
corresponded with changes in the experience of phantom limb pain (Kringelbach
et al., 2007).
Current techniques still do not provide unambiguous mappings of particular
functions to particular brain structures, regions, or even processes. Rather, some discrete structures, regions, or processes of the brain appear to be involved in particular
cognitive functions. Our current understanding of how particular cognitive functions
are linked to particular brain structures or processes allows us only to infer suggestive
indications of some kind of relationship. Through sophisticated analyses, we can infer increasingly precise relationships. But we are not yet at a point where we can
determine the specific cause–effect relationship between a given brain structure or
process and a particular cognitive function because particular functions may be influenced by multiple structures, regions, or processes of the brain. Finally, these techniques provide the best information only in conjunction with other experimental
techniques for understanding the complexities of cognitive functioning. These combinations generally are completed with human participants, although some researchers have combined in vivo studies in animals with brain-imaging techniques
(Dedeogle et al., 2004; Kornblum et al., 2000; Logothetis, 2004).
CONCEPT CHECK
1. In the investigation of the structure and functions of the brain, what methods of study
can be used only in nonhuman animals, and what methods can be used in humans?
2. What are typical questions that are investigated with EEGs, PETs, and fMRIs?
3. Why is it useful to have imaging methods that display the metabolism of the brain?
4. What are the advantages and disadvantages of in vivo techniques compared to
postmortem studies?
Brain Disorders
A number of brain disorders can impair cognitive functioning. Brain disorders can
give us valuable insight into the functioning of the brain. As mentioned above,
scientists often write detailed notes about the condition of a patient and analyze
the brain of a patient once the patient has died to see which areas in the brain
may have caused the symptoms the patient experienced. Furthermore, with the in
vivo techniques that have been developed over the past decades, many tests and diagnostic procedures can be executed during the lifetime of a patient to help ease
patient symptoms and to gain new insight into how the brain works.
Stroke
Vascular disorder is a brain disorder caused by a stroke. Strokes occur when the flow
of blood to the brain undergoes a sudden disruption. People who experience stroke
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CHAPTER 2 • Cognitive Neuroscience
typically show marked loss of cognitive functioning. The nature of the loss depends
on the area of the brain that is affected by the stroke. There may be paralysis, pain,
numbness, a loss of speech, a loss of language comprehension, impairments in
thought processes, a loss of movement in parts of the body, or other symptoms.
Two kinds of stroke may occur (NINDS stroke information page, 2009). An ischemic stroke usually occurs when a buildup of fatty tissue occurs in blood vessels over a
period of years, and a piece of this tissue breaks off and gets lodged in arteries of the
brain. Ischemic strokes can be treated by clot-busting drugs. The second kind of
stroke, a hemorrhagic stroke, occurs when a blood vessel in the brain suddenly breaks.
Blood then spills into surrounding tissue. As the blood spills over, brain cells in the
affected areas begin to die. This death is either from the lack of oxygen and nutrients or from the rupture of the vessel and the sudden spilling of blood. The prognosis for stroke victims depends on the type and severity of damage. Symptoms of
stroke appear immediately on the occurrence of stroke.
Typical symptoms include (NINDS stroke information page, 2009):
• numbness or weakness in the face, arms, or legs (especially on one side of the
body)
• confusion, difficulty speaking or understanding speech
• vision disturbances in one or both eyes
• dizziness, trouble walking, loss of balance or coordination
• severe headache with no known cause
Brain Tumors
Brain tumors, also called neoplasms, can affect cognitive functioning in very serious
ways. Tumors can occur in either the gray or the white matter of the brain. Tumors
of the white matter are more common (Gazzaniga, Ivry, & Mangun, 2009).
Two types of brain tumors can occur. Primary brain tumors start in the brain.
Most childhood brain tumors are of this type. Secondary brain tumors start as tumors
somewhere else in the body, such as in the lungs. Brain tumors can be either benign
or malignant. Benign tumors do not contain cancer cells. They typically can be removed and will not grow back. Cells from benign tumors do not invade surrounding
cells or spread to other parts of the body. However, if they press against sensitive
areas of the brain, they can result in serious cognitive impairments. They also can be
life-threatening, unlike benign tumors in most other parts of the body. Malignant
brain tumors, unlike benign ones, contain cancer cells. They are more serious and
usually threaten the victim’s life. They often grow quickly. They also tend to invade
surrounding healthy brain tissue. In rare instances, malignant cells may break away
and cause cancer in other parts of the body. Following are the most common symptoms of brain tumors (What you need to know about brain tumors, 2009):
•
•
•
•
•
•
•
•
headaches (usually worse in the morning)
nausea or vomiting
changes in speech, vision, or hearing
problems balancing or walking
changes in mood, personality, or ability to concentrate
problems with memory
muscle jerking or twitching (seizures or convulsions)
numbness or tingling in the arms or legs
Brain Disorders
77
n BELIEVE IT OR NOT
BRAIN SURGERY CAN BE PERFORMED
WHILE YOU ARE AWAKE!
Can you imagine having major surgery performed on you
while you are awake? It’s possible, and indeed sometimes it is done. When patients who have brain tumors
or who suffer from epilepsy receive brain surgery, they are
often woken up from the anesthesia after the surgeons
have opened their skull and exposed the brain. This
way the surgeons can talk to the patient and perform tests
by stimulating the patient’s brain in order to map the different areas of the brain that control important functions
like vision or memory. The brain itself does not contain
any pain receptors, and when doctors stimulate a patient’s brain during open-brain surgery while the patient
is awake, the patient does not feel any pain. You can
nevertheless get a headache, but that is because the tissue
and nerves that surround the brain are sensitive to pain,
not the brain itself. The communication with the patient
enhances the safety and precision of the procedure as
compared with brain surgery that is performed solely on
the basis of brain scans that were performed using imaging technologies discussed in this chapter.
The diagnosis of brain tumor is typically made through neurological examination, CT scan, and/or MRI. The most common form of treatment is a combination
of surgery, radiation, and chemotherapy.
Head Injuries
Head injuries result from many causes, such as a car accident, contact with a hard
object, or a bullet wound. Head injuries are of two types. In closed-head injuries, the
skull remains intact but there is damage to the brain, typically from the mechanical
force of a blow to the head. Slamming one’s head against a windshield in a car accident might result in such an injury. In open-head injuries, the skull does not remain
intact but rather is penetrated, for example, by a bullet.
Head injuries are surprisingly common. Roughly 1.4 million North Americans
suffer such injuries each year. About 50,000 of them die, and 235,000 need to be
hospitalized. About 2% of the American population needs long-term assistance in
their daily living due to head injuries (What is traumatic brain injury, 2009).
Loss of consciousness is a sign that there has been some degree of damage to
the brain as a result of the injury. Damage resulting from head injury can include
spastic movements, difficulty in swallowing, and slurring of speech, among many
other cognitive problems. Immediate symptoms of a head injury include (Signs and
symptoms, 2009):
•
•
•
•
•
•
•
•
•
•
•
•
unconsciousness
abnormal breathing
obvious serious wound or fracture
bleeding or clear fluid from the nose, ear, or mouth
disturbance of speech or vision
pupils of unequal size
weakness or paralysis
dizziness
neck pain or stiffness
seizure
vomiting more than two to three times
loss of bladder or bowel control
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CHAPTER 2 • Cognitive Neuroscience
Generally, brain damage can result from many causes. When brain damage occurs,
it always should be treated by a medical specialist at the earliest possible time. A neuropsychologist may be called in to assist in diagnosis, and rehabilitation psychologists
can be helpful in bringing the patient to the optimal level of psychological functioning possible under the circumstances.
CONCEPT CHECK
1. Why is the study of brain disorders useful for cognitive psychologists?
2. What are brain tumors, and how are they diagnosed?
3. What are the causes of strokes?
4. What are the symptoms of head injuries?
Intelligence and Neuroscience
The human brain is clearly the organ that serves as a biological basis for human intelligence. Early studies, such as those of Karl Lashley, studied the brain to find biological indices of intelligence and other aspects of mental processes. They were a
resounding failure, despite great efforts. As tools for studying the brain have become
more sophisticated, however, we are beginning to see the possibility of finding physiological indicators of intelligence. Some investigators believe that at some point
we will have clinically useful psychophysiological indices of intelligence
(e.g., Matarazzo, 1992). But widely applicable indices will be much longer in coming. In the meantime, the biological studies we now have are largely correlational.
They show statistical associations between biological and psychometric or other
measures of intelligence. They do not establish causal relations.
Intelligence and Brain Size
One line of research looks at the relationship of brain size or volume to intelligence
(see Jerison, 2000; Vernon et al., 2000; Witelson, Beresh, & Kiga, 2006). The evidence suggests that, for humans, there is a modest but significant statistical relationship between brain size and intelligence (Gignac, Vernon, & Wickett, 2003;
McDaniel, 2005). The amount of gray matter in the brain is strongly correlated
with IQ in many areas of the frontal and temporal lobes (Haier, Jung, Yeo, Head,
& Alkire, 2004). However, the brain areas that are correlated with IQ appear to
differ in men versus women. Frontal areas are of relatively more importance in
women, whereas posterior areas are of relatively more importance in men, even if
both genders are matched for intelligence (Haier, Jung, Yeo, Head, & Alkire,
2005). This finding opens the question of whether there are two different brain
architectures in men versus women that both result in roughly equal levels of intelligence (Haier, 2010). It is important to note that the relationship between brain
size and intelligence does not hold across species (Jerison, 2000). Rather, what holds
seems to be a relationship between intelligence and brain size, relative to the rough
general size of the organism.
Intelligence and Neuroscience
79
Intelligence and Neurons
The development of electrical recording and imaging techniques offers some appealing possibilities. For example, complex patterns of electrical activity in the brain,
which are prompted by specific stimuli, appear to correlate with scores on IQ tests
(Barrett & Eysenck, 1992). Several studies initially suggested that speed of conduction of neural impulses may correlate with intelligence, as measured by IQ tests
(McGarry-Roberts, Stelmack, & Campbell, 1992; Vernon & Mori, 1992). A
follow-up study, however, failed to find a strong relation between neural-conduction
velocity and intelligence (Wickett & Vernon, 1994). In this study, conduction
velocity was measured by neural-conduction speeds in a main nerve of the arm. Intelligence was measured by a Multidimensional Aptitude Battery. Surprisingly, neuralconduction velocity appears to be a more powerful predictor of IQ scores for men
than for women. So gender differences may account for some of the differences
in the data (Wickett & Vernon, 1994). As of now, the results are inconsistent
(Haier, 2010).
Intelligence and Brain Metabolism
More recent work suggests that the flexibility of neural circuitry, rather than speed of
conduction, is key (Newman & Just, 2005). Hence, we would want to study not just
speed but neural circuitry. An alternative approach to studying the brain suggests
that neural efficiency may be related to intelligence. Such an approach is based on
studies of how the brain metabolizes glucose (a simple sugar required for brain activity) during mental activities. Higher intelligence correlates with reduced levels of
glucose metabolism during problem-solving tasks (Haier et al., 1992; Haier & Jung,
2007). That is, smarter brains consume less sugar and therefore expend less effort
than less smart brains doing the same task. Furthermore, cerebral efficiency increases
as a result of learning on a relatively complex task involving visuospatial manipulations, for example, the computer game Tetris (Haier et al., 1992). As a result of
practice, more intelligent participants not only show lower cerebral glucose metabolism overall but also show more specifically localized metabolism of glucose. In most
areas of their brains, smarter participants show less glucose metabolism. But in selected areas of their brains, believed to be important to the task at hand, they
show higher levels of glucose metabolism. Thus, more intelligent participants may
have learned how to use their brains more efficiently. They carefully focus their
thought processes on a given task.
Other research, however, suggests that the relationship between glucose metabolism and intelligence may be more complex (Haier et al., 1995; Larson et al.,
1995). On the one hand, one study confirmed the earlier findings of increased glucose metabolism in less smart participants, in this case, participants who had mild
mental retardation (Haier et al., 1995). On the other hand, another study found,
contrary to the earlier findings, that smarter participants had increased glucose metabolism relative to their average comparison group (Larson et al., 1995).
There was a problem with earlier studies—the tasks participants received were
not matched for difficulty level across groups of smart and average individuals. The
study by Larson and colleagues used tasks that were matched to the ability levels of
the smarter and average participants. They found that the smarter participants used
more glucose. Moreover, the glucose metabolism was highest in the right hemisphere
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CHAPTER 2 • Cognitive Neuroscience
of the more intelligent participants performing the hard task. These results again
suggest selectivity of brain areas. What could be driving the increases in glucose
metabolism? Currently, the key factor appears to be subjective task difficulty. In
earlier studies, smarter participants simply found the tasks to be too easy. Matching
task difficulty to participants’ abilities seems to indicate that smarter participants increase glucose metabolism when the task demands it. The preliminary findings in
this area will need to be investigated further before any conclusive answers arise.
Biological Bases of Intelligence Testing
Some neuropsychological research suggests that performance on intelligence tests
may not indicate a crucial aspect of intelligence—the ability to set goals, to plan
how to meet them, and to execute those plans (Dempster, 1991). Specifically, people with lesions on the frontal lobe of the brain frequently perform quite well on
standardized IQ tests. These tests require responses to questions within a highly
structured situation. But they do not require much in the way of goal setting or planning. These tests frequently use what could be classified as crystallized intelligence.
Damage to the posterior regions of the brain seems to have negative effects on measures of crystallized intelligence (Gray & Thompson, 2004; Kolb & Whishaw, 1996;
Piercy, 1964). In patients with frontal lobe damage, impairments in fluid intelligence
are observed (Duncan, Burgess, & Emslie, 1995; Gray, Chabris, & Braver, 2003;
Gray & Thompson, 2004). This result should come as no surprise, given that the
frontal lobes are involved in reasoning, decision making, and problem solving (see
Chapters 11 and 12). Other research highlights the importance of the parietal
regions for performance on general and fluid intelligence tasks (Lee et al., 2006;
see also Glaescher et al., 2009). Intelligence involves the ability to learn from experience and to adapt to the surrounding environment. Thus, the ability to set goals
and to design and implement plans cannot be ignored. An essential aspect of goal
setting and planning is the ability to attend appropriately to relevant stimuli.
Another related ability is that of ignoring or discounting irrelevant stimuli.
The P-FIT Theory of Intelligence
The discovered importance of the frontal and parietal regions in intelligence tasks has
led to the development of an integrated theory of intelligence that highlights the
importance of these areas. This theory, called the parietal-frontal integration theory
(P-FIT), stresses the importance of interconnected brain regions in determining
differences in intelligence. The regions this theory focuses on are the prefrontal
cortex, the inferior and superior parietal lobe, the anterior cingulated cortex, and
portions of the temporal and occipital lobes (Colom et al., 2009; Jung & Haier, 2007).
P-FIT theory describes patterns of brain activity in people with different levels of
intelligence; it cannot, however, explain what makes a person intelligent or what intelligence is.
We cannot realistically study a brain or its contents and processes in isolation
without also considering the entire human being. We must consider the interactions
of that human being with the entire environmental context within which the person acts intelligently. Many researchers and theorists urge us to take a more contextual view of intelligence. Furthermore, some alternative views of intelligence
attempt to broaden the definition of intelligence to be more inclusive of people’s
varied abilities.
Summary
81
CONCEPT CHECK
1. Is there a relationship between brain size and intelligence?
2. Why does higher intelligence in many instances correlate with reduced levels of
glucose metabolism during problem-solving tasks?
3. What is the P-FIT theory of intelligence?
Key Themes
In Chapter 1, we reviewed seven key themes that pervade cognitive psychology.
Several of them are relevant here.
Biological versus behavioral methods. The mechanisms and methods described
in this chapter are primarily biological. And yet, a major goal of biological researchers is to discover how cognition and behavior relate to these biological mechanisms.
For example, they study how the hippocampus enables learning. Thus, biology, cognition, and behavior work together. They are not in any way mutually exclusive.
Nature versus nurture. One comes into the world with many biological structures and mechanisms in place. But nurture acts to develop them and enable them
to reach their potential. The existence of the cerebral cortex is a result of nature, but
the memories stored in it derive from nurture. As stated in Chapter 1, nature does
not act alone. Rather, its marvels unfold through the interventions of nurture.
Applied versus basic research. Much of the research in biological approaches to
cognition is basic. But this basic research later enables us, as cognitive psychologists,
to make applied discoveries. For example, to understand how to treat and, hopefully,
help individuals with brain damage, cognitive neuropsychologists first must understand the nature of the damage and its pervasiveness. Many modern antidepressants,
for example, affect the reuptake of serotonin in the nervous system. By inhibiting
reuptake, they increase serotonin concentrations and ultimately increase feelings of
well-being. Interestingly, applied research can help basic research as much as basic
research can help applied research. In the case of antidepressants, scientists knew
the drugs worked before they knew exactly how they worked. Applied research in
creating the drugs helped the scientists understand the biological mechanisms underlying the success of the drugs in relieving symptoms of depression.
Summary
1. What are the fundamental structures and processes within the brain? The nervous system,
governed by the brain, is divided into two main
parts: the central nervous system, consisting of
the brain and the spinal cord, and the peripheral nervous system, consisting of the rest of the
nervous system (e.g., the nerves in the face,
legs, arms, and viscera).
2. How do researchers study the major structures and processes of the brain? For centuries
scientists have viewed the brain by dissecting it.
Modern dissection techniques include the use
of electron microscopes and sophisticated
chemical analyses to probe the mysteries of
individual cells of the brain. Additionally, surgical techniques on animals (e.g., the use of
selective lesioning and single-cell recording) often are used. On humans, studies have included
electrical analyses (e.g., electroencephalograms
and event-related potentials), studies based on
the use of X-ray techniques (e.g., angiograms
and computed tomograms), studies based on
computer analyses of magnetic fields within
the brain (magnetic resonance imaging), and
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CHAPTER 2 • Cognitive Neuroscience
studies based on computer analyses of blood
flow and metabolism within the brain (positron
emission tomography and functional magnetic
resonance imaging).
3. What have researchers found as a result of
studying the brain? The major structures of
the brain may be categorized as those in the
forebrain (e.g., the all-important cerebral cortex
and the thalamus, the hypothalamus, and the
limbic system, including the hippocampus), the
midbrain (including a portion of the brainstem), and the hindbrain (including the
medulla oblongata, the pons, and the cerebellum). The highly convoluted cerebral cortex
surrounds the interior of the brain and is the
basis for much of human cognition. The cortex
covers the left and right hemispheres of the
brain. They are connected by the corpus callosum. In general, each hemisphere contralaterally controls the opposite side of the body.
Based on extensive split-brain research, many
investigators believe that the two hemispheres
are specialized: In most people, the left hemisphere primarily controls language. The right
hemisphere primarily controls visuospatial processing. The two hemispheres also may process
information differently.
Another way to view the cortex is to identify
differences among four lobes. Roughly speaking,
higher thought and motor processing occur in
the frontal lobe. Somatosensory processing occurs in the parietal lobe. Auditory processing
occurs in the temporal lobe, and visual processing occurs in the occipital lobe. Within the
frontal lobe, the primary motor cortex controls
the planning, control, and execution of movement. Within the parietal lobe, the primary somatosensory cortex is responsible for sensations
in our muscles and skin. Specific regions of
these two cortices can be mapped to particular
regions of the body.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. How have views of the nature of the relation
between brain and cognition changed over
time?
2. Briefly summarize the main structures and
functions of the brain.
3. What are some of the reasons that researchers
are interested in finding out the localization of
function in the human brain?
4. In your opinion, why have the hindbrain, the
midbrain, and the forebrain evolved (across the
human species) and developed (across human
prenatal development) in the sequence mentioned in this chapter? Include the main
functions of each in your comments.
5. Researchers already are aware that a deficit of a
neurotransmitter, acetylcholine, in the hippocampus is linked to Alzheimer’s disease. Given
the difficulty of reaching the hippocampus
without causing other kinds of brain damage,
how might researchers try to treat Alzheimer’s
disease?
6. In your opinion, why is it that some discoveries,
such as that of Marc Dax, go unnoticed? What
can be done to maximize the possibility that key
discoveries will be noticed?
7. Given the functions of each of the cortical
lobes, how might a lesion in one of the lobes be
discovered?
8. What is an area of cognition that could be
studied effectively by viewing the structure or
function of the human brain? Describe how
a researcher might use one of the techniques
mentioned in this chapter to study that area
of cognition.
Key Terms
amygdala, p. 46
axon, p. 61
brain, p. 42
brainstem, p. 50
cerebellum, p. 51
cerebral cortex, p. 51
cerebral hemispheres, p. 52
cognitive neuroscience, p. 42
contralateral, p. 52
corpus callosum, p. 52
dendrites, p. 61
electroencephalograms (EEGs), p. 67
Media Resources
event-related potential (ERP),
p. 67
frontal lobe, p. 56
functional magnetic
resonance imaging (fMRI),
p. 73
hippocampus, p. 46
hypothalamus, p. 48
ipsilateral, p. 52
Korsakoff’s syndrome, p. 46
limbic system, p. 46
lobes, p. 56
localization of function, p. 43
magnetic resonance imaging
(MRI), p. 70
magnetoencephalography
(MEG), p. 74
medulla oblongata, p. 50
myelin, p. 61
nervous system, p. 43
neurons, p. 61
neurotransmitters, p. 62
nodes of Ranvier, p. 62
occipital lobe, p. 57
parietal lobe, p. 56
pons, p. 51
positron emission tomography
(PET), p. 72
primary motor cortex, p. 57
primary somatosensory cortex,
p. 58
reticular activating system (RAS),
p. 48
septum, p. 46
soma, p. 61
split-brain patients, p. 54
synapse, p. 62
temporal lobe, p. 57
terminal buttons, p. 62
thalamus, p. 48
transcranial magnetic
stimulation (TMS), p. 74
visual cortex, p. 60
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Brain Asymmetry
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3
C
H
A
P
T
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R
Visual Perception
CHAPTER OUTLINE
From Sensation to Representation
Some Basic Concepts of Perception
Seeing Things That Aren’t There, or Are They?
How Does Our Visual System Work?
Pathways to Perceive the What and the Where
Approaches to Perception: How Do We
Make Sense of What We See?
Bottom-Up Theories
Direct Perception
Template Theories
Feature-Matching Theories
Recognition-by-Components Theory
Top-Down Theories
How Do Bottom-Up Theories and Top-Down
Theories Go Together?
Perception of Objects and Forms
Viewer-Centered vs. Object-Centered Perception
The Perception of Groups—Gestalt Laws
Recognizing Patterns and Faces
Two Different Pattern Recognition Systems
The Neuroscience of Recognizing Faces
and Patterns
84
The Environment Helps You See
Perceptual Constancies
Depth Perception
Depth Cues
The Neuroscience of Depth Perception
Deficits in Perception
Agnosias and Ataxias
Difficulties Perceiving the “What”
Difficulties in Knowing the “How”
Are Perceptual Processes Independent
of Each Other?
Anomalies in Color Perception
Why Does It Matter? Perception in Practice
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
CHAPTER 3 • Visual Perception
85
Here are some of the questions we will explore in this chapter:
1. How can we perceive an object like a chair as having a stable form, given that the image of the chair
on our retina changes as we look at it from different directions?
2. What are two fundamental approaches to explaining perception?
3. What happens when people with normal visual sensations cannot perceive visual stimuli?
n BELIEVE IT OR NOT
IF YOU ENCOUNTERED TYRANNOSAURUS REX,
WOULD STANDING STILL SAVE YOU?
Have you seen the movie Jurassic Park? In this movie, one
protagonist tells another while facing a Tyrannosaurus Rex
that they will be safe as long as they don’t move, because
the T. Rex can detect his prey only when it is moving.
Well, he could not have been more wrong. As it now
turns out, T. Rex had excellent binocular vision (i.e., the
vision fields of both eyes are combined to achieve depth
perception). Researchers had the heads of several dinosaur species reconstructed and found that T. Rex probably
could see 13 times better than humans (for comparison,
eagles can only see 3.6 times better than humans). Its
excellent vision is due to the big binocular range, which
is the area that can be seen by both eyes at the same
time. In addition, over time T. Rex’s snout became longer,
its cheeks grew thinner so as not to obstruct the view, and
its eyeballs became bigger. These changes all helped
T. Rex to have excellent three-dimensional (3-D) vision
(Jaffe, 2006; Stevens, 2006). This chapter will introduce
you to the basics of visual perception for humans—and
sometimes for other species as well.
As we are writing this chapter, we can look out of the window onto the city of
Boston. The high-rise buildings that are less than a mile away look about as small
as our computer screen. Yet we know that they are actually much bigger than our
screen—they only appear to be small. Try it out yourself. Look out of your window.
Can you see how things that are farther away seem much smaller than you know they
are? This is just one example of the complex process of perception.
Have you ever been told that you “can’t see something that’s right under your
nose”? How about that you “can’t see the forest for the trees”? Have you ever
listened to your favorite song over and over, trying to decipher the lyrics? In each of
these situations, we call on the complex construct of perception. Perception is the set
of processes by which we recognize, organize, and make sense of the sensations we
receive from environmental stimuli (Goodale, 2000a, 2000b; Kosslyn & Osherson,
1995; Marr, 1982; Pomerantz, 2003). Perception encompasses many psychological
phenomena. In this chapter, we focus primarily on visual perception. It is the most
widely recognized and the most widely studied perceptual modality (i.e., system for a
particular sense, such as touch or smell). First, we will get to know a few basic terms
and concepts of perception. We will then consider optical illusions that illustrate some
of the intricacies of human perception. Next, we will have a look at the biology of the
visual system. We will consider some approaches to explain perception, and afterward
have a closer look at some details of the perceptual process, namely the perception of
objects and forms, and how the environment provides cues to help you perceive your
surroundings. We will also explore what happens when people have difficulties in
perception.
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CHAPTER 3 • Visual Perception
INVESTIGATING COGNITIVE PSYCHOLOGY
Perception
Stand at one end of a room and hold your thumb up to your eye so that it is the same
size as the door on the opposite side of the room. Do you really think that your thumb is
as large as a door? No. You know that your thumb is close to you, so it just looks as
large as the door. There are numerous cues in the room to tell you that the door is farther
away from you than your thumb is. In your mind, you make the door much larger to compensate for the distance away from you. Knowledge is a key to perception. You know
that your thumb and the door are not the same size, so you are able to use this knowledge to correct for what you know is not so.
From Sensation to Representation
(b)
© Karin Sternberg
(a)
© Karin Sternberg
We do not perceive the world exactly as our eyes see it. Instead, our brain actively
tries to make sense of the many stimuli that enter our eyes and fall on our retina.
Take a look at Figure 3.1. You can see two high-rise buildings in the city of Boston.
(We live in one of them!) In the right photo, the right tower seems to be substantially higher than the left one. The left picture, however, shows that the towers actually are in fact exactly the same height. Depending on your viewpoint, objects can
look quite different, revealing different details. Thus, perception does not consist of
Figure 3.1 Objects Look Different Depending on the Perspective.
The pictures show the same two high-rise buildings in Boston from two different perspectives. In (a) they look about the
same size, as they in fact are. In (b), their image on the retina makes them seem to be of different heights, and it is only
through further processing that we can pinpoint they are the same size.
87
© Karin Sternberg
From Sensation to Representation
Figure 3.2 Reality or Reflection?
This picture shows the reflection of a church in a skyscraper. What is easy for us to perceive
constitutes a big problem for computers. Where does one building end and the next one
start? Which part of the percept belongs to which object? What distinguishes the real person
on the street from his or her reflection in the building so that a computer can recognize which
one is the reflection?
just seeing what is being projected onto your retina; the process is much more complex. Your brain processes the visual stimuli, giving the stimuli meaning and interpreting them.
How difficult it is to interpret what we see has become clear in recent years
as researchers have tried to teach computers to “see”; but computers are still
lagging behind humans in object recognition. Can you recognize what is shown in
Figure 3.2? The picture shows a church that is reflected in a high-rise building. It
might have taken you a few moments to figure out what is depicted in the photo,
but for computers, this is an extremely difficult task. It is not immediately clear in
this picture what is reflection, what is the building, and what is surrounding. Furthermore, the borders of the church are blurred so that it becomes very challenging
to see where the object ends and what it really is. So, while it may not take you a lot
of effort to identify the objects in this photo, it does take a lot of processing to perceive them, as the stimuli are very ambiguous.
This chapter focuses on the processes of visual perception and the processes
we use to make sense of the visual stimuli that are focused on our retina. We start
our exploration by familiarizing ourselves with some basic concepts. To illustrate
the intricacies of perception, we then look at some optical illusions. And finally
we learn how the eye receives impressions of stimuli and sends signals to the
brain.
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CHAPTER 3 • Visual Perception
Some Basic Concepts of Perception
In his influential and controversial work, James Gibson (1966, 1979) provided a useful framework for studying perception. He introduced the concepts of distal (external) object, informational medium, proximal stimulation, and perceptual object.
Let’s examine each of these.
The distal (far) object is the object in the external world (e.g., a falling tree).
The event of the tree falling creates a pattern on an informational medium. The
informational medium could be sound waves, as in the sound of the falling tree.
The informational medium might also be reflected light, chemical molecules, or
tactile information coming from the environment. For example, when the information from light waves come into contact with the appropriate sensory receptors of
the eyes, proximal (near) stimulation occurs (i.e., the cells in your retina absorb
the light waves). Perception occurs when a perceptual object (i.e., what you see) is
created in you that reflects the properties of the external world. That is, an image
of a falling tree is created on your retina that reflects the falling tree that is in front
of you.
Table 3.1 lists the various properties of distal objects, informational media, proximal stimuli, and perceptual objects for five different senses (sight, sound, smell,
taste, and touch). The processes of perception vary tremendously across the different
senses.
Table 3.1
Perceptual Continuum
Perception occurs when the informational medium carries information about a distal object to a person. When the
person’s sense receptors pick up on the information, proximal stimulation occurs, which results in the person’s
perceiving an object.
Modality
Distal Object
Informational Medium
Proximal Stimulation
Perceptual Object
Vision—
sight
Grandma’s
face
Reflected light from Grandma’s face (visible electromagnetic waves)
Photon absorption in the rod
and cone cells of the retina, the
receptor surface in the back of
the eye
Grandma’s face
Audition—
sound
A falling
tree
Sound waves generated by
the tree’s fall
Sound-wave conduction to the
basilar membrane, the receptor
surface within the cochlea of the
inner ear
A falling tree
Olfaction—
smell
Bacon being fried
Molecules released by frying
bacon
Molecular absorption in the
cells of the olfactory epithelium,
the receptor surface in the nasal
cavity
Bacon
Gustation—
taste
Ice cream
Molecules of ice cream both
released into the air and dissolved in water
Molecular contact with taste
buds, the receptor cells on the
tongue and soft palate, combined with olfactory stimulation
Ice cream
Touch
A computer
keyboard
Mechanical pressure and vibration at the point of contact
between the surface of the skin
and the keyboard
Stimulation of various receptor
cells within the dermis, the innermost layer of skin
Computer keys
From Sensation to Representation
89
So, if a tree falls in the forest and no one is around to hear it, does it make a
sound? It makes no perceived sound. But it does make a sound by creating sound
waves. So the answer is “yes” or “no,” depending on how you look at the question.
“Yes” if you believe that the existence of sound waves is all that’s needed to confirm
the existence of a sound. But you would answer “no” if you believe the sound needs to
be perceived (for the sound waves to have landed on the receptors in someone’s ears).
The question of where to draw the line between perception and cognition, or
even between sensation and perception, arouses much debate with no ready resolution. Instead, to be more productive in moving toward answerable questions, we
should view these processes as part of a continuum. Information flows through the
system. Different processes address different questions. Questions of sensation focus
on qualities of stimulation. Is that shade of red brighter than the red of an apple? Is
the sound of that falling tree louder than the sound of thunder? How well do one
person’s impressions of colors or sounds match someone else’s impressions of those
same colors or sounds?
This same color or sound information answers different questions for perception.
These are typically questions of identity and of form, pattern, and movement. Is that
red thing an apple? Did I just hear a tree falling? Finally, cognition occurs as this information is used to serve further goals. Is that apple edible? Should I get out of this
forest?
We never can experience through vision, hearing, taste, smell, or touch exactly
the same set of stimulus properties we have experienced before. Every apple casts a
somewhat different image on our retina; no falling tree sounds exactly like another;
and even the faces of our relatives and friends look quite different, depending on
whether they are smiling, enraged, or sad. Likewise, the voice of any person sounds
somewhat different, depending on whether he or she is sick, out of breath, tired,
happy, or sad. Therefore, one fundamental question for perception is “How do we
achieve perceptual stability in the face of this utter instability at the level of sensory
receptors?” Actually, given the nature of our sensory receptors, variation seems even
necessary for perception! In the phenomenon of sensory adaptation, receptor cells
adapt to constant stimulation by ceasing to fire until there is a change in stimulation. Through sensory adaptation, we may stop detecting the presence of a
stimulus.
To study visual perception, scientists devised a way to create stabilized images.
Such images do not move across the retina because they actually follow the eye
movements. The use of this technique has confirmed the hypothesis that constant
stimulation of the cells of the retina gives the impression that the image disappears
(Ditchburn, 1980; Martinez-Conde, Macknik, & Hybel, 2004; Riggs et al., 1953).
The word “Ganzfeld” is German and means “complete field.” It refers to an unstructured visual field (Metzger, 1930). When your eyes are exposed to a uniform
field of stimulation (e.g., a red surface area without any shades, a clear blue sky, or
dense fog), you will stop perceiving that stimulus after a few minutes and see just a
gray field instead. This is because your eyes have adapted to the stimulus.
The mechanism of sensory adaptation ensures that sensory information is changing constantly. Because of the dulling effect of sensory adaptation in the retina (the
receptor surface of the eye), our eyes constantly are making tiny rapid movements.
These movements create constant changes in the location of the projected image
inside the eye. Thus, stimulus variation is an essential attribute for perception. It
paradoxically makes the task of explaining perception more difficult.
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INVESTIGATING COGNITIVE PSYCHOLOGY
The Ganzfeld Effect
Cut a Ping-Pong ball in two halves or use two plastic spoons. Paint them uniformly in red,
for example, making sure there are no streaks so that you really have one uniform field
of color. Put the ball halves or the spoons over your eyes so that your eyes are completely
covered. Then gaze toward a light source for a few minutes. At some point, your perception will change from the color red to gray because your cells have adapted to the constant stimulus. Some people also perceive hallucinations and experience altered states of
consciousness when exposed to a Ganzfeld (Wackermann, Puetz, & Allefeld, 2008).
Seeing Things That Aren’t There, or Are They?
To find out about some of the phenomena of perception, psychologists often study
situations that pose problems in making sense of our sensations. Consider, for example, the image displayed in Figure 3.3. To most people, the figure initially looks like
a blur of meaningless shadings. A recognizable creature is staring them in the face,
but they may not see it. When people finally realize what is in the figure, they rightfully feel “cowed.” The figure of the cow is hidden within the continuous gradations
of shading that constitute the picture. Before you recognized the figure as a cow, you
correctly sensed all aspects of the figure. But you had not yet organized those sensations to form a mental percept—that is, a mental representation of a stimulus that is
perceived. Without such a percept of the cow, you could not meaningfully grasp
what you previously had sensed.
The preceding examples show that sometimes we cannot perceive what does exist. At other times, however, we perceive things that do not exist. For example, notice the black triangle in the center of the left panel of Figure 3.4. Also note the
white triangle in the center of the right panel of Figure 3.4. They jump right out at
Figure 3.3 Dallenbach’s Cow.
What do you learn about your own perception by trying to identify the object staring at you
from this photo?
Source: From Dallenbach, K. M. (1951). A puzzle-picture with a new principle of concealment. American Journal of Psychology, 54, 431–433.
From Sensation to Representation
91
Figure 3.4 Elusive Triangles: Real or Illusions?
You easily can see the triangles in this figure—or are the triangles just an illusion?
Source: From In Search of the Human Mind by Robert J. Sternberg, © 1995 by Harcourt Brace & Company.
Reproduced by permission of the publisher.
(a)
(b)
Figure 3.5 The Parthenon.
The columns of the Parthenon in Greece actually bulge slightly in the middle (b) to compensate for the visual tendency to perceive that straight parallel lines (a) seem to curve inward.
Similarly, the horizontal lines of the beams crossing the top of the columns and the top step of
the porch bulge slightly upward to counteract the tendency to perceive that they curve slightly
downward. In addition, the columns lean ever so slightly inward at the top to compensate for
the tendency to perceive them as spreading out as we gaze upward at them. Architects consider these distortions of visual perception in their designs today.
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you. Now look very closely at each of the panels. You will see that the triangles are
not really all there. The black that constitutes the center triangle in the left panel
looks darker, or blacker, than the surrounding black. But it is not. Nor is the white
central triangle in the right panel any brighter, or whiter, than the surrounding
white. Both central triangles are optical illusions. They involve the perception of
visual information not physically present in the visual sensory stimulus.
So, sometimes we perceive what is not there. Other times, we do not perceive
what is there. And at still other times, we perceive what cannot be there.
The existence of perceptual illusions suggests that what we sense (in our sensory
organs) is not necessarily what we perceive (in our minds). Our minds must be taking the available sensory information and manipulating that information somehow
to create mental representations of objects, properties, and spatial relationships
within our environments (Peterson, 1999). The way we represent these objects will
depend in part on our viewpoint in perceiving the objects (Edelman & Weinshall,
1991; Poggio & Edelman, 1990; Tarr, 1995; Tarr & Bülthoff, 1998). An example
in architecture is the use of optical illusions in the construction of the Parthenon
(Figure 3.5). Were the Parthenon actually constructed the way it appears to us perceptually (with strictly rectilinear form), its appearance would be bizarre.
Architects are not the only ones to have recognized some fundamental principles of perception. For centuries, artists have known how to lead us to perceive
3-D percepts when viewing two-dimensional (2-D) images. What are some of the
principles that guide our perceptions of both real and illusory percepts? We will explore the answer to this question as we move through the chapter. We begin with
examining our visual system.
Increasing energy
Increasing wavelength
0.0001 nm
10 nm
0.01 nm
Gamma rays
X-rays
1000 nm
Ultraviolet
0.01 cm
Infrared
1 cm
1m
Radio waves
Radar TV FM
Visible light
400 nm
500 nm
600 nm
100 m
700 nm
Figure 3.6 The Electromagnetic Spectrum.
This image shows the different wavelengths that light comes in, and the small array of
wavelengths that is actually visible to humans.
AM
From Sensation to Representation
93
How Does Our Visual System Work?
The precondition for vision is the existence of light. Light is electromagnetic radiation that can be described in terms of wavelength. Humans can perceive only a
small range of the wavelengths that exist; the visible wavelengths are from 380 to
750 nanometers (Figure 3.6; Starr, Evers, & Starr, 2007).
Vision begins when light passes through the protective covering of the eye
(Figure 3.7). This covering, the cornea, is a clear dome that protects the eye. The
light then passes through the pupil, the opening in the center of the iris. It continues
through the crystalline lens and the vitreous humor. The vitreous humor is a gel-like
substance that comprises the majority of the eye.
Eventually, the light focuses on the retina where electromagnetic light energy is
transduced—that is, converted—into neural electrochemical impulses (Blake, 2000).
Vision is most acute in the fovea, which is a small, thin region of the retina, the
size of the head of a pin. When you look straight at an object, your eyes rotate so that
the image falls directly onto the fovea. Although the retina is only about as thick as a
single page in this book, it consists of three main layers of neuronal tissue (Figure 3.8).
The first layer of neuronal tissue—closest to the front, outward-facing surface of the
eye—is the layer of ganglion cells, whose axons constitute the optic nerve. The second
layer consists of three kinds of interneuron cells. Amacrine cells and horizontal cells
Suspensory ligaments
Conjunctiva
Anterior chamber
containing aqueous
humor
Sclera (white of eye)
Choroid
Retina
Pupil
Lens
Vitreous humor
Cornea
Fovea
Iris
(colored
part of eye)
Posterior
chamber
Ciliary body
(containing ciliary
muscle)
Optic nerve
Blind spot
Tendon of rectus muscle
Figure 3.7 The Human Eye.
The composition of the human eye.
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CHAPTER 3 • Visual Perception
Rods
Cones
Horizontal
cell
Bipolar
cell
Amacrine
cell
Ganglion cell
Light
Figure 3.8 The Retina.
The retina is made up of rods and cones, horizontal cells, bipolar cells, amacrine cells, and
ganglion cells.
make single lateral (i.e., horizontal) connections among adjacent areas of the retina in
the middle layer of cells. Bipolar cells make dual connections forward and outward to
the ganglion cells, as well as backward and inward to the third layer of retinal cells.
The third layer of the retina contains the photoreceptors, which convert light
energy into electrochemical energy that is transmitted by neurons to the brain.
There are two kinds of photoreceptors—rods and cones. Each eye contains roughly
120 million rods and 8 million cones. Rods and cones differ not only in shape but
also in their compositions, locations, and responses to light. Within the rods and
cones are photopigments, chemical substances that react to light and transform
physical electromagnetic energy into an electrochemical neural impulse that can be
understood by the brain. The rods are long and thin photoreceptors. They are more
highly concentrated in the periphery of the retina than in the foveal region. The
rods are responsible for night vision and are sensitive to light and dark stimuli.
From Sensation to Representation
95
The cones are short and thick photoreceptors and allow for the perception of color.
They are more highly concentrated in the foveal region than in the periphery of the
retina (Durgin, 2000).
The rods, cones, and photopigments could not do their work were they not somehow hooked up to the brain. The neurochemical messages processed by the rods and
cones of the retina travel via the bipolar cells to the ganglion cells (see Goodale,
2000a, 2000b). The axons of the ganglion cells in the eye collectively form the optic
nerve for that eye. The optic nerves of the two eyes join at the base of the brain to
form the optic chiasma (see Figure 2.8 in Chapter 2). At this point, the ganglion cells
from the inward, or nasal, part of the retina—the part closer to your nose—cross
through the optic chiasma and extend to the opposite hemisphere of the brain. The
ganglion cells from the outward, or temporal area of the retina closer to your temple
go to the hemisphere on the same side of the body. The lens of each eye naturally
inverts the image of the world as it projects the image onto the retina. In this way,
the message sent to your brain is literally upside-down and backward.
After being routed via the optic chiasma, about 90% of the ganglion cells then
go to the lateral geniculate nucleus of the thalamus. From the thalamus, neurons
carry information to the primary visual cortex (V1 or striate cortex) in the occipital
lobe of the brain. The visual cortex contains several processing areas. Each area handles different kinds of visual information relating to intensity and quality, including
color, location, depth, pattern, and form.
Pathways to Perceive the What and the Where
What are the visual pathways in the brain? A pathway in general is the path the
visual information takes from its entering the human perceptual system through
the eyes to its being completely processed. Generally, researchers agree that there
are two pathways. Work on visual perception has identified separate neural pathways
in the cerebral cortex for processing different aspects of the same stimuli (De Yoe &
Van Essen, 1988; Köhler et al., 1995). Perception deficits like ataxia and agnosia that
are covered later in this chapter also point toward the existence of different pathways.
Why are there two pathways? It is because the information from the primary
visual cortex in the occipital lobe is forwarded through two fasciculi (fiber bundles):
One ascends toward the parietal lobe (along the dorsal pathway), and one descends
to the temporal lobe (along the ventral pathway). The dorsal pathway is also called
the where pathway and is responsible for processing location and motion information;
the ventral pathway is called the what pathway because it is mainly responsible for
processing the color, shape, and identity of visual stimuli (Ungerleider & Haxby,
1994; Ungerleider & Mishkin, 1982).
This general view is referred to as the what/where hypothesis. Most of the research
in this area has been carried out with monkeys. In particular, a group of monkeys
with lesions in the temporal lobe were able to indicate where things were but seemed
unable to recognize what they were. In contrast, monkeys with lesions in the parietal
lobe were able to recognize what things were but not where they were.
An alternative interpretation of the visual pathways has been suggested. This
interpretation is that the two pathways refer not to what things are and to where
they are, but rather, to what they are and to how they function. This view is known
as the what/how hypothesis (Goodale & Milner, 2004; Goodale & Westwood, 2004).
This hypothesis argues that spatial information about where something is located in
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space is always present in visual information processing. What differs between the
two pathways is whether the emphasis is on identifying what an object is or, instead,
on how we can situate ourselves so as to grasp the object.
The what pathway can be found in the ventral stream and is responsible for the
identification of objects. The how pathway is located in the dorsal stream and controls movements in relation to the objects that have been identified through the
“what” pathway. Ventral and dorsal streams both arise from the same early visual
areas (Milner & Goodale, 2008).
The what/how hypothesis is best supported by evidence of processing deficits:
There are deficits that impair people’s ability to recognize what they see and
there are distinct deficits that impair people’s ability to reach for what they see (how).
CONCEPT CHECK
1. What is the difference between sensation and perception?
2. What is the difference between the distal and the perceptual object?
3. How are rods and cones both similar to and different from each other?
4. What are some of the major parts of the eye and what are their functions?
5. What is the “what/where” hypothesis?
Approaches to Perception: How Do We Make
Sense of What We See?
Now that we know how a light stimulus that enters our eye is processed and routed to
the brain, the question still remains as to how we actually perceive what we see. Do we
just perceive whatever is being projected on our retina, or is there more to perception?
Does our knowledge, and other rules we have learned throughout our life, maybe influence our perception of the world? Going back to our view out of the window, the
image on our retina suggests that the buildings we see in the distance are very small.
However, we do see other buildings, trees, and streets in front of them that suggest
that those buildings are in fact quite large and just appear small because they are far
away from our office. In this case, our experience and knowledge about perception and
the world allows us to perceive those buildings as tall ones even though they do not
look larger than does our hand in front of us on our desk.
There are different views on how we perceive the world. These views can be
summarized as bottom-up theories and top-down theories. Bottom-up theories describe approaches where perception starts with the stimuli whose appearance you
take in through your eye. You look out onto the cityscape, and perception happens
when the light information is transported to your brain. Therefore, they are datadriven (i.e., stimulus-driven) theories.
Not all theorists focus on the sensory data of the perceptual stimulus. Many
theorists prefer top-down theories, according to which perception is driven by
high-level cognitive processes, existing knowledge, and the prior expectations that
influence perception (Clark, 2003). These theories then work their way down to
considering the sensory data, such as the perceptual stimulus. You perceive buildings
as big in the background of the city scene because you know these buildings are far
Approaches to Perception: How Do We Make Sense of What We See?
97
away and therefore must be bigger than they appear. From this viewpoint, expectations are important. When people expect to see something, they may see it even if it
is not there or is no longer there. For example, suppose people expect to see a certain person in a certain location. They may think they see that person, even if they
are actually seeing someone else who looks only vaguely similar (Simons, 1996).
Top-down and bottom-up approaches have been applied to virtually every aspect of cognition. Bottom-up and top-down approaches usually are presented as
being in opposition to each other. But to some extent, they deal with different aspects of the same phenomenon. Ultimately, a complete theory of perception will
need to encompass both bottom-up and top-down processes.
Bottom-Up Theories
The four main bottom-up theories of form and pattern perception are direct perception, template theories, feature theories, and recognition-by-components theory.
Direct Perception
How do you know the letter A when you see it? Easy to ask, hard to answer. Of
course, it’s an A because it looks like an A. What makes it look like an A, though,
instead of like an H? Just how difficult it is to answer this question becomes apparent when you look at Figure 3.9. You probably will see the image in Figure 3.9 as the
words “THE CAT.” Yet the H of “THE” is identical to the A of “CAT.” What subjectively feels like a simple process of pattern recognition is almost certainly quite
complex.
Gibson’s Theory of Direct Perception How do we connect what we perceive to
what we have stored in our minds? Gestalt psychologists referred to this problem as
the Hoffding function (Köhler, 1940). It was named after 19th-century Danish psychologist Harald Hoffding. He questioned whether perception is such a simple process that all it takes is to associate what is seen with what is remembered
(associationism). An influential and controversial theorist who questioned associationism is James J. Gibson (1904–1980).
According to Gibson’s theory of direct perception, the information in our sensory receptors, including the sensory context, is all we need to perceive anything. As
the environment supplies us with all the information we need for perception, this
view is sometimes also called ecological perception. In other words, we do not need
higher cognitive processes or anything else to mediate between our sensory experiences and our perceptions. Existing beliefs or higher-level inferential thought processes are not necessary for perception.
Figure 3.9 Can You Read These Words?
When you read these words, you probably have no difficulty differentiating the A from
the H. Look more closely at each of these two letters. What features differentiate them?
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© Karin Sternberg
Gibson believed that, in the real world, sufficient contextual information usually
exists to make perceptual judgments. He claimed that we need not appeal to higherlevel intelligent processes to explain perception. Gibson (1979) believed that we use
this contextual information directly. In essence, we are biologically tuned to respond
to it. According to Gibson, we use texture gradients as cues for depth and distance.
Those cues aid us to perceive directly the relative proximity or distance of objects
and of parts of objects.
In Figure 3.10, you can see different rock formations at the sea coast. For the
rocks that are closest to the photographer, you can see many details, like notches,
holes, and variations in color. The farther away the objects on the picture are, the
fewer the details you can see. You are using texture gradients as an indicator of how
far away the rocks are. And because some of the rocks cover up parts of other rocks,
you infer from that information that the rocks that are partly covered must be farther away than the rocks that cover them. Based on our analysis of the stable relationships among features of objects and settings in the real world, we directly
perceive our environment (Gibson, 1950, 1954/1994; Mace, 1986). We do not
need the aid of complex thought processes.
Such contextual information might not be readily controlled in a laboratory experiment. But such information is likely to be available in a real-world setting.
Figure 3.10 Cues Used in Depth Perception.
The farther away an object is, the fewer details you can see. You can see small holes and the rough texture of the rock
in the foreground whereas the rocks in the background look much smoother. The rock that is partly obscured is located
behind the rock that obscures it. We use these cues to aid us in depth perception.
Approaches to Perception: How Do We Make Sense of What We See?
99
Therefore, as noted above, Gibson’s model sometimes is referred to as an ecological
model (Turvey, 2003). This reference is a result of Gibson’s concern with perception
as it occurs in the everyday world (the ecological environment) rather than in laboratory situations, where less contextual information is available.
Ecological constraints apply not only to initial perceptions but also to the ultimate internal representations (such as concepts) that are formed from those perceptions (Hubbard, 1995; Shepard, 1984). Continuing to wave the Gibsonian banner
was Eleanor Gibson (1991, 1992), James’ wife. She conducted landmark research
in infant perception. She observed that infants (who certainly lack much prior
knowledge and experience) quickly develop many aspects of perceptual awareness,
including depth perception.
Direct perception may also play a role in interpersonal situations when we try to
make sense of others’ emotions and intentions (Gallagher, 2008). After all, we can
recognize emotion in faces as such; we do not see facial expressions that we then try
to piece together to result in the perception of an emotion (Wittgenstein, 1980).
Neuroscience and Direct Perception Neuroscience also indicates that direct perception may be involved in person perception. About 30 to 100 milliseconds after
a visual stimulus, mirror neurons start firing. Mirror neurons are active both when a
person acts and when he or she observes that same act performed by somebody else.
So before we even have time to form hypotheses about what we are perceiving, we
may already be able to understand the expressions, emotions, and movements of the
person we observe (Gallagher, 2008).
Furthermore, studies indicate that there are separate neural pathways (what pathways) in the lateral occipital area for the processing of form, color, and texture in
objects. When asked to judge the length of an object, for example, people cannot
ignore the width. However, they can judge the color, form, and texture of an object
independently of the other qualities (Cant & Goodale, 2007; Cant, Large, McCall, &
Goodale, 2008).
Template Theories
Template theories suggest that we have stored in our minds myriad sets of templates.
Templates are highly detailed models for patterns we potentially might recognize.
We recognize a pattern by comparing it with our set of templates. We then choose
the exact template that perfectly matches what we observe (Selfridge & Neisser,
1960). We see examples of template matching in our everyday lives. Fingerprints
are matched in this way. Machines rapidly process imprinted numerals on checks
by comparing them to templates. Increasingly, products of all kinds are identified
with universal product codes (UPCs or “bar codes”). They can be scanned and identified by computers at the time of purchase. Chess players who have knowledge of
many games use a matching strategy in line with template theory to recall previous
games (Gobet & Jackson, 2002). Template matching theories belong to the group of
chunk-based theories that suggest that expertise is attained by acquiring chunks of
knowledge in long-term memory that can later be accessed for fast recognition.
Studies with chess players have shown that the temporal lobe is indeed activated
when the players access the stored chunks in their long-term memory (Campitelli,
Gobet, Head, Buckley, & Parker, 2007).
In each of the aforementioned instances, the goal of finding one perfect match
and disregarding imperfect matches suits the task. You would be alarmed to find that
your bank’s numeral-recognition system failed to register a deposit to your account.
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Such failure might occur because it was programmed to accept an ambiguous character according to what seemed to be a best guess. For template matching, only an
exact match will do. This is exactly what you want from a bank computer. However,
consider your perceptual system at work in everyday situations. It rarely would work
if you required exact matches for every stimulus you were to recognize. Imagine, for
example, needing mental templates for every possible percept of the face of someone
you love. Imagine one for each facial expression, each angle of viewing, each addition or removal of makeup, each hairdo, and so on.
Template-matching theories fail to explain some aspects of the perception of letters. For one thing, such theories cannot easily account for our perception of the
letters and words in Figure 3.9. We identify two different letters (A and H) from
only one physical form. Hoffding (1891) noted other problems. We can recognize
an A as an A despite variations in the size, orientation, and form in which the letter
is written. Are we to believe that we have mental templates for each possible size,
orientation, and form of a letter? Storing, organizing, and retrieving so many templates in memory would be unwieldy. How could we possibly anticipate and create
so many templates for every conceivable object of perception (Figure 3.11)?
Neuroscience and Template Theories Letters of the alphabet are simpler than
faces and other complex stimuli. But how do we recognize letters? And does it
make a difference to our brain whether we perceive letters or digits? Experiments
suggest that there is indeed a difference between letters and digits. There is an
area on or near the left fusiform gyrus that is activated significantly more when a
person is presented with letters than with digits. It is not clear if this “letter area”
only processes letters or if it also plays a more minor role in the processing of
digits (Polk et al., 2002). The notion of the visual cortex specializing in different
stimuli is not new; other areas have been found that specialize in faces, for example (see Kanwisher et al., 1997; McCarthy et al., 1997). Later in this chapter we
will consider in more detail the structures of the brain that enable us to recognize
faces.
Why Computers Have Trouble Reading Handwriting Think about how easy it
is for you to perceive and understand someone’s handwriting. In handwriting, everybody’s numbers and letters look a bit different. You can still distinguish them
without any problems (at least in most cases). This is something computers do not
do very well at all. For computers, the reading of handwriting is an incredibly difficult process that’s prone to mistakes. When you deposit a check at an ATM
machine, it “reads” your check automatically. In fact, the numbers at the bottom of
your check that are written in a strange-looking font are so distinct that a machine
cannot mistake them for one another. However, it is much harder for a machine to
decipher handwriting. Similarly, a machine also will have trouble determining that
all the letters in the right of Figure 3.11 are As (unless it has a template for each one
of the As). Therefore, some computers work with algorithms that consider the
context in which the word is presented, the angular positions of the written letters
(e.g., upright or tilted), and other factors.
Given the sophistication of current-day robots, what is the source of human superiority? There may be several, but one is certainly knowledge. We simply know
much more about the environment and sources of regularity in the environment
than do robots. Our knowledge gives us a great advantage that robots, at least of
the current day, are still unable to bridge.
Approaches to Perception: How Do We Make Sense of What We See?
101
A
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Figure 3.11
Template Matching in Barcodes and Letters.
A particular barcode will always look exactly the same way, making it easy for computers to
read. Letters, to the contrary, can look very differently although they depict the same letter.
Template matching will distinguish between different bar codes but will not recognize that
different versions of the letter A written in different scripts are indeed both As.
Feature-Matching Theories
Yet another alternative explanation of pattern and form perception may be found in
feature-matching theories. According to these theories, we attempt to match features of a pattern to features stored in memory, rather than to match a whole pattern
to a template or a prototype (Stankiewicz, 2003).
The Pandemonium Model One such feature-matching model has been called Pandemonium (“pandemonium” refers to a very noisy, chaotic place and hell). In it, metaphorical “demons” with specific duties receive and analyze the features of a stimulus
(Selfridge, 1959).
In Oliver Selfridge’s Pandemonium Model, there are four kinds of demons: image
demons, feature demons, cognitive demons, and decision demons. Figure 3.12 shows
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Feature demons
(decode specific
features)
Cognitive demons
(“shout” when they receive
certain combinations
of features)
Vertical lines
1
2
3
4
Horizontal lines
1
2
3
4
A
C D
E
F
G
Oblique lines
Image demon
(receives sensory input)
R
1
2
3
4
I
K
Right angles
R
Processing
of signal
1
2
3
4
Acute angles
1
2
3
4
Discontinuous
curves
1
2
3
4
Continuous
curves
1
2
3
4
B
M
O
Q
S
H
J
Decision demon
(“listens” for
loudest shout
in pandemonium
to identify input)
L
P? D?
N
R
P
R
T
U
W
V
X
Y
Z
Figure 3.12 Selfridge’s Feature-Matching Model.
According to Oliver Selfridge’s feature-matching model, we recognize patterns by matching observed features to features already stored in memory. We recognize the patterns for which we have found the greatest number of matches.
this model. The “image demons” receive a retinal image and pass it on to “feature
demons.” Each feature demon calls out when there are matches between the stimulus and the given feature. These matches are yelled out at demons at the next
level of the hierarchy, the “cognitive (thinking) demons.” The cognitive demons
in turn shout out possible patterns stored in memory that conform to one or
Approaches to Perception: How Do We Make Sense of What We See?
Figure 3.13
H
H
H
H
H
H
H
H
HHHHHH
H
H
H
H
H
H
H
H
S
S
S
S
S
S
S
S
S S S S S S
S
S
S
S
S
S
S
S
(a)
(b)
103
The Global Precedence Effect.
Compare panel (a) (a global H made of local Hs) with panel (b) (a global H made of local
Ss). All the local letters are tightly spaced.
Source: From D. Navon, “Forest before Trees: The Precedence to Global Features in Visual Perception,”
Cognitive Psychology, July 1977, Vol. 9, No. 3, pp. 353–382. Reprinted by permission of Elsevier.
more of the features noticed by the feature demons. A “decision demon” listens to
the pandemonium of the cognitive demons. It decides on what has been seen,
based on which cognitive demon is shouting the most frequently (i.e., which has
the most matching features).
Although Selfridge’s model is one of the most widely known, other feature models have been proposed. Most also distinguish not only different features but also different kinds of features, such as global versus local features. Local features constitute
the small-scale or detailed aspects of a given pattern. There is no consensus as to
what exactly constitutes a local feature. Nevertheless, we generally can distinguish
such features from global features, the features that give a form its overall shape.
Consider, for example, the stimuli depicted in Figure 3.13 (a) and (b). These are
of the type used in some research on pattern perception (see for example Navon,
1977, or Olesen et al., 2007). Globally, the stimuli in panels (a) and (b) form the
letter H. In panel (a), the local features (small Hs) correspond to the global ones. In
panel (b), comprising many local letter Ss, they do not.
In one study, participants were asked to identify the stimuli at either the global
or the local level (Navon, 1977). When the local letters were small and positioned
close together, participants could identify stimuli at the global level (the “big” letter)
more quickly than at the local level. When participants were required to identify
stimuli at the global level, whether the local features (small letters) matched the
global one (big letter) did not matter. They responded equally rapidly whether the
global H was made up of local Hs or of local Ss. However, when the participants
were asked to identify the “small” local letters, they responded more quickly if the
global features agreed with the local ones. In other words, they were slowed down
if they had to identify local (small) Ss combining to form a global (big) H instead
of identifying local (small) Hs combining to form a global (big) H. This pattern of
results is called the global precedence effect (see also Kimchi, 1992). Experiments have
showed that global information dominates over local information even in infants
(Cassia, Simion, Milani, & Umiltà, 2002).
In contrast, when letters are more widely spaced, as in panels (a) and (b) of
Figure 3.14, the effect is reversed. Then a local precedence effect appears. That is,
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CHAPTER 3 • Visual Perception
H
H
S
S
H
H
S
S
H
S
H
S
H
S
H
H
H
H
(a)
Figure 3.14
S
S
S
S
(b)
The Local Precedence Effect.
Compare panels (a) and (b), in which the local letters are widely spaced. Why does Figure
3.13 show the global precedence effect, and why does Figure 3.14 show the local precedence effect?
Source: D. Navon, “Forest before Trees: The Precedence to Global Features in Visual Perception,” Cognitive
Psychology, July 1977, Vol. 9, No. 3, pp. 353–382. Reprinted by permission of Elsevier.
the participants more quickly identify the local features of the individual letters
than the global ones, and the local features interfere with the global recognition
in cases of contradictory stimuli (Martin, 1979). So when the letters are close together at the local level, people have problems identifying the local stimuli (small
letters) if they are not concordant with the global stimulus (big letter). When the
letters on the local level are relatively far apart from each other, it is harder for
people to identify the global stimulus (big letter) if it is not concordant with the
local stimuli (small letters). Other limitations (e.g., the size of the stimuli) besides
special proximity of the local stimuli hold as well, and other kinds of features also
influence perception.
Neuroscience and Feature-Matching Theories Some support for feature theories
comes from neurological and physiological research. Researchers used single-cell recording techniques with animals (Hubel & Wiesel, 1963, 1968, 1979). They carefully measured the responses of individual neurons to visual stimuli in the visual
cortex. Then they mapped those neurons to corresponding visual stimuli for particular locations in the visual field (see Chapter 2). Their research showed that the
visual cortex contains specific neurons that respond only to a particular kind of stimulus (e.g., a horizontal line), and only if that stimulus fell onto a specific region of
the retina. Each individual cortical neuron, therefore, can be mapped to a specific
receptive field on the retina. A disproportionately large amount of the visual cortex
is devoted to neurons mapped to receptive fields in the foveal region of the retina,
which is the area of the most acute vision.
Most of the cells in the cortex do not respond simply to spots of light. Rather,
they respond to “specifically oriented line segments” (Hubel & Wiesel, 1979, p. 9).
What’s more, these cells seem to show a hierarchical structure in the degree of complexity of the stimuli to which they respond, somewhat in line with the ideas behind
the Pandemonium Model. That means that the outputs of the cells are combined to
create higher-order detectors that can identify increasingly more complex features.
At the lowest level, cells respond to lines, at a higher level they respond to corners
Approaches to Perception: How Do We Make Sense of What We See?
105
and edges, then to shapes, and so forth. Neurons that can recognize a complex
object are called gnostic units or “grandmother cells” because they imply that there
is a neuron that is capable of recognizing your grandmother. None of those neurons
are quite so specific, however, that they respond to just one person’s head. Even at
such a high level there is still some selectivity involved that allows cells to generally
fire when a human face comes into view.
Consider what happens as the stimulus proceeds through the visual system to
higher levels in the cortex. In general, the size of the receptive field increases, as
does the complexity of the stimulus required to prompt a response. As evidence of
this hierarchy, there were once believed to be just two kinds of visual cortex neurons
(Figure 3.15), simple cells and complex cells (Hubel & Wiesel, 1979), which were believed to differ in the complexity of the information about stimuli they processed.
This view proved to be oversimplified.
Based on Hubel and Wiesel’s work, other investigators have found feature detectors that respond to corners, angles, stars, or triangles (DeValois & DeValois, 1980;
Shapley & Lennie, 1985; Tanaka, 1993). In some areas of the cortex, highly sophisticated complex cells fire maximally only in response to very specific shapes, regardless
of the size of the given stimulus. Examples would be a hand or a face. As the stimulus
decreasingly resembles the optimal shape, these cells are decreasingly likely to fire.
We now know the picture is more complex than Hubel and Wiesel imagined. Cells
can serve multiple functions. These cells operate partially in parallel, although we are
not conscious of their operation. For example, spatial information about locations of
perceived objects was found to be processed simultaneously with information about
the contours of the object. Quite complex judgments about what is perceived are
made quite early in information processing, and in parallel (Dakin & Hess, 1999).
But once discrete features have been analyzed according to their orientations,
how are they integrated into a form we can recognize as particular objects? The
recognition-by-components theory we will consider next sheds some light on this
question.
off
on
off
off
Figure 3.15
on
on
Line Orientation and Cell Activation.
David Hubel and Torsten Wiesel discovered that cells in our visual cortex become activated
only when they detect the sensation of line segments of particular orientations. As you can
see, there is hardly any activation when the cell is presented with a horizontal line segment.
There is more activation when the line is diagonally oriented, and when the line is vertical,
the cell reacts with even more activation.
Source: From In Search of the Human Mind by Robert J. Sternberg, copyright © 1995 by Harcourt Brace &
Company. Reproduced by permission of the publisher.
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CHAPTER 3 • Visual Perception
Recognition-by-Components Theory
How do we form stable 3-D mental representations of objects? The recognitionby-components theory explains our ability to perceive 3-D objects with the help of
simple geometric shapes.
Seeing with the Help of Geons Irving Biederman (1987) suggested that we achieve
this by manipulating a number of simple 3-D geometric shapes called geons (for geometrical ions). They include objects such as bricks, cylinders, wedges, cones, and
their curved axis counterparts (Biederman, 1990/1993b). According to Biederman’s
recognition-by-components (RBC) theory, we quickly recognize objects by observing the edges of them and then decomposing the objects into geons. The geons also
can be recomposed into alternative arrangements. You know that a small set of letters can be manipulated to compose countless words and sentences. Similarly, a
small number of geons can be used to build up many basic shapes and then myriad
basic objects (Figure 3.16).
The geons are simple and are viewpoint-invariant (i.e., distinct from various
viewpoints). The objects constructed from geons thus are recognized easily from
many perspectives, despite visual noise. According to Biederman (1993a, 2001), his
RBC theory parsimoniously explains how we recognize the general classification for
multitudinous objects quickly, automatically, and accurately. This recognition occurs
despite changes in viewpoint. It occurs even under many situations in which the
stimulus object is degraded in some way. For example, if you see a car, you perceive
it as being made up of a number of different geons. You can recognize the car even if
you can’t see all of the geons because the car is partly obscured by another object in
front of it. Because the geons are viewpoint-invariant, you will also recognize the car
even if you look at it from the side or from behind. Cells in the inferior temporal
cortex (i.e., the lower part of the temporal cortex) react stronger to changes in geons
(which are viewpoint-invariant) than to changes in other geometrical properties
(e.g., changes in the size or diameter of a cylinder; Vogels, Biederman, Bar, &
Lorincz, 2001).
Biederman’s RBC theory explains how we may recognize general instances of
chairs, lamps, and faces, but it does not adequately explain how we recognize particular chairs or particular faces. An example would be your own face or your best friend’s
face. They are both made up of geons that constitute your mouth, eyes, nose, eyebrows, and so forth. But these geons are the same for both your and your friend’s faces.
So RBC theory cannot explain how we can distinguish one face from the next.
Biederman recognized that aspects of his theory require further work, such as
how the relations among the parts of an object can be described (Biederman, 1990/
1993b). Another problem with Biederman’s approach, and the bottom-up approach
in general, is how to account for the effects of prior expectations and environmental
context on some phenomena of pattern perception.
Neuroscience and Recognition-by-Components Theory What results would we expect if we were to confirm Biederman’s theory? Geons are viewpoint-invariant, so
studies should show that neurons exist that react to properties of an object that
stay the same, no matter whether you look at them from the front or the side. And
indeed, there are studies that have found neurons in the inferior temporal cortex
that are sensitive to just those viewpoint-invariant properties (Vogels et al., 2001).
However, many neurons respond primarily to one view of an object and decrease
their response gradually the more the object is rotated (Logothetis, Pauls, & Poggio,
Approaches to Perception: How Do We Make Sense of What We See?
107
(a)
(b)
Figure 3.16
Geons.
Irving Biederman amplified feature-matching theory by proposing a set of elementary components of patterns (a), which he based on variations in 3-D shapes derived in large part from a
cone (b).
1995). This finding contradicts the notion of Biederman’s theory that we recognize
objects by means of viewpoint-invariant geons. As a result, it is not clear at this
point whether Biederman’s theory is correct.
Top-Down Theories
In contrast to the bottom-up approach to perception is the top-down, constructive
approach (Bruner, 1957; Gregory, 1980; Rock, 1983; von Helmholtz, 1909/1962). In
constructive perception, the perceiver builds (constructs) a cognitive understanding
(perception) of a stimulus. The concepts of the perceiver and his or her cognitive
processes influence what he or she sees. The perceiver uses sensory information as
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CHAPTER 3 • Visual Perception
the foundation for the structure but also uses other sources of information to build the
perception. This viewpoint also is known as intelligent perception because it states
that higher-order thinking plays an important role in perception. It also emphasizes the
role of learning in perception (Fahle, 2003). Some investigators have pointed out that
not only does the world affect our perception but also the world we experience is actually
formed by our perception (Goldstone, 2003). In other words, perception is reciprocal
with the world we experience. Perception both affects and is affected by the world as
we experience it.
An interesting feature of the theory of constructive perception is that it links human intelligence even to fairly basic processes of perception. According to this theory,
perception comprises not merely a low-level set of cognitive processes, but actually a
quite sophisticated set of processes that interact with and are guided by human intelligence. When you look out your window, you “see” many things, but what you recognize yourself as seeing is highly processed by your intelligence. Interestingly, Titchener’s
structuralist approach (described in Chapter 1) ultimately failed because despite the efforts of Titchener and his followers to engage in introspection independently of their
prior knowledge, they and others found this, in the end, to be impossible. What you
perceive is shaped, at some level, by what you know and what you think.
For example, picture yourself driving down a road you have never traveled before. As you approach a blind intersection, you see an octagonal red sign with white
lettering. It bears the letters “ST_P.” An overgrown vine cuts between the T and
the P. Chances are, you will construct from your sensations a perception of a stop
sign. You thus will respond appropriately. Perceptual constancies are another example (see below). When you see a car approaching you on the street, its image on
your retina gets bigger as the car comes closer. And yet, you perceive the car to
stay the same size. This suggests that high-level constructive processes are at work
during perception. In color constancy, we perceive that the color of an object remains the same despite changes in lighting that alter the hue. Even in lighting that
becomes so dim that color sensations are virtually absent, we still perceive bananas
as yellow, plums as purple, and so on.
According to constructivists, during perception we quickly form and test various
hypotheses regarding percepts. The percepts are based on three things:
• what we sense (the sensory data),
• what we know (knowledge stored in memory), and
• what we can infer (using high-level cognitive processes).
In perception, we consider prior expectations. You’ll be fast to recognize your
friend from far away on the street when you have arranged a meeting. We also use
what we know about the context. When you see something approaching on rail
tracks you infer that it must be a train. And we also may use what we reasonably
can infer, based both on what the data are and on what we know about the data.
According to constructivists, we usually make the correct attributions regarding our
visual sensations. The reason is that we perform unconscious inference, the process
by which we unconsciously assimilate information from a number of sources to create a perception (Snow & Mattingley, 2003). In other words, using more than one
source of information, we make judgments that we are not even aware of making.
In the stop-sign example, sensory information implies that the sign is a meaningless assortment of oddly spaced consonants. However, your prior learning tells you
something important—that a sign of this shape and color posted at an intersection
of roadways and containing these three letters in this sequence probably means that
Approaches to Perception: How Do We Make Sense of What We See?
109
you should stop thinking about the odd letters. Instead, you should start slamming on
the brakes. Successful constructive perception requires intelligence and thought in
combining sensory information with knowledge gained from previous experience.
One reason for favoring the constructive approach is that bottom-up (datadriven) theories of perception do not fully explain context effects. Context effects
are the influences of the surrounding environment on perception (e.g., our perception of “THE CAT” in Figure 3.9). Fairly dramatic context effects can be demonstrated experimentally (Biederman, 1972; Biederman et al., 1974; Biederman,
Glass, & Stacy, 1973; De Graef, Christiaens, & D’Ydewalle, 1990). In one study,
people were asked to identify objects after they had viewed the objects in either an
appropriate or an inappropriate context for the items (Palmer, 1975). For example,
participants might see a scene of a kitchen followed by stimuli such as a loaf of
bread, a mailbox, and a drum. Objects that were appropriate to the established context, such as the loaf of bread in this example, were recognized more rapidly than
were objects that were inappropriate to the established context. The strength of
the context also plays a role in object recognition (Bar, 2004).
Perhaps even more striking is a context effect known as the configural-superiority effect (Bar, 2004; Pomerantz, 1981), by which objects presented in certain configurations
are easier to recognize than the objects presented in isolation, even if the objects in the
configurations are more complex than those in isolation. Suppose you show a participant
four stimuli, all of them diagonal lines [see Figure 3.17 (a)]. Three of the lines are
slanting one way, and one line is slanting the other way. The participant’s task is to
identify which stimulus is unlike the others. Now suppose that you show participants
four stimuli that are comprised of three lines each [Figure 3.17 (c)]. Three of the
stimuli are shaped like triangles, and one is not. In each case, the stimulus is a diagonal
line [Figure 3.17 (a)] plus other lines [Figure 3.17 (b)]. Thus, the stimuli in this second
condition are more complex variations of the stimuli in the first condition. However,
participants can more quickly spot which of the three-sided, more complicated
figures is different from the others than they can spot which of the lines is different
from the others.
(a)
Figure 3.17
(b)
(c)
The Configural-Superiority Effect.
Subjects more readily perceive differences among integrated configurations comprising multiple lines (c) than they do
solitary lines (a). In this figure, the lines in panel (b) are added to the lines in panel (a) to form shapes in panel (c),
thereby making panel (c) more complex than panel (a).
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CHAPTER 3 • Visual Perception
In a similar vein, there is an object-superiority effect, in which a target line that forms
a part of a drawing of a 3-D object is identified more accurately than a target that forms
a part of a disconnected 2-D pattern (Lanze, Weisstein, & Harris, 1982; Weisstein &
Harris, 1974). These findings parallel findings in the study of letter and word recognition: The word-superiority effect indicates that when people are presented with strings of
letters, it is easier for them to identify a single letter if the string makes sense and forms a
word instead of being just a nonsense sequel of letters. For example, it is easier to recognize the letter “o” in the word “house” than in the word “huseo” (Reicher, 1969).
The viewpoint of constructive or intelligent perception shows the central relation between perception and intelligence. According to this viewpoint, intelligence
is an integral part of our perceptual processing. We do not perceive simply in terms
of what is “out there in the world.” Rather, we perceive in terms of the expectations
and other cognitions we bring to our interaction with the world. In this view, intelligence and perceptual processes interact in the formation of our beliefs about what
it is that we are encountering in our everyday contacts with the world at large.
An extreme top-down position would drastically underestimate the importance
of sensory data. If it were correct, we would be susceptible to gross inaccuracies of
perception. We frequently would form hypotheses and expectancies that inadequately evaluated the sensory data available. For example, if we expected to see a
friend and someone else came into view, we might inadequately consider the perceptible differences between the friend and a stranger and mistake the stranger for the
friend. Thus, an extreme constructivist view of perception would be highly errorprone and inefficient. However, an extreme bottom-up position would not allow
for any influence of past experience or knowledge on perception. Why store knowledge that has no use for the perceiver? Neither extreme is ideal for explaining perception. It is more fruitful to consider ways in which bottom-up and top-down
processes interact to form meaningful percepts.
How Do Bottom-Up Theories and Top-Down
Theories Go Together?
Both theoretical approaches have garnered empirical support (cf. Cutting &
Kozlowski, 1977, vs. Palmer, 1975). So how do we decide between the two? On
one level, the constructive-perception theory, which is more top-down, seems to
contradict direct-perception theory, which is more bottom-up. Constructivists emphasize the importance of prior knowledge in combination with relatively simple
and ambiguous information from the sensory receptors. In contrast, directperception theorists emphasize the completeness of the information in the receptors
themselves. They suggest that perception occurs simply and directly. Thus, there is
little need for complex information processing.
Instead of viewing these theoretical approaches as incompatible, we may gain
deeper insight into perception by considering the approaches to be complementary.
Sensory information may be more richly informative and less ambiguous in interpreting experiences than the constructivists would suggest. But it may be less informative
than the direct-perception theorists would assert. Similarly, perceptual processes may
be more complex than hypothesized by Gibsonian theorists. This would be particularly true under conditions in which the sensory stimuli appear only briefly or are
degraded. Degraded stimuli are less informative for various reasons. For example,
the stimuli may be partially obscured or weakened by poor lighting. Or they may
be incomplete, or distorted by illusory cues or other visual “noise” (distracting visual
Perception of Objects and Forms
111
stimulation analogous to audible noise). We likely use a combination of information
from the sensory receptors and our past knowledge to make sense of what we perceive. Some experimental evidence supports this integrated view (Treue, 2003; van
Zoest & Donk, 2004; Wolfe et al., 2003).
Recent work suggests that, whereas the very first stage of the visual pathway represents only what is in the retinal image of an object, very soon, color, orientation, motion, depth, spatial frequency, and temporal frequency are represented. Later-stage
representations emphasize the viewer’s current interest or attention. In other words, the
later-stage representations are not independent of our attentional focus. On the contrary, they are directly affected by it (Maunsell, 1995). Moreover, vision for different
things can take different forms. Visual control of action is mediated by cortical pathways that are different from those involved in visual control of perception (Ganel &
Goodale, 2003). In other words, when we merely see an object, such as a cell phone,
we process it differently than if we intend also to pick up the object. In general, according to Ganel and Goodale (2003), we perceive objects holistically. But if we plan to act
on them, we perceive them more analytically so that we can act in an effective way.
To summarize, current theories concerning the ways we perceive patterns explain some, but not all, of the phenomena we encounter in the study of form and
pattern perception. Given the complexity of the process, it is impressive that we understand as much as we do. At the same time, clearly a comprehensive theory is still
forthcoming. Such a theory would need to account fully for the kinds of context
effects, such as the configural-superiority effect, described in this section.
Perception of Objects and Forms
Do we perceive objects in a viewer-centered or in an object-centered way? When we
gaze at any object in the space around us, do we perceive it in relation to us rather
than its actual structure, or do we perceive it in a more objective way that is independent of how it appears to us right this moment? We’ll examine this question in the next
section. Then, we look at Gestalt principles for perception, which explain why we perceive some objects as in groups but others as not so grouped (what is it that makes some
birds flying in the afternoon sky appear to be in a group whereas others do not?). Finally, we will consider the question of how we perceive patterns, for example faces.
Viewer-Centered vs. Object-Centered Perception
Right now one of your authors is looking at the computer on which he is typing this
text. He depicts the results of what he sees as a mental representation. What form
does this mental representation take? There are two common positions regarding the
answer to this question.
One position, viewer-centered representation, is that the individual stores the
way the object looks to him or her. Thus, what matters is the appearance of the
object to the viewer (in this case, the appearance of the computer to the author),
not the actual structure of the object. The shape of the object changes, depending
on the angle from which we look at it. A number of views of the object are stored,
and when we try to recognize an object, we have to rotate that object in our mind
until it fits one of the stored images.
The second position, object-centered representation, is that the individual
stores a representation of the object, independent of its appearance to the viewer.
In this case, the shape of the object will stay stable across different orientations
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CHAPTER 3 • Visual Perception
PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
DEPTH CUES IN PHOTOGRAPHY
Models and actors often use these depth cues of perception to their advantage while being photographed. For example, some models only allow certain angles or orientations to
be photographed. A long nose can appear shorter when photographed from slightly below the facial midline (just look closely at some pictures of Barbara Streisand from different
angles) because the bridge of the nose recedes slightly into the distance. Also, leaning forward a little can make the upper body appear slightly larger than the lower body, and
vice versa for leaning backward. In group pictures, standing slightly behind another person makes you appear smaller; standing slightly in front makes you appear larger.
Women’s swimsuit designers create optical-illusion swimsuits to enhance different features
of the body, making legs appear longer or waists appear smaller and either enhancing or
de-emphasizing bustlines. Some of these processes to alter perceptions are so basic that
many animals have special adaptations designed to make them appear larger (e.g., the
fanning peacock tail) or to disguise their identity from predators.
How could you apply perceptual processes to your advantage when having a photo
taken or when dressing for a party?
(McMullen & Farah, 1991). This stability can be achieved by means of establishing
the major and minor axes of the object, which then serve as a basis for defining further properties of the object.
Both positions can account for how the author represents a given object and its
parts. The key difference is in whether he represents the object and its parts in relation to him (viewer-centered) or in relation to the entirety of the object itself, independent of his own position (object-centered).
Consider, for example, the computer on which this text is being written. It has
different parts: a screen, a keyboard, a mouse, and so forth. Suppose the author represents the computer in terms of viewer-centered representation. Then its various
parts are stored in terms of their relation to him. He sees the screen as facing him
at perhaps a 20-degree angle. He sees the keyboard facing him horizontally. He sees
the mouse off to the right side and in front of him. Suppose, instead, that he uses an
object-centered representation. Then he would see the screen at a 70-degree angle
relative to the keyboard. And the mouse is directly to the right side of the keyboard,
neither in front of it nor in back of it.
One potential reconciliation of these two approaches to mental representation
suggests that people may use both kinds of representations. According to this approach, recognition of objects occurs on a continuum (Burgund & Marsolek, 2000;
Tarr, 2000; Tarr & Bülthoff, 1995). At one end of this continuum are cognitive
mechanisms that are more viewpoint-centered. At the other end of the continuum
are cognitive mechanisms that are more object-centered. For example, suppose you
see a picture of a car that is inverted. How do you know it is a car? Object-centered
mechanisms would recognize the object as a car, but viewpoint-centered mechanisms
would recognize the car as inverted.
A third orientation in representation is landmark-centered. In landmark-centered
representation, information is characterized by its relation to a well-known or prominent
item. Imagine visiting a new city. Each day you leave your hotel and go on short trips. It
is easy to imagine that you would represent the area you explore in relation to your hotel.
Perception of Objects and Forms
113
Evidence indicates that, in the laboratory, participants can switch between these
three strategies. There are, however, differences in brain activation among these
strategies (Committeri et al., 2004).
The Perception of Groups—Gestalt Laws
Perception helps us make sense of the confusing stimuli that we perceive in the world.
One way to bring order and coherence into our perception is our ability to group
similar things. This way, we can reduce the number of things that need to be processed. We can also better decide which things belong together or to the same object.
In other words, we organize objects in a visual array into coherent groups.
The Gestalt approach to form perception that was developed in Germany in the
early 20th century is useful particularly for understanding how we perceive groups of
objects or even parts of objects to form integral wholes (Palmer, 1999a, 1999b, 2000;
Palmer & Rock, 1994; Prinzmetal, 1995). It was founded by Kurt Koffka (1886–1941),
Wolfgang Köhler (1887–1968), and Max Wertheimer (1880–1943) and was based on
the notion that the whole differs from the sum of its individual parts (see Chapter 1).
The overarching law is the law of Prägnanz. We tend to perceive any given
visual array in a way that most simply organizes the different elements into a stable
and coherent form. Thus, we do not merely experience a jumble of unintelligible,
disorganized sensations. For example, we tend to perceive a focal figure and other
sensations as forming a background for the figure on which we focus.
Other Gestalt principles include figure-ground perception, proximity, similarity, continuity, closure, and symmetry (Figure 3.18; see also Table 3.2). Each of these principles supports the overarching law of Prägnanz. Each illustrates how we tend to
(a) Proximity
(b) Similarity
(c) Continuity
x
o
x
o
x
o
x
o
x
o
x
o
x
o
x
o
(d) Closure
(e) Symmetry
{[]}<()>
Figure 3.18
The Gestalt Principles of Form Perception.
The Gestalt principles of form perception include perception of figure-ground, (a) proximity, (b) similarity, (c) continuity,
(d) closure, and (e) symmetry. Each principle demonstrates the fundamental law of law of Prägnanz, which suggests
that through perception, we unify disparate visual stimuli into a coherent and stable whole.
CHAPTER 3 • Visual Perception
perceive visual arrays in ways that most simply organize the disparate elements into a
stable and coherent form. Stop for a moment and look at your environment. You
will perceive a coherent, complete, and continuous array of figures and background.
You do not perceive holes in objects where your textbook covers up your view of
them. If your book obscures part of the edge of a table, you still perceive the table
as a continuous entity. In viewing the environment, we tend to perceive groupings.
We see groupings of nearby objects (proximity) or of like objects (similarity). We
also perceive objects as complete even though we may only see a part of them (closure), continuous lines rather than broken ones (continuity), and symmetrical patterns rather than asymmetrical ones.
Let’s have a closer look at some of the Gestalt principles. Consider what happens when you walk into a familiar room. You perceive that some things stand out
(e.g., faces in photographs or posters). Others fade into the background (e.g., undecorated walls and floors). A figure is any object perceived as being highlighted. It is
almost always perceived against or in contrast to some kind of receding, unhighlighted (back)ground. Figure 3.19 (a) illustrates the concept of figure-ground—
(a)
Courtesy of Kaiser Porcelain, Ltd.
114
(b)
Figure 3.19
The Figure-Ground Effect.
In these two Gestalt images, (a) and (b), find which is the figure and which is the ground.
Perception of Objects and Forms
115
what stands out from, versus what recedes into, the background. You probably first
will notice the light-colored lettering of the word figure. We perceive this lightcolored lettering as the figure against the darker ground. But if you take a closer
look, you can see that the darker surrounding actually depicts the word “ground.”
Similarly, in Figure 3.19 (b), you can see either a white vase against a black background or two silhouetted faces peering at each other against a white ground. It is virtually impossible to see both sets of objects simultaneously. Although you may switch
rapidly back and forth between the vase and the faces, you cannot see them both at
the same time. One of the reasons suggested as to why each figure makes sense is that
both figures conform to the Gestalt principle of symmetry. Symmetry requires that features appear to have balanced proportions around a central axis or a central point.
People tend to use Gestalt principles even when they are confronted with novel
stimuli. Palmer (1977) showed participants novel geometric shapes that served as targets. He then showed them fragments of the shapes. For each fragment, the participants had to say whether it was part of the original novel geometric shape.
Participants were quicker to recognize the fragments as part of the original target if
they conformed to Gestalt principles. For example, a triangle exhibits closure, in that
its three sides form a complete, closed object. A triangle was recognized more quickly
as part of the original novel figure than were three line segments that were comparable
to the triangle except that they were not closed. They thus did not conform to the
Gestalt principle. In sum, we seem to use Gestalt principles in our everyday perception.
We use them, whether the figures to which we apply the principles are familiar or not.
Table 3.2
Gestalt Principles of Visual Perception
The Gestalt principles of proximity, similarity, continuity, closure, and symmetry aid in our perception of forms.
Gestalt Principles
Principle
Figure Illustrating the Principle
Figure-ground
When perceiving a visual field, some objects (figures) seem prominent, and other
aspects of the field recede into the background (ground).
Figure 3.19 shows a figure-ground vase, in which
one way of perceiving the figures brings one perspective or object to the fore, and another way of
perceiving the figures brings a different object or
perspective to the fore and relegates the former
foreground to the background.
Proximity
When we perceive an assortment of objects, we tend to see objects that are close
to each other as forming a group.
In Figure 3.18 (a), we tend to see the middle four
circles as two pairs of circles.
Similarity
We tend to group objects on the basis of
their similarity.
In Figure 3.18 (b), we tend to see four columns of xs
and os, not four rows of alternating letters.
Continuity
We tend to perceive smoothly flowing or
continuous forms rather than disrupted or
discontinuous ones.
Figure 3.18 (c) shows two fragmented curves bisecting, which we perceive as two smooth curves,
rather than as disjointed curves.
Closure
We tend to perceptually close up, or complete, objects that are not, in fact,
complete.
Figure 3.18 (d) shows only disjointed, jumbled line
segments, which you close up to see a triangle and
a circle.
Symmetry
We tend to perceive objects as forming
mirror images about their center.
For example, when viewing Figure 3.18 (e), a configuration of assorted brackets, we see the assortment as forming four sets of brackets, rather than
eight individual items, because we integrate the
symmetrical elements into coherent objects.
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CHAPTER 3 • Visual Perception
(a)
Figure 3.20
(b)
Ebbinghaus Illusion.
Guess which center circle is larger (a or b) and then measure the diameter of each one.
The Gestalt principles of form perception are remarkably simple. Yet they characterize much of our perceptual organization (Palmer, 1992). Even young infants organize visual stimuli by means of the Gestalt law of proximity (Quinn, Bhatt, &
Hayden, 2008). Interestingly, the Gestalt principles appear to apply only to humans
and not to other primates. An experiment by Parron and Fagot (2007) showed
that only humans misjudged the size of the central circle in the Ebbinghaus illusion
(Figure 3.20), whereas baboons did not. Maybe this difference is because a result of
humans’ paying more attention to the surrounding stimuli, whereas baboons concentrated their attention on the central circle.
The Gestalt principles provide valuable descriptive insights into form and pattern perception. But they offer few or no explanations of these phenomena. To
understand how or why we perceive forms and patterns, we need to consider explanatory theories of perception.
Recognizing Patterns and Faces
How do we recognize patterns when we look at objects? And are faces a special form
of pattern, or is there a special mechanism just for faces? In the next section we explore these and other questions.
Two Different Pattern Recognition Systems
Martha Farah suggests that humans have two systems for recognizing patterns (Farah,
1992, 1995; Farah et al., 1998). The first system specializes in recognition of parts of
objects and in assembling those parts into distinctive wholes (feature analysis system).
For example, when you are in a biology class and notice the elements of a tulip—the
stamen, the pistil, and so forth—you look at the flower through this first system. The
second system (configurational system) specializes in recognizing larger configurations.
It is not well equipped to analyze parts of objects or the construction of the objects.
But it is especially well equipped to recognize configurations. For example, if you
look at a tulip in a garden and admire its distinctive beauty and form, you look at
the flower through the second system.
Perception of Objects and Forms
117
The second system is most relevant to the recognition of faces. When you spot a
friend whom you see on a daily basis, you recognize him or her using the configurational system. So dependent are you on this system in everyday life that you might
not even notice some major change in your friend’s appearance, such as his or her
having longer hair or having put on new glasses.
The feature analysis system can also be used in face recognition. Suppose you see
someone whose face looks vaguely familiar, but you are not sure who it is. You start
analyzing features and then realize it is a friend you have not seen for 10 years. In
this case, you were able to make the facial recognition only after you analyzed the
face by its features. In the end, both configurational and feature analysis may help in
making difficult recognitions and discriminations.
Face recognition occurs, at least in part, in the fusiform gyrus of the temporal
lobe (Gauthier et al., 2003; Kanwisher, McDermott, & Chun, 1997; Tarr & Cheng,
2003). This brain area responds intensely when we look at faces but not when we
look at other objects. There is good evidence that there is something special about
recognition of faces, even from an early age. For example, infants track movements
of a photograph of a human face more rapidly than they track movements of stimuli
of similar complexity that are not, however, faces (Farah, 2000a). In one study, experimental participants were shown sketches of two kinds of objects, faces, and
houses (Farah et al., 1998). In each case, the face was paired with the name of the
person whom the face represented and the house was paired with the name of
the house owner. There were six pairings per trial. After learning the six pairings,
Isolated-part condition
Whole-object condition
Percent correct
80%
70%
60%
Faces
Figure 3.21
Houses
Recognition of Faces and Houses.
People have more trouble recognizing parts of faces than whole faces. They recognize parts of houses about as well as
they recognize whole houses, however.
Source: From J. W. Tanaka and M. J. Farah, “Parts and Wholes in Face Recognition,” Quarterly Journal of Experimental Psychology, 46A,
pp. 225–245, Fig. 6. Reprinted by permission of the Experimental Psychology Society.
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CHAPTER 3 • Visual Perception
© George Doyle/Stockbyte/Getty Images
participants were asked to recognize parts of either the faces or the houses or to recognize the faces or houses as a whole. For example, they might see just a nose or ear,
or just a window or a doorway. Or they might see a whole face or house.
If face recognition is somehow special and especially dependent on the second,
configurational system, then people should have more difficulty recognizing parts of
faces than parts of houses. And this is what the data showed (Figure 3.21): People
generally were better at recognizing houses, whether they were presented in parts or
in wholes. But more importantly, people had relatively more difficulty in recognizing
parts of faces than they had in recognizing whole faces. In contrast, they recognized
parts of houses just as well as whole houses. Face recognition, therefore, appears to be
special. Presumably, it is especially dependent on the configurational system.
An interesting example of a configurational effect in face recognition occurs
when people stare at distorted faces. If you stare at a distorted face for a while and
then stare at a normal face, the normal face will look distorted in the opposite
direction.
When you look at the faces in Figure 3.22, you will notice that the face in the
middle looks normal, whereas the faces to the right and left are gradually more distorted. If you stare at the face to the very left, where the eyes are too close together,
for example, and then look back to the normal face in the middle, the eyes in that
face will appear too far apart (Leopold et al., 2001; Webster et al., 2004; Zhao &
Chubb, 2001). Your knowledge of faces normally tells you what is a normal face
and what is a distorted one, but in this case, that knowledge is very briefly overridden by your having accustomed yourself to the distorted face.
Cognitive processing of faces and the emotions of the face can interact. Indeed,
there is some evidence of an age-related “face positivity” effect. In one study, older
but not younger adults were found to show a preference for looking at happy faces
and away from sad or angry faces (Isaacowitz et al., 2006a, 2006b). Furthermore,
happy faces are rated as more familiar than are either neutral or negative faces
(Lander & Metcalfe, 2007). But can you choose to ignore the emotion that another
person is displaying? Studies indicate that, at least in the case of some negative emotions, like fear, your amygdala processes the emotion automatically, at least when
you do not have to pay much attention to anything else. It is also possible that there
is a difference between highly anxious and less anxious individuals: Highly anxious
people’s amygdalas always process fear automatically, but less anxious people’s do not
(Palermo & Rhodes, 2007).
Figure 3.22 Normal and Distorted Faces.
Normal (center) and distorted faces.
Perception of Objects and Forms
119
IN THE LAB OF MARVIN CHUN
What Happens to Unattended
Information?
subliminal processing during the attentional blink. FMRI can directly probe how
information is processed in different brain
Apollo Robins, the gentleman thief, can
areas, even when subjects cannot report
pick your pockets clean without your nothem. A region of the brain called the
ticing it, even after telling you that he will
parahippocampal gyrus is devoted to
be stealing from you, or even if you are
scene processing; this “place area” is
on security detail for the Secret Service.
more active when scenes are viewed.
Magicians and illusionists are not just deft
Our experiment presented scenes as
MARVIN CHUN
with their hands, but have the more magisecond targets to be missed during the atcal ability to control your attention. Because perception
tentional blink. First, we measured the fMRI signal in the
is a construction of the mind, whoever can control your
place area to scenes that were presented and conattention governs what you perceive. Most of we see,
sciously detected by the subject (the experiment was
hear, feel, smell, taste, and even remember depends on
designed so that about half would be detected probawhat we select and attend to. Unattended information
bilistically). We also measured the lower boundary of
slips by—gorillas go unnoticed, pockets get picked, or
activity in the place area for trials when no scenes were
traffic signals missed by distracted observers focused
presented.
elsewhere. What happens to the rivers of unattended
The focus of the study was then to ask how the
information that pass by us all the time? My laboratory
place area responds to scenes that were missed.
uses both behavioral methods and functional magnetic
When subjects said they could not see the scene, did
resonance imaging to study the fate of unattended, igthe place area unconsciously see the scene? If so, the
nored events.
fMRI signal in the place area to unseen scenes should
Consider a lab task of searching for two letters
be higher than the lower bound baseline when no
among digits presented sequentially at a blindingly
scene was presented. Indeed, the place area produced
fast rate of 10 items per second, MTV style. People
significantly higher fMRI signals, suggesting that sublimhave a fleeting sense of what’s going by and can
inal perception occurs to a high level (scene detection),
pick out the first letter around 90% of the time. Howand that fMRI can be used to measure such unconever, if the second letter appears about 200–300 milliscious processing (Marois et al., 2004).
seconds after the first letter target, it is missed up to 70%
Attention modulates not just ongoing perception,
of the time. This phenomenon, known as the attentional
but also your ability to remember. Simply looking at or
blink (Raymond et al., 1992), is a form of inattentional
reading something does not ensure you will encode it,
blindness that highlights fundamental limitations regardas you may know all too well while studying for exams.
ing how much you can attend.
You must attend to the information you’re trying to learn,
But what happens to the missed target? We proor memory traces of the information will not be formed
posed that missed targets are identified, but then get
reliably in brain circuits important for memory. In fact,
lost or forgotten while waiting for the first target to be
using fMRI we demonstrated that attention is important
encoded (Chun and Potter, 1995, JEP:HPP). However,
both during encoding and when trying to retrieve inforit was difficult to prove unconscious identification with
mation (Yi and Chun, 2005). Unfortunately, for stubehavioral methods alone. Hence, we used functional
dents, learning without attention seems unlikely!
magnetic resonance imaging (fMRI) to investigate
The Neuroscience of Recognizing Faces and Patterns
There is evidence that emotion increases activation within the fusiform gyrus when
people are processing faces. In one study, participants were shown a face and asked
either to name the person or to name the expression. When asked to name the
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CHAPTER 3 • Visual Perception
expression, participants show increased activation of the fusiform gyrus compared
with when the participants were asked to name the person (Ganel et al., 2005). Examination of patients with autism provides additional evidence for the processing of
emotion within the fusiform gyrus. Patients with autism have impaired emotional
recognition. Scanning the brains of persons with autism reveals that the fusiform
gyrus is less active than in nonautistic populations.
Patients with autism can learn to identify emotions through an effortful process.
However, this training does not allow identification of emotion to become an automatic process in this population, nor does it increase the activation within the fusiform gyrus (Bolte et al., 2006; Hall, Szechtman, & Nahmias, 2003).
Researchers do not all agree that the fusiform gyrus is specialized for face perception, in contrast to other forms of perception. Another point of view is that this area
is that of greatest activation in face perception, but that other areas also show activation, but at lower levels. Similarly, this or other brain areas that respond maximally to faces or anything else may still show some activation when perceiving
other objects. In this view, areas of the brain are not all-or-none in what they perceive, but rather, may be differentially activated, in greater or lesser degrees, depending on what is perceived (Haxby et al., 2001; Haxby, Gobbini, & Montgomery,
2004; O’Toole et al., 2005).
Another theory concerning the role of the fusiform gyrus is called the expertindividuation hypothesis. According to this theory, the fusiform gyrus is activated
when one examines items with which one has visual expertise. Imagine that you
are an expert on birds and spend much of your time studying birds. It is expected
that you could differentiate among very similar birds and would have much practice
at such differentiation. As a result, if you are shown five robins, you would likely be
able to tell birds apart. It is unlikely that a person without this expertise could discern among these birds. If your brain were scanned during this activity, activation in
the fusiform gyrus, specifically the right one, would be seen. Such activation is seen
in persons who are experts concerning cars and birds. Even when people are taught
to differentiate among very similar abstract figures, activation of the fusiform gyrus is
observed (Gauthier et al., 1999, 2000; Rhodes et al., 2004; Xu, 2005). This theory is
able to account for the activation of the fusiform gyrus when people view faces because we are, in effect, experts at identifying and examining faces.
n BELIEVE IT OR NOT
DO TWO DIFFERENT FACES EVER LOOK
THE
SAME
TO
YOU?
Have you ever noticed that it is easier to recognize faces
of people that belong to your own ethnic group? For
example, if you are of African-American descent, it is
likely easier for you to recognize and differentiate between black faces than between white or Asian faces.
Maybe you thought that this is just because you are more
familiar with the faces you happen to see most often
around you and that it is this familiarity that makes it
easier for you to discriminate faces that are similar to
your own. But now imagine being told you have a
“red” personality. Do you think knowing this would
make it easier for you to recognize people who also
have a “red” personality as opposed to a “green” personality (even if they all are of the same race)? Studies
have shown that indeed social categorization plays a
role in how easy it is for you to recognize faces. As
soon as you perceive somebody as an out-group member, it will be harder for you to recognize that person’s
face. This effect is so stable that it can be elicited by
imaginary differences like “red” or “green” personalities,
or just by adding an African-American or Latino hairdo to
a white face (Bernstein et al., 2007; MacLin & Malpass,
2001, 2003; Ge et al., 2009).
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121
Prosopagnosia—the inability to recognize faces—would imply damage of some
kind to the configurational system (Damasio, Tranel, & Damasio, 1990; De Renzi
Faglioni, Grossi, & Nichelli, 1991; Farah, 2004). Somebody with prosopagnosia
can see the face of another person and even recognize if that person is sad, happy,
or angry. But what he fails to recognize is whether that person being observed is a
stranger, his friend, or his own mother. The ability to recognize faces is especially
influenced by lesions of the right fusiform gyrus, either unilateral or bilateral. Facial
memories are affected, in particular, when the bilateral lesions include the right
anterior temporal lobe (Barton, 2008).
Other disabilities, such as an early reading disability in which a beginning reader
has difficulty in recognizing the features that comprise unique words, might stem
from damage to the first, element-based system. Moreover, processing can move
from one system to another. A typical reader may learn the appearances of words
through the first system—element by element—and then come to recognize the
words as wholes. Indeed, some forms of reading disability might stem from the inability of the second system to take over from the first.
CONCEPT CHECK
1. What are the major Gestalt principles?
2. What is the “recognition by components” theory?
3. What is the difference between top-down and bottom-up theories of perception?
4. What is the difference between viewer-centered and object-centered perception?
5. What is prosopagnosia?
The Environment Helps You See
As we have seen, perceptual processes are not so easily completed that the image on
your retina can be taken as is without further interpretation. Our brain needs to interpret the stimuli it receives and make sense of them. The environment provides
cues that aid in the analysis of the retinal image and facilitate the construction of a
perception that is as close as possible to what is out there in the world—at least, to
the extent we can ascertain what is out there! The following part of this chapter
explains how we use environmental cues to perceive the world.
Perceptual Constancies
Picture yourself walking to your cognitive psychology class. Two students are standing outside the classroom door. They are chatting as you approach. As you get closer
to the door, the amount of space on your retina devoted to images of those students
becomes increasingly large. On the one hand, this proximal sensory evidence suggests that the students are becoming larger. On the other hand, you perceive that
the students have remained the same size. Why?
The perceptual system deals with variability by performing a rather remarkable
analysis regarding the objects in the perceptual field. Your classmates’ perceived constancy in size is an example of perceptual constancy. Perceptual constancy occurs
when our perception of an object remains the same even when our proximal
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CHAPTER 3 • Visual Perception
sensation of the distal object changes (Gillam, 2000). The physical characteristics of
the external distal object are probably not changing. But because we must be able to
deal effectively with the external world, our perceptual system has mechanisms that
adjust our perception of the proximal stimulus. Thus, the perception remains constant although the proximal sensation changes. Here we consider two of the main
constancies: size and shape constancies.
Size constancy is the perception that an object maintains the same size despite
changes in the size of the proximal stimulus. The size of an image on the retina depends directly on the distance of that object from the eye. The same object at two
different distances projects different-sized images on the retina. Some striking illusions can be achieved when our sensory and perceptual systems are misled by the
very same information that usually helps us to achieve size constancy.
An example of size constancy is the Müller-Lyer illusion (Figure 3.23). Here,
two line segments that are of the same length appear to be of different lengths. We
use shapes and angles from our everyday experience to draw conclusions about the
relative sizes of objects. Equivalent image sizes at different depths usually indicate
different-sized objects.
Studies indicate that the right posterior parietal cortex (involved in the manipulation of mental images) and the right temporo-occipital cortex are activated when people
are asked to judge the length of the lines in the Müller-Lyer illusion. The strength of the
illusion can be changed by adjusting the angles of the arrows that delimit the horizontal
line—the sharper the angles, the more pronounced the illusion. The strength of the illusion is associated with bilateral (on both sides) activation in the lateral (i.e., located on
the side of) occipital cortex and the right superior parietal cortex. As the right intraparietal sulcus (furrow) is activated as well, it seems like there is an interaction of the illusory
information with the top-down processes in the right parietal cortex that are responsible
for visuo-spatial judgments (Weidner & Fink, 2007).
(a)
(b)
(c)
(d)
Figure 3.23 The Müller-Lyer Illusion.
In this illusion, we tend to view two equally long line segments as being of different lengths. The vertical line segments
in panels (a) and (c) appear shorter than the line segments in panels (b) and (d), although they are the same size.
Oddly enough, we are not certain why such a simple illusion occurs. Sometimes, the illusion we see in the abstract line
segments (panels (a) and (b)) is explained in terms of the diagonal lines at the ends of the vertical segments which may
be implicit depth cues similar to the ones we would see in our perceptions of the exterior and interior of a building
(panels (c) and (d)) (Coren & Girgus, 1978).
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123
Finally, compare the two center circles in the pair of circle patterns in Figure 3.20.
Both center circles are actually the same size. But the size of the center circle relative
to the surrounding circles affects perception of the center circle’s size.
Like size constancy, shape constancy relates to the perception of distances but in
a different way. Shape constancy is the perception that an object maintains the same
shape despite changes in the shape of the proximal stimulus (Figure 3.24). An object’s perceived shape remains the same despite changes in its orientation and hence
Figure 3.24
Shape Constancy.
Here, you see a rectangular door and door frame, showing the door as closed, slightly
opened, more fully opened, or wide open. Of course, the door does not appear to be a different shape in each panel. Indeed, it would be odd if you perceived a door to be changing
shapes as you opened it. Yet, the shape of the image of the door sensed by your retinas does
change as you open the door. If you look at the figure, you will see that the drawn shape of
the door is different in each panel.
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CHAPTER 3 • Visual Perception
in the shape of its retinal image. As the actual shape of the pictured door changes,
some parts of the door seem to be changing differentially in their distance from us. It
is possible to use neuropsychological imaging to localize parts of the brain that are
used in this shape analysis. They are in the extrastriate cortex (Kanwisher et al.,
1996, 1997). Points near the outer edge of the door seem to move more quickly toward us than do points near the inner edge. Nonetheless, we perceive that the door
remains the same shape.
Depth Perception
Consider what happens when you reach for a cup of tea, or throw a baseball. You
must use information regarding depth. Depth is the distance from a surface, usually
using your own body as a reference surface when speaking in terms of depth perception. This use of depth information extends beyond the range of your body’s reach.
When you drive, you use depth to assess the distance of an approaching automobile.
When you decide to call out to a friend walking down the street, you determine how
loudly to call. Your decision is based on how far away you perceive your friend to be.
How do you manage to perceive 3-D space when the proximal stimuli on your retinas comprise only a 2-D projection of what you see? You have to rely on depth cues.
The next section explores what depth cues are and how we use them.
Depth Cues
Look at the impossible configurations in Figure 3.25. They are confusing because
there is contradictory depth information in different sections of the picture. Small
segments of these impossible figures look reasonable to us because there is no inconsistency in their individual depth cues (Hochberg, 1978). However, it is difficult to
make sense of the figure as a whole. The reason is that the cues providing depth
information in various segments of the picture are in conflict.
Generally, depth cues are either monocular (mon-, “one”; ocular, “related to the
eyes”) or binocular (bin-, “both,” “two”). Monocular depth cues can be represented
in just two dimensions and observed with just one eye. Figure 3.26 illustrates several
of the monocular depth cues defined in Table 3.3. They include texture gradients,
relative size, interposition, linear perspective, aerial perspective, location in the picture plane, and motion parallax. Before you read about the cues in either the table
or the figure caption, look just at the figure. See how many depth cues you can decipher simply by observing the figure carefully.
Table 3.3 also describes motion parallax, the only monocular depth cue not
shown in the figure. Motion parallax requires movement. It thus cannot be used
Figure 3.25
Impossible Figures.
What cues may lead you to perceive these impossible figures as entirely plausible?
The Environment Helps You See
125
to judge depth within a stationary image, such as a picture. Another means of
judging depth involves binocular depth cues, based on the receipt of sensory
information in three dimensions from both eyes (Parker, Cumming, & Dodd,
2000). Table 3.3 also summarizes some of the binocular cues used in perceiving
depth.
Binocular depth cues use the relative positioning of your eyes. Your two eyes are
positioned far enough apart to provide two kinds of information to your brain: binocular disparity and binocular convergence. In binocular disparity, your two eyes send
increasingly disparate (differing) images to your brain as objects approach you. Your
brain interprets the degree of disparity as an indication of distance from you. In addition, for objects we view at relatively close locations, we use depth cues based on
binocular convergence. In binocular convergence, your two eyes increasingly turn
Image not available due to copyright restrictions
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CHAPTER 3 • Visual Perception
Table 3.3
Monocular and Binocular Cues for Depth Perception
Various perceptual cues aid in our perception of the 3-D world. Some of these cues can be observed by one eye
alone; other cues require the use of both eyes.
Cues for Depth Perception
Appears Closer
Appears Farther Away
Texture gradients
Larger grains, farther apart
Smaller grains, closer together
Relative size
Bigger
Smaller
Interposition
Partially obscures other object
Is partially obscured by other object
Linear perspective
Apparently parallel lines seem to diverge
as they move away from the horizon
Apparently parallel lines seem to converge
as they approach the horizon
Aerial perspective
Images seem crisper, more clearly
delineated
Images seem fuzzier, less clearly delineated
Location in the picture
plane
Above the horizon, objects are higher in
the picture plane; below the horizon,
objects are lower in the picture plane
Above the horizon, objects are lower in the
picture plane; below the horizon, objects
are higher in the picture plane
Motion parallax
Objects approaching get larger at an everincreasing speed (i.e., big and moving
quickly closer)
Objects departing get smaller at an everdecreasing speed (i.e., small and moving
slowly farther away)
Binocular convergence
Eyes feel tug inward toward nose
Eyes relax outward toward ears
Binocular disparity
Huge discrepancy between image seen
by left eye and image seen by right eye
Minuscule discrepancy between image seen
by left eye and image seen by right eye
Monocular Depth Cues
Binocular Depth Cues
inward as objects approach you. Your brain interprets these muscular movements as
indications of distance from you.
In about 8% of people whose eyes are not aligned properly (strabismic eyes),
depth perception can occur even with just one eye. Usually people with strabismic
eyes have a sensitive zone in their retina other than the fovea that captures a part
of the space that should have been captured were the eyes properly aligned. This
capacity normally goes along with a partial inhibition of signals from the fovea.
If the fovea stays sensitive, however, those people produce double images,
which can be fused and result in stereoscopic vision with just one eye (Rychkova &
Ninio, 2009).
Depth perception may depend upon more than just the distance or depth at
which an object is located relative to oneself. The perceived distance to a target
is influenced by the effort required to walk to the location of the target (Proffitt
et al., 2003, 2006). People with a heavy backpack perceive the distance to a target location as farther than those not wearing a heavy backpack. In other words,
there can be an interaction between the perceptual result and the perceived
effort required to reach the object perceived (Wilt, Proffitt, & Epstein, 2004).
The more effort one requires to reach something, the farther away it is perceived
to be.
Depth perception is a good example of how cues facilitate our perception. When
we see an object that appears small, there is no automatic reason to believe it is
Deficits in Perception
127
INVESTIGATING COGNITIVE PSYCHOLOGY
Binocular Depth Cues
You can test the differing perspectives in binocular disparity by holding your finger about
an inch from the tip of your nose. Look at it first with one eye covered, then the other: It
will appear to jump back and forth. Now do the same for an object 20 feet away, then
100 yards away. The apparent jumping, which indicates the amount of binocular disparity, will decrease with distance. Your brain interprets the information regarding disparity as a cue indicating depth.
farther away. Rather, the brain uses this contextual information to conclude that the
smaller object is farther away.
The Neuroscience of Depth Perception
Figure 3.27 illustrates how binocular disparity and binocular convergence work. The
brain contains neurons that specialize in the perception of depth. These neurons are,
as one might expect, referred to as binocular neurons. The neurons integrate incoming information from both eyes to form information about depth. The binocular
neurons are found in the visual cortex (Parker, 2007).
Research on both nonhuman animals and humans has shown that visual shape
is processed in the ventral visual stream as well as important visual areas such as the
lateral occipital cortex and the ventral temporal cortex. After the initial processing
in the primary visual cortex, moving 3-D shapes are processed in the human motion
complex (hMT), an area that is concerned with motion processing. Next to be processed are depth and shape information. This processing occurs mainly in the V5
region of the visual cortex; the medial parietal cortex may also participate in the
processing to some extent. In the next step, different features of the stimulus are analyzed in the lateral occipital cortex in order to infer the shape from the moving
object. The shape that was inferred is then compared with the shape representation
in the ventral occipital and ventral temporal areas of the cortex. The process ends
with activation in the parietal cortex and primary visual cortex which suggests that
the parietal cortex is involved in top-down processes that influence the areas in the
primary visual cortex where the visual stimuli are being processed in the beginning
(Jiang et al., 2008; Orban et al., 2003).
Deficits in Perception
Clearly, cognitive psychologists learn a great deal about normal perceptual processes
by studying perception in normal participants. However, we also often gain understanding of perception by studying people whose perceptual processes differ from the
norm (Farah, 1990; Weiskrantz, 1994).
Agnosias and Ataxias
Perceptual deficits provide an excellent way to test hypotheses with regard to how
the perceptual system works. Remember that there are two distinct visual pathways,
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CHAPTER 3 • Visual Perception
Left
eye
view
Distant object
Left
eye
view
Right
eye
view
Right
eye
view
Muscle commands
(strong)
Neural signals
(strong)
close
Muscle commands
(weak)
Neural signals
(weak)
distant
Images go to brain where
they are compared
(a) Binocular disparity
(b) Binocular convergence
Figure 3.27 Binocular Disparity and Convergence.
(a) Binocular disparity: The closer an object is to you, the greater the disparity between the views of it as sensed in
each of your eyes. (b) Binocular convergence: Because your two eyes are in slightly different places on your head,
when you rotate your eyes so that an image falls directly on the central part of your eye, in which you have the greatest
visual acuity, each eye must turn inward slightly to register the same image. The closer the object you are trying to see,
the more your eyes must turn inward. Your muscles send messages to your brain regarding the degree to which your
eyes are turning inward, and these messages are interpreted as cues indicating depth.
one for identifying objects (“what”), the other for pinpointing where objects are
located in space and how to manipulate them (“where” or “how”).
The what/how hypothesis is best supported by evidence of processing deficits:
There are both deficits that impair people’s ability to recognize what they see, and
deficits that impair people’s ability to reach for what they see (how).
Difficulties Perceiving the “What”
Consider first the “what.” People who suffer from an agnosia have trouble to perceive sensory information (Moscovitch, Winocur, & Behrmann, 1997). Agnosias
Deficits in Perception
129
often are caused by damage to the border of the temporal and occipital lobes (Farah,
1990, 1999) or restricted oxygen flow to areas of the brain, sometimes as a result of
traumatic brain injury (Zoltan, 1996). There are many kinds of agnosias. Not all of
them are visual. Here we focus on a few specific inabilities to see forms and patterns
in space.
Generally, people with agnosia have normal sensations of what is in front of
them. They can perceive the colors and shapes of objects and persons but they cannot recognize what the objects are—they have trouble with the “what” pathway.
People who suffer from visual-object agnosia can see all parts of the visual field,
but the objects they see do not mean anything to them (Kolb & Whishaw, 1985).
For example, one agnosic patient, on seeing a pair of eyeglasses, noted first that
there was a circle, then that there was another circle, then that there was a crossbar,
and finally guessed that he was looking at a bicycle. A bicycle does, indeed, comprise two circles and a crossbar (Luria, 1973).
Disturbance in the temporal region of the cortex can lead to simultagnosia. In
simultagnosia, an individual is unable to pay attention to more than one object at
a time. A person with simultagnosia would not see each of the objects depicted in
Figure 3.28. Rather, the person might report seeing the hammer but not the other
objects (Williams, 1970).
Prosopagnosia results in a severely impaired ability to recognize human faces
(Farah et al., 1995; Feinberg et al., 1994; McNeil & Warrington, 1993; Young,
2003). A person with prosopagnosia might not recognize her or his own face in the
mirror. This fascinating disorder has spawned much research on face identification, a
“hot topic” in visual perception (Damasio, 1985; Farah et al., 1995; Farah, Levinson,
& Klein, 1995; Haxby et al., 1996). The functioning of the right-hemisphere fusiform gyrus is strongly implicated in prosopagnosia. In particular, the disorder is associated with damage to the right temporal lobe of the brain. Prosopagnosia, in
particular, and agnosia, in general, are obstacles that persist over time. In one
particular case, a woman who sustained carbon-monoxide toxicity began to suffer
from agnosia, including prosopagnosia. After 40 years, this woman was reevaluated
Figure 3.28
Simultagnosia.
When you view this figure, you see various objects overlapping. People with simultagnosia
cannot see more than one of these objects at any one time.
Source: From Sensation and Perception by Stanley Coren and Lawrence M. Ward, copyright © 1989 by Harcourt
Brace & Company. Reproduced by permission of the publisher.
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CHAPTER 3 • Visual Perception
and still demonstrated these deficits. These findings reveal the lasting nature of agnosia (Sparr et al., 1991).
Difficulties in Knowing the “How”
A different kind of perceptual deficit is associated with damage to the “how” pathway. This deficit is optic ataxia, which is an impairment in the ability to use the
visual system to guide movement (Himmelbach & Karnath, 2005). People with
this deficit have trouble reaching for things. All of us have had the experience of
coming home at night and trying to find the keyhole in the front door. It’s too
dark to see, and we have to grope with our key for the keyhole, often taking quite
a while to find it. Someone with optic ataxia has this problem even with a fully lit
visual field. The “how” pathway is impaired.
Ataxia results from a processing failure in the posterior parietal cortex, where
sensorimotor information is processed. It is assumed that higher order processes are
involved because most patients’ disorders are complex and they can indeed grasp
objects under certain circumstances (Jackson et al., 2009). People with ataxia can
improve their movements toward a visible aim when they hold off with their movements for a few seconds. Immediate movements are executed through dorsal-stream
processing, while delayed movements make use of the ventral system, comprising the
occipito-temporal and temporo-parietal areas (Milner et al., 2003; Milner & Goodale, 2008; Himmelbach et al., 2009).
Are Perceptual Processes Independent of Each Other?
When we consider the different kinds of perceptual deficits, it is stunning to see how
specific they are. Some people cannot name colors; others cannot recognize movement or faces. Others can see a mug on the table in front of them, yet cannot grasp
the mug. This kind of extreme specificity of deficits leads to questions about specialization (modular processes). Specifically, are there distinct processing centers or
modules for particular perceptual tasks, such as for color or face recognition? This
question goes beyond the separation of perceptual processes along different sensory
modalities (e.g., the differences between visual and auditory perception). Modular
processes are those that are specialized for particular tasks. They may involve only
visual processes (as in color perception), or they may involve an integration of visual
and auditory processes (as in certain aspects of speech perception that are discussed
in Chapter 10). For face perception (or any perceptual process) to be considered a
truly modular process, we would need to have further evidence that the process is
domain-specific and therefore only uses specific kinds of information, and that information does not freely flow across different modules. That is, other perceptual processes should not contribute to, interfere with, or share information with face
perception.
Anomalies in Color Perception
Color perception deficits are much more common in men than in women, and they
are genetically linked. However, they can also result from lesions to the ventromedial occipital and temporal lobes.
There are several kinds of color deficiency, which are sometimes referred to as
kinds of “color blindness.” Least common is rod monochromacy, also called achromacy. People with this condition have no color vision at all. It is thus the only
Why Does It Matter? Perception in Practice
131
true form of pure color blindness. People with this condition have cones that are
nonfunctional. They see only shades of gray, as a function of their vision through
the rods of the eye.
Most people who suffer from deficits in color perception can still see some color,
despite the name “color blindness.” In dichromacy, only two of the mechanisms for
color perception work, and one is malfunctioning. The result of this malfunction is
one of three types of color blindness (color-perception deficits). The most common
is red-green color blindness. People with this form of color-blindness have difficulty in distinguishing red from green, although they may be able to distinguish,
for example, dark red from light green (Visual disabilities: Color-blindness, 2004).
The extreme form of red-green color blindness is called protanopia. The other types
of color blindness are: deuteranopia (trouble seeing greens), and tritanopia (blues
and greens can be confused, but yellows also can seem to disappear or to appear
as light shades of reds).
See the companion website for a picture showing a rainbow as seen by a person
with normal color vision and by persons suffering from the three kinds of
dichromacy.
CONCEPT CHECK
1. What is shape constancy?
2. What are the main cues for depth perception?
3. What is visual agnosia?
4. To what does “modularity” refer?
5. What is the difference between monochromacy and dichromacy?
Why Does It Matter? Perception in Practice
Perceptual processes and change blindness play a significant role in accidents and
efforts at accident prevention. About 50% of all collision accidents are a result of
missing or delayed perception (Nakayama, 1978). Especially two-wheeled vehicles
are often involved in “looked-but-failed-to-see” accidents, where the driver of the
involved car states that he did indeed look in the direction of the cyclist, but failed
to see the approaching motorcycle. It is possible that drivers develop a certain “scanning” strategy that they use in complex situations, such as at crossroads. The scanning strategy concentrates on the most common and dangerous threats but fails to
recognize small deviations, or more uncommon objects like two-wheeled vehicles.
In addition, people tend to fail to recognize new objects after blinking and saccades
(fast movements of both eyes in one direction).
Generally, people are not aware of the danger of change blindness and believe
that they will be able to see all obstacles when looking in a particular direction
(“change blindness blindness”, Simons & Rensink, 2005; Davis et al., 2008). This
tendency has implications for the education of drivers with regard to their perceptual
abilities. It also has implications for the design of traffic environments, which should
be laid out in a way that facilitates complex traffic flow and makes drivers aware of
unexpected obstacles, like bicycles (Galpin et al., 2009; Koustanai, Boloix, Van
Elslande, & Bastien, 2008).
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CHAPTER 3 • Visual Perception
Key Themes
Several key themes, as outlined in Chapter 1, emerge in our study of perception.
Rationalism versus empiricism. How much of the way we perceive can be understood as due to some kind of order in the environment that is relatively independent of our perceptual mechanisms? In the Gibsonian view, much of what we
perceive derives from the structure of the stimulus, independent of our experience
with it. In contrast, in the view of constructive perception, we construct what we
perceive. We build up mechanisms for perceiving based on our experience with the
environment. As a result, our perception is influenced at least as much by our intelligence (rationalism) as it is by the structure of the stimuli we perceive
(empiricism).
Basic versus applied research. Research on perception has many applications,
such as in understanding how we can construct machines that perceive. The U.S.
Postal Service relies heavily on machines that read zip codes. To the extent that
the machines are inaccurate, mail risks going astray. These machines cannot rely
on strict template matching because people write numbers in different ways. So the
machines must do at least some feature analysis.
Another application of perception research is in human factors. Human-factors
researchers design machines and user interfaces to be user-friendly. An automobile
driver or airplane pilot sometimes needs to make split-second decisions. The cockpits thus must have instrument panels that are well-lit, easy to read, and accessible
for quick action. Basic research on human perception can inform developers what
user-friendly means.
Domain generality versus domain specificity. Perhaps nowhere is this theme
better illustrated than in research on face recognition. Is there something special
about face recognition? It appears so. Yet many of the mechanisms that are used for
face recognition are used for other kinds of perception as well. Thus, it appears that
perceptual mechanisms may be mixed—some general across domains, others specific
to domains such as face recognition.
Summary
1. How can we perceive an object like a chair as
having a stable form, given that the image of
the chair on our retina changes as we look at it
from different directions? Perceptual experience involves four elements: distal object,
informational medium, proximal stimulation,
and perceptual object. Proximal stimulation is
constantly changing because of the variable nature of the environment and physiological processes designed to overcome sensory adaptation.
Perception therefore must address the fundamental question of constancy.
Perceptual constancies (e.g., size and shape
constancy) result when our perceptions of objects tend to remain constant. That is, we see
constancies even as the stimuli registered by our
senses change. Some perceptual constancies
may be governed by what we know about the
world. For example, we have expectations
regarding how rectilinear structures usually appear. But constancies also are influenced by invariant relationships among objects in their
environmental context.
One reason we can perceive 3-D space is the
use of binocular depth cues. Two such cues are
binocular disparity and binocular convergence.
Binocular disparity is based on the fact that
each of two eyes receives a slightly different
image of the same object as it is being viewed.
Binocular convergence is based on the degree
Summary
to which our two eyes must turn inward toward
each other as objects get closer to us. We also
are aided in perceiving depth by monocular
depth cues. These cues include texture gradients, relative size, interposition, linear perspective, aerial perspective, height in the picture
plane, and motion parallax. One of the earliest
approaches to form and pattern perception is
the Gestalt approach to form perception. The
Gestalt law of Prägnanz has led to the explication of several principles of form perception.
These principles include figure-ground, proximity, similarity, closure, continuity, and symmetry. They characterize how we perceptually
group together various objects and parts of
objects.
2. What are two fundamental approaches to explaining perception? Perception is the set of
processes by which we recognize, organize, and
make sense of stimuli in our environment.
It may be viewed from either of two basic theoretical approaches: constructive or directperception. The viewpoint of constructive (or
intelligent) perception asserts that the perceiver
essentially constructs or builds up the stimulus
that is perceived. He or she does so by using
prior knowledge, contextual information, and
sensory information. In contrast, the viewpoint
of direct perception asserts that all the information we need to perceive is in the sensory input
(such as from the retina) that we receive.
An alternative to both these approaches
integrates features of each. It suggests that
perception may be more complex than directperception theorists have suggested, yet perception also may involve more efficient use of
sensory data than constructive-perception theorists have suggested. Specifically, a computational approach to perception suggests that
our brains compute 3-D perceptual models of
the environment based on information from
the 2-D sensory receptors in our retinas.
The main bottom-up theoretical approaches
to pattern perception include templatematching theories and feature-matching
theories. Some support for feature-matching
theories comes from neurophysiological studies
133
identifying what are called “feature detectors”
in the brain. It appears that various cortical
neurons can be mapped to specific receptive
fields on the retina. Differing cortical neurons
respond to different features. Examples of such
features are line segments or edges in various
spatial orientations. Visual perception seems to
depend on three levels of complexity in the
cortical neurons. Each level of complexity
seems to be further removed from the incoming
information from the sensory receptors. Another bottom-up approach, the recognitionby-components (RBC) theory, more specifically delineates a set of features involved in
form and pattern perception.
Bottom-up approaches explain some aspects
of form and pattern perception. Other aspects
require approaches that suggest at least some
degree of top-down processing of perceptual information. For example, top-down approaches
better but incompletely explain such phenomena as context effects, including the objectsuperiority effect and the word-superiority
effect.
3. What happens when people with normal visual sensations cannot perceive visual stimuli?
Agnosias, which are usually associated with
brain lesions, are deficits of form and pattern
perception. They cause afflicted people to be
insufficiently able to recognize objects that are
in their visual fields, despite normal sensory
abilities. People who suffer from visual-object
agnosia can sense all parts of the visual field.
But the objects they see do not mean anything
to them. Individuals with simultagnosia are unable to pay attention to more than one object
at a time. People with spatial agnosia have severe difficulty in comprehending and handling
the relationship between their bodies and the
spatial configurations of the world around
them. People with prosopagnosia have severe
impairment in their ability to recognize human
faces, including their own. These deficits lead
to the question of whether specific perceptual
processes are modular—specialized for particular tasks. Color blindness is another type of perceptual deficit.
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CHAPTER 3 • Visual Perception
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Briefly describe each of the monocular and
binocular depth cues listed in this chapter.
2. Describe bottom-up and top-down approaches
to perception.
3. How might deficits of perception, such as
agnosia, offer insight into normal perceptual
processes?
4. Compare and contrast the Gestalt approach to
form perception and the theory of direct
perception.
5. Design a demonstration that would illustrate the
phenomenon of perceptual constancy.
6. Design an experiment to test the featurematching theory.
7. To what extent does perception involve
learning? Why?
Key Terms
agnosia, p. 128
amacrine cells, p. 93
binocular depth cues, p. 125
bipolar cells, p. 94
bottom-up theories, p. 96
cones, p. 95
constructive perception, p. 107
context effects, p. 109
depth, p. 124
direct perception, p. 97
feature-matching theories, p. 101
figure-ground, p. 114
fovea, p. 93
ganglion cells, p. 93
Gestalt approach to form
perception, p. 113
horizontal cells, p. 93
landmark-centered, p. 112
law of Prägnanz, p. 113
monocular depth cues, p. 124
object-centered representation,
p. 111
optic ataxia, p. 130
optic nerve, p. 93
percept, p. 90
perception, p. 85
perceptual constancy, p. 121
photopigments, p. 94
photoreceptors, p. 94
recognition-by-components
(RBC) theory, p. 106
retina, p. 93
rods, p. 94
templates, p. 99
top-down theories, p. 96
viewer-centered representation,
p. 111
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Mapping the Blind Spot
Receptive Fields
Apparent Motion
Metacontrast Masking
Müller-Lyer Illusion
Signal Detection
Visual Search
Lexical Decision
4
C
H
A
P
T
E
R
Attention and Consciousness
CHAPTER OUTLINE
The Nature of Attention and Consciousness
Attention
Attending to Signals over the Short
and Long Terms
Signal Detection: Finding Important Stimuli
in a Crowd
Vigilance: Waiting to Detect a Signal
Search: Actively Looking
Feature-Integration Theory
Similarity Theory
Guided Search Theory
Neuroscience: Aging and Visual Search
Selective Attention
What Is Selective Attention?
Theories of Selective Attention
Neuroscience and Selective Attention
Divided Attention
Investigating Divided Attention in the Lab
Theories of Divided Attention
Divided Attention in Everyday Life
Factors That Influence Our Ability
to Pay Attention
Neuroscience and Attention: A Network Model
Intelligence and Attention
Inspection Time
Reaction Time
When Our Attention Fails Us
Change Blindness and Inattentional Blindness
Spatial Neglect–One Half of the World
Goes Amiss
Dealing with an Overwhelming World—
Habituation and Adaptation
Automatic and Controlled Processes
in Attention
Automatic and Controlled Processes
How Does Automatization Occur?
Automatization in Everyday Life
Mistakes We Make in Automatic Processes
Consciousness
The Consciousness of Mental Processes
Preconscious Processing
Studying the Preconscious—Priming
What’s That Word Again? The Tipof-the-Tongue Phenomenon
When Blind People Can See
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
Attention Deficit Hyperactivity Disorder (ADHD)
135
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CHAPTER 4 • Attention and Consciousness
Here are some of the questions we will explore in this chapter:
1. Can we actively process information even if we are not aware of doing so? If so, what do we do, and
how do we do it?
2. What are some of the functions of attention?
3. What are some theories cognitive psychologists have developed to explain attentional processes?
4. What have cognitive psychologists learned about attention by studying the human brain?
n BELIEVE IT OR NOT
DOES PAYING ATTENTION ENABLE YOU
TO MAKE BETTER DECISIONS?
So you’ve got an important decision to make? People are
usually taught to deliberate carefully upon the more complex decisions in their lives. Sometimes, however, unconsciously made decisions can be better than carefully
deliberated ones.
Ap Dijksterhuis and colleagues (2006) conducted experiments in which participants had to
choose the best from four cars and other objects like
toothpaste. The complexity of the decision depended
on the number of important attributes that described
the object. Participants were best able to make a simple decision, like the one for toothpaste (which was
based on two attributes), when they deliberated
about their choices. However, when participants
had to choose the best of four cars (described by
12 attributes each), they fared much better when
they were not given the chance consciously to think
about their choices.
Conscious choices can be flawed because we do
not have unlimited mental capacity. At some point, we
have to cut down on the amount of information we will
consider. Also, when consciously thinking about alternatives, we sometimes attach more importance to less relevant attributes, which can lead to suboptimal choices. So
next time you have a complex decision to make, it may be
best to just sit back, relax, and let the decision come to
you. This chapter introduces you to attention and consciousness and how cognitive psychologists approach
them (See also the description of the work of Gerd Gigerenzer on fast and frugal heuristics in Chapter 12).
Let’s examine what it means to pay attention in an everyday situation. Imagine
driving in rush hour, near a major sports stadium where an event is about to start.
The streets are filled with cars, some of them honking. At some intersections the
police are regulating the traffic, but not quite in synchrony with the traffic lights.
This asynchronicity—with the traffic light signaling one thing and the police
signaling another—divides your attention. Some cars are stranded in the middle of
an intersection. Also, there are thousands of people streaming through the streets to
attend the sports event. You need to pay close attention to the traffic light as well as
the officer on the road, the cars passing by, and the pedestrians that might
unexpectedly cross the street. What is it that lets us pay attention to so many
different moving parts in traffic? What lets us shift attention if a pedestrian suddenly
walks out into the street without notice? And why does our attention sometimes fail
us, occasionally with drastic consequences such as a car accident? This chapter will
explore our amazing capability to pay attention, divide our attention, and select
stimuli to which to pay attention in detail.
The Nature of Attention and Consciousness
137
The Nature of Attention and Consciousness
[Attention] is the taking possession of the mind, in clear and vivid form,
of one out of what seem several simultaneously possible objects or trains
of thoughts. … It implies withdrawal from some things in order to deal
effectively with others.
—William James, Principles of Psychology
It can be difficult to clearly describe in words what we mean when we talk about
attention (or any other psychological phenomenon). So what do we refer to exactly,
when we talk about attention in this chapter? Attention is the means by which we
actively process a limited amount of information from the enormous amount of information available through our senses, our stored memories, and our other cognitive
processes (De Weerd, 2003a; Rao, 2003). It includes both conscious and unconscious processes. In many cases, conscious processes are relatively easy to study. Unconscious processes are harder to study, simply because you are not conscious of
them (Jacoby, Lindsay, & Toth, 1992; Merikle, 2000). For example, you always
have a wealth of information available to you that you are not even aware of until
you retrieve that information from your memory or shift your attention toward it.
You probably can remember where you slept when you were ten years old or where
you ate your breakfasts when you were 12. At any given time, you also have available a dazzling array of sensory information to which you just do not attend. After
all, if you attended to each and every detail of your environment, you would feel
overwhelmed pretty fast (Figure 4.1). You also have very little reliable information
about what happens when you sleep. Therefore, it is hard to study processes that are
hidden somewhere in your unconsciousness, and of which you are not aware.
Attention allows us to use our limited mental resources judiciously. By dimming
the lights on many stimuli from outside (sensations) and inside (thoughts and memories), we can highlight the stimuli that interest us. This heightened focus increases
the likelihood that we can respond speedily and accurately to interesting stimuli.
Sensations
+
Memories
+
Thought processes
Driving a car
It’s cold in the car
You think about your new study
assignment
You watch the street
Attention:
Controlled processes
(including consciousness)
+
Automatic processes
You notice a child running across
the street in front of you
Actions
You brake
Figure 4.1 How Does Attention Work?
At any point in time, we perceive a lot of sensory information. Through attentional processes (which can be automatic
or controlled), we filter out the information that is relevant to us and that we want to attend to. Eventually, this leads to
our taking action on the basis of the information we attended to.
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CHAPTER 4 • Attention and Consciousness
Heightened attention also paves the way for memory processes. We are more likely
to remember information to which we paid attention than information we ignored.
At one time, psychologists believed that attention was the same thing as consciousness. Now, however, they acknowledge that some active attentional processing
of sensory and of remembered information proceeds without our conscious awareness
(Bahrami et al., 2008; Shear, 1997). For example, writing your name requires little
conscious awareness. You may write it while consciously engaged in other activities.
In contrast, writing a name that you have never encountered requires attention to
the sequence of letters.
Consciousness includes both the feeling of awareness and the content of awareness, some of which may be under the focus of attention (Bourguignon, 2000;
Farthing, 1992, 2000; Taylor, 2002). Therefore, attention and consciousness form
two partially overlapping sets (Srinivasan, 2008; DiGirolamo & Griffin, 2003).
Conscious attention serves three purposes in playing a causal role for cognition.
First, it helps in monitoring our interactions with the environment. Through such
monitoring, we maintain our awareness of how well we are adapting to the situation
in which we find ourselves. Second, it assists us in linking our past (memories) and
our present (sensations) to give us a sense of continuity of experience. Such continuity may even serve as the basis for personal identity. Third, it helps us in controlling and planning for our future actions. We can do so based on the information
from monitoring and from the links between past memories and present sensations.
In this chapter, we will first explore different kinds of attention like vigilance,
search, selective attention, and divided attention. Afterward, we will consider what
happens when our attention does not work properly, and what strategies we use in
order not to get overwhelmed in a world that is full of sensory stimuli. Then, we will
explore the nature of automatic processes, which help humans to make the best use
of their attentional resources. Last but not least, we will consider the topic of consciousness in more detail.
Attention
In this section, we will explore the four main functions of attention as well as theories to explain them (see also Table 4.1):
Here are the four main functions of attention:
1. Signal detection and vigilance: We try to detect the appearance of a particular
stimulus. Air traffic controllers, for example, keep an eye on all traffic near and
over the airport.
2. Search: We try to find a signal amidst distracters, for example, when we are
looking for our lost cell phone on an autumn leaf-filled hiking path.
3. Selective attention: We choose to attend to some stimuli and ignore others, as
when we are involved in a conversation at a party.
4. Divided attention: We prudently allocate our available attentional resources to
coordinate our performance of more than one task at a time, as when we are
cooking and engaged in a phone conversation at the same time.
We will also have a look at a number of neuroscientific studies and explanatory
models. Lastly, we will turn our attention to situations and conditions when our attention fails us.
Attention
Table 4.1
139
Four Main Functions of Attention
Function
Description
Example
Signal detection
and vigilance
On many occasions, we vigilantly try to detect
whether we did or did not sense a signal—a
particular target stimulus of interest. Through
vigilant attention to detecting signals, we are
primed to take speedy action when we do
detect signal stimuli.
In a research submarine, we may watch for
unusual sonar blips; on a dark street, we may
try to detect unwelcome sights or sounds; or
following an earthquake, we may be wary of
the smell of leaking gas or of smoke.
Search
We often engage in an active search for particular stimuli.
If we detect smoke (as a result of our vigilance),
we may engage in an active search for the
source of the smoke. In addition, some of us are
often in search of missing keys, sunglasses, and
other objects.
Selective attention
We constantly are making choices regarding
the stimuli to which we will pay attention and
the stimuli that we will ignore. By ignoring or at
least deemphasizing some stimuli, we thereby
highlight particularly salient stimuli. The concentrated focus of attention on particular informational stimuli enhances our ability to
manipulate those stimuli for other cognitive
processes, such as verbal comprehension or
problem solving.
We may pay attention to reading a textbook or
to listening to a lecture while ignoring such stimuli as a nearby radio or television or latecomers to the lecture.
Divided attention
We often manage to engage in more than one
task at a time, and we shift our attentional resources to allocate them prudently, as needed.
Experienced drivers easily can talk while driving
under most circumstances, but if another vehicle
seems to be swerving toward their car, they
quickly switch all their attention away from
talking and toward driving.
Attending to Signals over the Short and Long Terms
Have you ever spent a hot summer day at an overcrowded beach? People are lying
side by side on the sand, lined up like sardines in a tin. And though a trip to the
water might bring some relief from the heat, it does not provide any relief from the
crowding on the beach—people are standing thronged in the water with little space
to move unless you move out considerably further into the water. The lifeguards on
duty have to be constantly monitoring the crowds in the water to detect anything
that seems unusual. In this way, they can act fast enough in case there is an emergency. In the short term, they have to detect a crucial stimulus among the mass of
stimuli on the beach (signal detection), for example, making sure no one is drowning;
but they also have to maintain their attention over a long period of time (vigilance)
to make sure nothing is amiss during their entire working period. What factors contribute to their ability to detect events that might be emergencies? How do they
search the beaches and shorelines to detect important stimuli? Understanding this
function of attention has immediate practical importance. Occupations requiring
vigilance include those involving communications and warning systems and
quality control, as well as the work of police detectives, physicians. Also, research
psychologists must search out from among a diverse array of items those that are
CHAPTER 4 • Attention and Consciousness
(b)
© Steven L. Raymer/National Geographic/Getty Images
(a)
© Cultura RM/Alamy
© Robert Maass/Corbis
140
(c)
Signal Detection, Vigilance, and Search in Everyday Life.
(a) Signal detection. Luggage screeners learn techniques to enable them to maximize
“hits” and “correct rejections” and to minimize “false alarms” and “misses.” (b) Vigilance.
For air traffic controllers, vigilance is a matter of life and death. (c) Search. These trained
police dogs are actively seeking out a target, such as bombs or drugs.
more important. In each of these settings, people must remain alert to detect the
appearance of a stimulus. But each setting also involves the presence of distracters,
as well as prolonged periods during which the stimulus is absent. In the following
sections, we will first explore how people detect a target stimulus out of a wealth of
stimuli (i.e., how they detect signals). Once we know how people discriminate between target signals and distracters, we will turn to the maintenance of attention
over a prolonged period of time (vigilance) in order to detect important stimuli.
Signal Detection: Finding Important Stimuli in a Crowd
Signal-detection theory (SDT) is a framework to explain how people pick out the
few important stimuli when they are embedded in a wealth of irrelevant, distracting
stimuli. SDT often is used to measure sensitivity to a target’s presence. When we try
to detect a target stimulus (signal), there are four possible outcomes (Table 4.2).
Let’s stay with our example of the lifeguard. First, in hits (also called “true positives”), the lifeguard correctly identifies the presence of a target (i.e., somebody
drowning). Second, in false alarms (also called “false positives”), he or she incorrectly
identifies the presence of a target that is actually absent (i.e., the lifeguard thinks
somebody is drowning who actually isn’t). Third, in misses (also called “false negatives”), the lifeguard fails to observe the presence of a target (i.e., the lifeguard does
not see the drowning person). Fourth, in correct rejections (also called “true negatives”), the lifeguard correctly identifies the absence of a target (i.e., nobody is
drowning, and he or she knows that nobody is in trouble).
Attention
Table 4.2
141
Signal Detection Matrix Used in Signal-Detection Theory
Signal-detection theory was one of the first theories to suggest an interaction between the
physical sensation of a stimulus and cognitive processes such as decision making. Think
about the work of airport screeners. They need to be capable of perceiving objects like a
box cutter in hand-carried luggage.
Signal
Detect a Signal
Do Not Detect a Signal
Present
Hit
The screener recognizes a box cutter in the luggage.
Miss
The screener fails to see the box cutter
in the luggage.
Absent
False alarm
The screener thinks there is a box
cutter in the luggage when there is
none.
Correct rejection
The screener recognizes that there is no
box cutter in the luggage, and there is
indeed none.
Usually, the presence of a target is difficult to detect. Thus, we make detection
judgments based on inconclusive information with some criteria for target detections. The number of hits is influenced by where you place your criteria for considering something a hit. In other words, how willing are you to make false alarms? For
example, in the case of the lifeguard, the consequences of a miss are so grave that
the lifeguard lowers the criteria for considering something as a hit. In this way, he or
she increases the number of false alarms to boost hits (correct detections).
This trade-off often occurs with medical diagnoses as well. For example, it might
occur with highly sensitive screening tests where positive results lead to further tests.
Thus, overall sensitivity to targets must reflect a flexible criterion for declaring the detection of a signal. If the criterion for detection is too high, then the doctor will miss
illnesses (misses). If the criterion is too low, the doctor will falsely detect illnesses that
do not exist (false alarms). Sensitivity is measured in terms of hits minus false alarms.
Signal-detection theory can be discussed in the context of attention, perception,
or memory:
• attention—paying enough attention to perceive objects that are there;
• perception—perceiving faint signals that may or may not be beyond your
perceptual range (such as a very high-pitched tone);
• memory—indicating whether you have/have not been exposed to a stimulus before, such as whether the word “champagne” appeared on a list that was to be
memorized.
Disturbingly, on September 11, 2001, when terrorists crashed two airliners into
the Twin Towers in New York City, the 9/11 hijackers were screened at airports as
they prepared to board their flights. Several of them were pulled aside because they
set off metal detectors. After further screening, they were allowed onto their planes
anyway, even though they were carrying box cutters. The results of what constituted
a “miss” for the screeners were disastrous. As a result of this fiasco, the rules for
screening were tightened up considerably. But the tightening of rules created many
false alarms. Babies, grandmothers, and other relatively low-risk passengers started to
get second and sometimes even third screenings. So the rules were modified to profile passengers by computer. For example, those who bought one-way tickets or
changed their flight plans at the last moment became more likely to be subjected
to extra screening. This procedure, in turn, has inconvenienced those travelers who
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need to change their travel plans frequently, such as business travelers. The system
for screening passengers is constantly evolving in order to minimize both misses and
false alarms.
Vigilance: Waiting to Detect a Signal
When you have to pay attention in order to detect a stimulus that can occur at any
time over a long period of time, you need to be vigilant.
What is Vigilance? Vigilance refers to a person’s ability to attend to a field of stimulation over a prolonged period, during which the person seeks to detect the appearance of a particular target stimulus of interest. When being vigilant, the individual
watchfully waits to detect a signal stimulus that may appear at an unknown time.
Typically, vigilance is needed in settings where a given stimulus occurs only rarely
but requires immediate attention as soon as it does occur. Military officers watching
for a sneak attack are engaged in a high-stakes vigilance task.
In an early study, participants watched a visual display that looked like the face
of a clock (Mackworth, 1948). A clock hand moved in continuous steps except that
sometimes it would take a double step, which needed to be detected by the participants. Participants’ performance began to deteriorate substantially after just half an
hour of observation (see MacLean et al., 2009, for a more recent study). To relate
these findings to SDT, over time it appears that participants become less willing to
risk reporting false alarms. They err instead by failing to report the presence of the
signal stimulus when they are not sure they detect it, showing higher rates of misses.
Training can help to increase vigilance, but to counteract fatigue, nothing but taking a break really helps much (Fisk & Schneider, 1981).
In vigilance tasks, expectations regarding stimulus location strongly affect
response efficiency (LaBerge, Carter, & Brown, 1992; Motter, 1999). Thus, a busy lifeguard or air-traffic controller may respond quickly to a signal within a narrow radius of
where a signal is expected to appear. But signals appearing outside the concentrated
range of vigilant attention may not be detected as quickly or as accurately. However,
the abrupt onset of a stimulus (i.e., the sudden appearance of a stimulus) captures our
attention (Yantis, 1993). Thus, we seem to be predisposed to notice the sudden
appearance of stimuli in our visual field. We might speculate about the adaptive
advantage this feature of attention may have offered to our ancestral hunter-gatherer
forebears. They presumably needed to avoid predators and had to catch prey.
Vigilance is extremely important during scans at airports in detecting abandoned
bags or suspect items that may pose a security risk. Medical workers interpreting
results like MRI scans or X-rays need to be vigilant as well, watching for any
abnormalities in the results they are interpreting, even if they are very small. The costs
of failure of vigilance, in today’s world, can be great loss of life as well as of property.
Neuroscience and Vigilance Increased vigilance is seen in cases where emotional
stimuli are used (e.g., when somebody is confronted with a threatening stimulus).
The amygdala plays a pivotal role in the recognition of emotional stimuli. Thus, the
amygdala appears to be an important brain structure in the regulation of vigilance
(Phelps, 2004, 2006; van Marle et al., 2009). The thalamus is involved in vigilance
as well. Two specific activation states play a role in vigilance: bursts and the tonic
state. A burst is the result of relative hyperpolarization of the resting membrane potential (i.e., polarity of the membrane increases relative to its surrounding), and a
tonic state results from relative depolarization. During sleep, when people are less
Attention
143
responsive to stimuli, the neurons are hyperpolarized and in burst mode higher levels
of vigilance are associated with tonic discharges. Also, the less vigilance a person
displays, the more low-frequency activity and smaller event-related potentials can be
detected through EEG measurement (Llinas & Steriade, 2006; Oken et al., 2006).
Search: Actively Looking
Have you ever picked up your parents or friends at a crowded airport and tried to
locate them among the masses of people streaming out of the terminals? Search involves actively and often skillfully seeking out a target (Cisler et al., 2007; Posner &
DiGirolamo, 1998). Specifically, search refers to a scan of the environment for particular features—actively looking for something when you are not sure where it will
appear. As with vigilance, when we are searching for something, we may respond by
making false alarms. The police actively search an area where a crime like a bank
robbery has occurred, trying to find the robbers before they can escape. Search is
made more difficult by distracters, nontarget stimuli that divert our attention away
from the target stimulus. In the case of search, false alarms usually arise when we
encounter such distracters while searching for the target stimulus. For instance, consider searching for a product in the grocery store. We often see several distracting
items that look something like the item we hope to find. Package designers take advantage of the effectiveness of distracters when creating packaging for products. For
example, if a container looks like a box of Cheerios, you may pick it up without
realizing that it’s really Tastee-O’s.
The number of targets and distracters affects the difficulty of the task. This is
illustrated in Figure 4.2. Try to find the T in panel (a). Then try to find the T in
panel (b) of Figure 4.2. Display size is the number of items in a given visual array. (It
does not refer to the size of the items or even the size of the field on which the array
is displayed.) The display-size effect is the degree to which the number of items in
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Figure 4.2 Display Size.
Compare the relative difficulty in finding the T in panels (a) and (b). The display size affects your ease of performing
the task.
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CHAPTER 4 • Attention and Consciousness
a display hinders (slows down) the search process. When studying visual-search phenomena, investigators often manipulate the display size. They then observe how various contributing factors increase or decrease the display-size effect.
Distracters cause more trouble under some conditions than under others. Suppose we look for an item with a distinct feature like color or shape. We conduct a
feature search, in which we simply scan the environment for that feature (Treisman,
1993; Weidner & Mueller, 2009). Distracters play little role in slowing our search in
that case. For example, try to find the O in panel (c) of Figure 4.3. The O has a
distinctive form as compared with the L distracters in the display. The O thus seems
to pop out of the display. Featural singletons, which are items with distinctive features, stand out in the display (Yantis, 1993). When featural singletons are targets,
they seem to grab our attention. Unfortunately, any featural singletons grab our attention. This includes featural singletons that are distracters that can distract us from
finding the target (Navalpakkam & Itti, 2007). For example, find the T in panel (d)
of Figure 4.3. The T is a featural singleton. But the presence of the black (filled)
circle probably slows you down in your search.
A problem arises, however, when the target stimulus has no unique or even distinctive features, like a particular boxed or canned item in a grocery aisle. In these
situations, the only way we can find it is to conduct a conjunction search (Treisman,
1991). In a conjunction search, we look for a particular combination (conjunction—
joining together) of features. For example, the only difference between a T and an L
is the particular integration (conjunction) of the line segments. The difference is not
a property of any single distinctive feature of either letter. Both letters comprise a
horizontal line and a vertical line. So a search looking for either of these features
would provide no distinguishing information. In panels (a) and (b), you had to perform a conjunction search to find the T. So it probably took you longer to find it
than to find the O in panel (c). The dorsolateral prefrontal cortex as well as both
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Figure 4.3 Feature Search.
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Attention
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frontal eye fields and the posterior parietal cortex play a role only in conjunction
searches, but not so in feature searches (Kalla et al., 2009).
In the following section, we explore three theories that try to explain search
processes. These theories have developed in a dialectical way as responses to each
other: feature-integration theory, similarity theory, and guided search theory.
Feature-Integration Theory
Feature-integration theory explains the relative ease of conducting feature searches
and the relative difficulty of conducting conjunction searches. Consider Treisman’s
(1986) model of how our minds conduct visual searches. For each possible feature of
a stimulus, each of us has a mental map for representing the given feature across the
visual field. For example, there is a map for every color, size, shape, or orientation
(e.g., p, q, b, d) of each stimulus in our visual field. For every stimulus, the features
are represented in the feature maps immediately. There is no added time required for
additional cognitive processing. Thus, during feature searches, we monitor the relevant feature map for the presence of any activation anywhere in the visual field.
This monitoring process can be done in parallel (all at once). It therefore shows no
display-size effects. However, during conjunction searches, an additional stage of processing is needed. During this stage, we must use our attentional resources as a sort of
mental “glue.” This additional stage conjoins two or more features into an object
representation at a particular location. In this stage, we can conjoin the features
only one object at a time. This stage must be carried out sequentially, conjoining
each object one by one. Effects of display size (i.e., a larger number of objects with
features to be conjoined) therefore appear.
There is some neuropsychological support for Treisman’s model. For example,
Nobel laureates David Hubel and Torsten Wiesel (1979) identified specific neural
feature detectors. These are cortical neurons that respond differentially to visual stimuli of particular orientations (e.g., vertical, horizontal, or diagonal). More
recent research has indicated that the best search strategy is not for the brain to increase the activity of neurons that respond to the particular target stimuli; in fact,
the brain seems to use the more nearly optimal strategy of activating neurons that
best distinguish between the target and distracters while at the same time ignoring
the neurons that are tuned best to the target (Navalpakkam & Itti, 2007; Pouget &
Bavelier, 2007).
Similarity Theory
Not everyone agrees with Treisman’s model, however. According to similarity theory,
Treisman’s data can be reinterpreted. In this view, the data are a result of the fact
that as the similarity between target and distracter stimuli increases, so does the difficulty in detecting the target stimuli (Duncan & Humphreys, 1992; Watson et al.,
2007). Thus, targets that are highly similar to distracters are relatively hard to detect.
Targets that are highly disparate from distracters are relatively easy to detect. For example, try to find the black (filled) circle in Figure 4.4, panel (e). The target is highly
similar to the distracters (black squares or white circles). Therefore it is very difficult
to find. Furthermore, the difficulty of search tasks depends on the degree of disparity
among the distracters. But it does not depend on the number of features to be integrated. For instance, one reason that it is easier to read long strings of text written in
lowercase letters than text written in capital letters is that capital letters tend to be
more similar to one another in appearance. Lowercase letters, in contrast, have more
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(e)
Figure 4.4 Similarity Theory.
In panel (e), find the black circle.
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Figure 4.5 Similarity Theory.
In panels (f) and (g), find the R.
distinguishing features. Try to find the capital letter R in panels (f) and (g) of Figure
4.5 to get an idea of how highly dissimilar distracters impede visual search.
Guided Search Theory
In response to these and other findings, investigators have proposed an alternative to
Treisman’s model. They call it guided search (Cave & Wolfe, 1990; Wolfe, 2007).
The guided-search model suggests that all searches, whether feature searches or
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147
(h)
Figure 4.6 Guided Search Theory.
In panel (h), find the black circle.
conjunction searches, involve two consecutive stages. The first is a parallel stage: the
individual simultaneously activates a mental representation of all the potential targets. The representation is based on the simultaneous activation of each of the features of the target. In a subsequent serial stage, the individual sequentially evaluates
each of the activated elements, according to the degree of activation. Then, the person chooses the true targets from the activated elements. According to this model,
the activation process of the parallel initial stage helps to guide the evaluation and
selection process of the serial second stage of the search.
Let’s see how guided search might work. Look at panel (h) of Figure 4.6. Try to
find the black circle. The parallel stage will activate a mental map that contains all
the features of the target (circle, black). Thus, black circles, white circles, and black
squares will be activated. During the serial stage, you first will evaluate the black
circle, which was highly activated. But then you will evaluate the black squares
and the white circles, which were less highly activated. You then will dismiss them
as distracters.
Neuroscience: Aging and Visual Search
An interesting study investigated the effect of aging on visual search capabilities
(Madden et al., 2002; Madden, 2007). The researchers had two groups of
participants—one in their 20s and one between 60 and 77 years of age—conduct a
variety of visual searches of various difficulties for a black upright L: a feature search,
where participants had to find the black upright L between white, partly rotated Ls; a
guided search, where the target had to be found in between white Ls as well as three
black Ls of various rotation; and a conjunction search where the black L had to be
found in between a variety of rotated Ls that were either black or white (Figure 4.7).
Younger adults’ searches were more accurate and faster than the searches of the
older adults. Also, participants were slower by approximately 300 milliseconds when
doing guided searches as compared with feature searches. Older adults’ cortical
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CHAPTER 4 • Attention and Consciousness
Feature
Guided
Conjunction
Figure 4.7 Search Tasks in an Experiment.
Here are examples for feature search, guided search, and conjunction search. In all three
cases, participants were instructed to look for the upright black L.
Source: Madden, D. J., Turkington, T. G., Provenzale, J. M., Denny, L. L., Langley, L. K., Hawk, T. C., et al.
(2002). Aging and attentional guidance during visual search: Funtional neuroanatomy by positron emission
tomography. Psychology and Aging, 17(1), 24–43.
volume was lower than that of the younger adults, which is consistent with an approximate decline in volume of 2% per decade. The most difficult search (conjunction search) led to activation in the dorsal and ventral visual pathways as well as the
prefrontal cortex in both young and older adults. Although there was less activation
in the right occipital cortex in older adults, the activation was about the same in
both age groups in the prefrontal and superior parietal regions. The more difficult a
search task was, the more the occipito-temporal cortex was activated in younger
adults but not in older adults. The older adults seem to have this brain region activated at a higher level even during easier search tasks, apparently trying to compensate for the age-related decline; but they did not recruit other brain regions outside
the visual pathways to compensate for age-related decline.
Selective Attention
We explored the first two functions of attention—signal detection and search. Now,
let’s examine another function of attention—selective attention.
What Is Selective Attention?
Suppose you are at a dinner party. It is just your luck that you are sitting next to a
salesman. He sells 110 brands of vacuum cleaners. He describes to you in excruciating detail the relative merits of each brand. As you are listening to this blatherer,
who happens to be on your right, you become aware of the conversation of the
two diners sitting on your left. Their exchange is much more interesting. It contains
juicy information you had not known about one of your acquaintances. You find
yourself trying to keep up the semblance of a conversation with the blabbermouth
on your right, but you are also tuning in to the dialogue on your left.
Colin Cherry (1953, see also Bee & Micheyl, 2008) referred to this phenomenon
as the cocktail party problem, the process of tracking one conversation in the face of
the distraction of other conversations. He observed that cocktail parties are often settings in which selective attention is salient. Cherry did not actually hang out at numerous cocktail parties to study conversations. He studied selective attention in a
more carefully controlled experimental setting. He devised a task known as shadowing.
Attention
149
In a picnic basket, she had peanut butter
sandwiches and chocolate brownies...
In the picnic basket,
she had peanut butter,
sandwiches, and chocolate
brownies
Shadowed ear
The cat suddenly
started to run
after the mouse
and ....
Unattended ear
Figure 4.8 Dichotic Presentation.
In dichotic presentation, each ear is presented a separate message.
In shadowing, you listen to two different messages. Cherry presented a separate message
to each ear, known as dichotic presentation. Figure 4.8 illustrates how these listening
tasks might be presented. You are required to repeat back only one of the messages as
soon as possible after you hear it. In other words, you are to follow one message (think
of a detective “shadowing” a suspect) but ignore the other.
Cherry’s participants were quite successful in shadowing distinct messages in
dichotic-listening tasks, although such shadowing required a significant amount of
concentration. The participants were also able to notice physical, sensory changes
in the unattended message—for example, when the message was changed to a tone
or the voice changed from a male to a female speaker. However, they did not notice
semantic changes in the unattended message. They failed to notice even when the
unattended message shifted from English to German or was played backward. Conversely, about one third of people, when their name is presented during these situations, will switch their attention to their name. Some researchers have noted that
those who hear their name in the unattended message tend to have limited
working-memory capacity. As a result, they are easily distracted (Conway, Cowan,
& Bunting, 2001). Infants will also shift their attention to one of two messages if
their name is said (Newman, 2005).
Think of being in a noisy restaurant. Three factors help you to selectively attend
only to the message of the target speaker to whom you wish to listen:
1. Distinctive sensory characteristics of the target’s speech. Examples of such characteristics are high versus low pitch, pacing, and rhythmicity.
2. Sound intensity (loudness).
3. Location of the sound source (Brungard & Simpson, 2007).
Attending to the physical properties of the target speaker’s voice has its advantages. You can avoid being distracted by the semantic content of messages from nontarget speakers in the area. Clearly, the sound intensity of the target also helps. In
addition, you probably turn one ear toward and the other ear away from the target
speaker. Note that this method offers no greater total sound intensity. The reason is
that with one ear closer to the speaker, the other is farther away. The key advantage
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CHAPTER 4 • Attention and Consciousness
is the difference in volume. It allows you to locate the source of the target sound.
Recent psychophysical studies have found, however, that spatial cues are less important than factors like how harmonious and rhythmic the target sounds (Darwin,
2008; Muente et al., 2010).
Theories of Selective Attention
In the following section, we will discuss several theories of selective attention. Note
how dialectical processes influenced the development of subsequent theories. The
theories described here belong to the group of filter and bottleneck theories. A filter
blocks some of the information going through and thereby selects only a part of the
total of information to pass through to the next stage. A bottleneck slows down information passing through. The models differ in two ways. First, do they have a distinct “filter” for incoming information? Second, if they do, where in the processing
of information does the filter occur (early or late)?
Broadbent’s Model According to one of the earliest theories of attention, we
filter information right after we notice it at the sensory level (Broadbent, 1958;
Figure 4.9). Multiple channels of sensory input reach an attentional filter. Those
channels can be distinguished by their characteristics like loudness, pitch, or accent.
The filter permits only one channel of sensory information to proceed and reach the
processes of perception. We thereby assign meaning to our sensations. Other stimuli
Sensory
register
Unattended
Broadbent
Attended
Treisman
Attended
Perceptual
processes
Short-term
memory
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register
Unattended
Selective
filter
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processes
Attenuation
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Limited
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memory
R
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P
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Figure 4.9 Broadbent and Treisman’s Models of Attention.
Various mechanisms have been proposed suggesting a means by which incoming sensory information passes through
the attentional system to reach high-level perceptual processes.
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151
will be filtered out at the sensory level and may never reach the level of perception.
Broadbent’s theory was supported by Colin Cherry’s findings that sensory information sometimes may be noticed by an unattended ear if it does not have to be
processed elaborately (e.g., you may notice that the voice in your unattended ear
switches to a tone). But information requiring higher perceptual processes is not
noticed if not attended to (e.g., you would likely not notice that the language in
your unattended ear switches from English to German).
Selective Filter Model Not long after Broadbent’s theory, evidence began to
suggest that Broadbent’s model must be wrong (e.g., Gray & Wedderburn, 1960).
Moray found that even when participants ignore most other high-level (e.g., semantic) aspects of an unattended message, they frequently still recognize their names in
an unattended ear (Moray, 1959; Wood & Cowan, 1995). He suggested that the
reason for this effect is that messages that are of high importance to a person may
break through the filter of selective attention (e.g., Koivisto & Revonsuo, 2007;
Marsh et al., 2007). But other messages may not. To modify Broadbent’s metaphor,
one could say that, according to Moray, the selective filter blocks out most information at the sensory level. But some personally important messages are so powerful
that they burst through the filtering mechanism.
Attenuation Model To explore why some unattended messages pass through the
filter, Anne Treisman conducted some experiments. She had participants shadowing
coherent messages, and at some point switched the remainder of the coherent message from the attended to the unattended ear. Participants picked up the first few
words of the message they had been shadowing in the unattended ear (Treisman,
1960), so they must have been somehow processing the content of the unattended
message. Moreover, if the unattended message was identical to the attended one, all
participants noticed it. They noticed even if one of the messages was slightly out of
temporal synchronization with the other (Treisman, 1964a, 1964b). Treisman also
observed that some fluently bilingual participants noticed the identity of messages
if the unattended message was a translated version of the attended one.
Moray’s modification of Broadbent’s filtering mechanism was clearly not sufficient
to explain Treisman’s (1960, 1964a, 1964b) findings. Her findings suggested that at
least some information about unattended signals is being analyzed. Treisman proposed
a theory of selective attention that involves a later filtering mechanism (Figure 4.9).
Instead of blocking stimuli out, the filter merely weakens (attenuates) the strength of
INVESTIGATING COGNITIVE PSYCHOLOGY
Attenuation Model
Get two friends to help you with this experiment. Ask one friend to read something very
softly into your other friend’s ear (it can be anything—a joke, a greeting card, or a cognitive psychology textbook), and have your other friend try to “shadow” what the other
friend is saying. (Shadowing is repeating all the words that another person is saying.) In
your friend’s other ear, say “animal” very softly. Later, ask your friend what you said.
Is your friend able to identify what you said? Probably not. Try this again, but this time
say your friend’s name. Your friend will most likely be able to recall that you said his or
her name. This finding demonstrates Treisman’s attenuation model.
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CHAPTER 4 • Attention and Consciousness
Sensory
register
Deutsch
&
Deutsch,
Norman
Figure 4.10
Perceptual
processes
I
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Selective
filter
Short-term
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Deutsch & Deutsch’s Late-Filter Model.
According to some cognitive psychologists, the attentional filtering mechanisms follow, rather than precede, preliminary
perceptual processes.
stimuli other than the target stimulus. So when the stimuli reach us, we analyze them
at a low level for target properties like loudness and pitch. You may listen for the
voice of the person you are talking to in a noisy bar, for example. If the stimuli possess
those target properties, we pass the signal on to the next stage; if they do not possess
those target properties, we pass on a weakened version of the stimulus. In a next step,
we perceptually analyze the meaning of the stimuli and their relevance to us, so that
even a message from the unattended ear that is supposedly irrelevant can come into
consciousness and influence our subsequent actions if it has some meaning for us.
Late-Filter Model Deutsch and Deutsch (1963; Norman, 1968) developed a model
in which the location of the filter is even later (Figure 4.10). They suggested that
stimuli are filtered out only after they have been analyzed for both their physical
properties and their meaning. This later filtering would allow people to recognize
information entering the unattended ear. For example, they might recognize the
sound of their own names or a translation of attended input (for bilinguals). Note
that proponents of both the early and the late-filtering mechanisms propose that
there is an attentional bottleneck through which only a single source of information
can pass. The two models differ only in terms of where they hypothesize the bottleneck to be positioned.
A Synthesis of Early-Filter and Late-Filter Models Both early and late selection
theories have data to support them. So what is a researcher to do? In 1967, Ulric
Neisser synthesized the early-filter and the late-filter models and proposed that there
are two processes governing attention:
• Preattentive processes: These automatic processes are rapid and occur in parallel.
They can be used to notice only physical sensory characteristics of the unattended message. But they do not discern meaning or relationships.
• Attentive, controlled processes: These processes occur later. They are executed serially and consume time and attentional resources, such as working memory.
They also can be used to observe relationships among features. They serve to
synthesize fragments into a mental representation of an object.
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153
A two-step model could account for Cherry’s, Moray’s, and Treisman’s data.
The model also nicely incorporates aspects of Treisman’s signal-attenuation theory
and of her subsequent feature-integration theory. According to Treisman’s theory,
discrete processes for feature detection and for feature integration occur during
searches. The feature-detection process may be linked to the former of the two processes (i.e., speedy, automatic processing). Her feature-integration process may be
linked to the latter of the two processes (i.e., slower, controlled processing). Unfortunately, however, the two-step model does not do a good job of explaining the continuum of processes from fully automatic ones to fully controlled ones. Recall, for
example, that fully controlled processes appear to be at least partially automatized
(Spelke, Hirst, & Neisser, 1976). How does the two-process model explain the automatization of processes in divided-attention phenomena? For example, how can
one read for comprehension while writing dictated, categorized words? We will discuss this in the section on divided attention.
Neuroscience and Selective Attention
As early as in the 1970s, researchers employed event-related potentials (ERPs) to
study attention. A groundbreaking study was conducted by Hillyard and his colleagues (1973), when they exposed their participants to two streams of tones, one in
each ear (the streams differed in pitch). The participants had to detect occasionally
occurring target stimuli. When the target stimuli occurred in the attended ear, the
first negative component of the ERP was larger than when the target occurred in
the unattended ear. N1 is a negative wave that appears about 90 milliseconds after
the onset of the target stimulus. The researchers hypothesized that the N1 wave was a
result of the enhancement of the target stimulus. At the same time, there was a suppression of the other stimuli. This result is consistent with filter theories. Later studies
(Woldorff & Hillyard, 1991) found an even earlier reaction to the target stimulus in
the form of a positive wave that occurs about 20–50 milliseconds after the onset of a
target. The wave originates in the Heschl’s gyri, which are located in the auditory
cortex (Woldorff et al., 1993). Studies still use these methods today to explore topics
as diverse as the influence of mothers’ socio-economic status on children’s selective
attention (Stevens et al., 2009). They have found that children of mothers with lower
levels of education show reduced effects of selective attention on neural processing.
Similar effects also have been found for visual attention. If a target stimulus appears in an attended region of the visual field, the occipital P1 (a wave of positive
polarity) is larger than when the target appears in an unattended region (Eason et al.,
1969; Van Voorhis & Hillyard, 1977). The P1 effect also occurs when participants’
attention is drawn to a particular location by a sensory cue, and the target subsequently appears in just that location. If the interval between the appearance of
the cue and the target is very small, the P1 wave is enlarged and the reaction time
is faster than for targets that appear with a significant delay after the cue. In fact,
a delay between cue and target can even lead to a delay in reaction time and
decreased size of P1 wave (Hopfinger & Mangun, 1998, 2001).
Divided Attention
Have you ever been driving with a friend and the two of you were engaged in an
exciting conversation? Or have made dinner while on the phone with a friend? Anytime you are engaged in two or more tasks at the same time, your attention is
divided between those tasks.
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CHAPTER 4 • Attention and Consciousness
WATCH
FOR
ICE
Failure of Divided Attention
Investigating Divided Attention in the Lab
Early work in the area of divided attention had participants view a videotape in
which the display of a basketball game was superimposed on the display of a handslapping game. Participants could successfully monitor one activity and ignore the
other. However, they had great difficulty in monitoring both activities at once,
even if the basketball game was viewed by one eye and the hand-slapping game
was watched separately by the other eye (Neisser & Becklen, 1975).
Neisser and Becklen hypothesized that improvements in performance eventually
would have occurred as a result of practice. They also hypothesized that the performance of multiple tasks was based on skill resulting from practice. They believed it
not to be based on special cognitive mechanisms.
The following year, investigators used a dual-task paradigm to study divided
attention during the simultaneous performance of two activities: reading short stories and writing down dictated words (Spelke, Hirst, & Neisser, 1976). The researchers would compare and contrast the response time (latency) and accuracy
of performance in each of the three conditions. Of course, higher latencies mean
slower responses. As expected, initial performance was quite poor for the two tasks
when the tasks had to be performed at the same time. However, Spelke and her
colleagues had their participants practice to perform these two tasks 5 days a
week for many weeks (85 sessions in all). To the surprise of many, given enough
practice, the participants’ performance improved on both tasks. They showed improvements in their speed of reading and accuracy of reading comprehension, as
measured by comprehension tests. They also showed increases in their recognition
memory for words they had written during dictation. Eventually, participants’ performance on both tasks reached the same levels that the participants previously
had shown for each task alone.
When the dictated words were related in some way (e.g., they rhymed or
formed a sentence), participants first did not notice the relationship. After repeated
practice, however, the participants started to notice that the words were related to
Attention
155
INVESTIGATING COGNITIVE PSYCHOLOGY
Dividing Your Attention
Repeatedly write your name on a piece of paper while you picture everything you can
remember about the room in which you slept when you were 10 years old. While continuing to write your name and picturing your old bedroom, take a mental journey of
awareness to notice your bodily sensations, starting from one of your big toes and proceeding up your leg, across your torso, to the opposite shoulder, and down your arm.
What sensations do you feel—pressure from the ground, your shoes, or your clothing or
even pain anywhere? Are you still managing to write your name while retrieving remembered images from memory and continuing to pay attention to your current sensations?
Either task would have been easier done by itself than when done in parallel. Were you
able to divide your attention successfully?
each other in various ways. They soon could perform both tasks at the same time
without a loss in performance. Spelke and her colleagues suggested that these findings showed that controlled tasks can be automatized so that they consume fewer
attentional resources. Furthermore, two discrete controlled tasks may be automatized
to function together as a unit. The tasks do not, however, become fully automatic.
For one thing, they continue to be intentional and conscious. For another, they involve relatively high levels of cognitive processing.
An entirely different approach to studying divided attention has focused on extremely simple tasks that require speedy responses. When people try to perform two
overlapping speeded tasks, the responses for one or both tasks are almost always
slower (Pashler, 1994). When a second task begins soon after the first task has
started, speed of performance usually suffers. The slowing resulting from simultaneous engagement in speeded tasks, as mentioned earlier in the chapter, is the PRP
(psychological refractory period) effect, also called attentional blink. Findings from
PRP studies indicate that people can accommodate fairly easily perceptual processing
of the physical properties of sensory stimuli while engaged in a second speeded task
(Miller et al., 2009; Pashler, 1994). However, they cannot readily accomplish more
than one cognitive task requiring them to choose a response, retrieve information
from memory, or engage in various other cognitive operations. When both tasks require performance of any of these cognitive operations, one or both tasks will show
the PRP effect.
How well people can divide their attention also has to do with their intelligence
(Hunt & Lansman, 1982). For example, suppose that participants are asked to solve
mathematical problems and simultaneously to listen for a tone and press a button as
soon as they hear it. We can expect that they both would solve the math problems
effectively and respond quickly to hearing the tone. According to Hunt and
Lansman, more intelligent people are better able to timeshare between two tasks
and to perform both effectively.
Theories of Divided Attention
In order to understand our ability to divide our attention, researchers have
developed capacity models of attention. These models help to explain how we can
perform more than one attention-demanding task at a time. They posit that people
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CHAPTER 4 • Attention and Consciousness
Stimulus inputs
Mental
resources
available
Allocated
to
Task 1
Allocated
to
Task 2
Possible activities
selected
Actual responses
(a)
Stimulus inputs
Mental
resources
available
Modality
1
Possible activities
selected
Modality
2
Possible activities
selected
Actual responses
(b)
Figure 4.11 Allocation of Attentional Resources.
Attentional resources may involve either a single pool or a multiplicity of modality-specific pools. Although the attentional resources theory has been criticized for its imprecision, it seems to complement filter theories in explaining some
aspects of attention.
have a fixed amount of attention that they can choose to allocate according to what
the task requires. There are two different kinds: One kind of model suggests that
there is one single pool of attentional resources that can be divided freely, and the
other model suggests that there are multiple sources of attention (McDowd, 2007).
Figure 4.11 shows examples of the two kinds of models. In panel (a), the system has
a single pool of resources that can be divided up, say, among multiple tasks (Kahneman, 1973).
It now appears that such a model represents an oversimplification. People are
much better at dividing their attention when competing tasks are in different
modalities. At least some attentional resources may be specific to the modality
(e.g., verbal or visual) in which a task is presented. For example, most people easily
can listen to music and concentrate on writing simultaneously. But it is harder to
listen to the news station and concentrate on writing at the same time. The reason
is that both are verbal tasks. The words from the news interfere with the words you
are thinking about. Similarly, two visual tasks are more likely to interfere with each
other than are a visual task coupled with an auditory one. Panel (b) of Figure 4.11
shows a model that allows for attentional resources to be specific to a given modality
(Navon & Gopher, 1979).
Attentional-resources theory has been criticized severely as overly broad and
vague (e.g., Navon, 1984; S. Yantis, personal communication, December 1994). Indeed, it may not stand alone in explaining all aspects of attention, but it complements filter theories quite well. Filter and bottleneck theories of attention seem to
be more suitable metaphors for competing tasks that appear to be attentionally incompatible, like selective-attention tasks or simple divided-attention tasks.
Consider the psychological refractory period (PRP) effect, for example. To obtain this effect, participants are asked to respond to stimuli once they appear, and if
a second stimulus follows a first one immediately, the second response is delayed. For
these kinds of tasks, it appears that processes requiring attention must be handled
Attention
157
n BELIEVE IT OR NOT
ARE YOU PRODUCTIVE WHEN YOU’RE MULTITASKING?
You’re working on your term paper, you’re texting with
your best friend, and are having a little snack while listening to some music in the background. And you think
you’re productive? Researcher David Meyer and colleagues (2007) have found that working on more than one
task at the same time not only makes you slower but also
increases your chances of making mistakes. Your reaction
time goes down by up to one second when you do two
things at once. While this may not be so crucially important while you sit at your desk working, it can save or risk
lives when you drive your car and text or make a call at
the same time. However, even your learning capabilities
are impaired. A study by Foerde and colleagues (2006)
found that the formation of declarative memory (which is
essential for successful learning) is hampered even by little
distractions like a sound in the background. This is because when we perform complex tasks, we keep a lot
of information activated in our memory. The required concentration can easily be broken by external disturbances.
If you want to try out how well you can text and drive at
the same time, here’s a little game for you:
http://www.nytimes.com/interactive/2009/
07/19/technology/20090719-driving-game
.html
sequentially, as if passing one-by-one through an attentional bottleneck (Olivers &
Meeter, 2008).
Resource theory seems to be a better metaphor for explaining phenomena of divided attention (see Believe It or Not) on complex tasks. In these tasks, practice effects may be observed. According to this metaphor, as each of the complex tasks
becomes increasingly automatized, performance of each task makes fewer demands
on limited-capacity attentional resources. Additionally, for explaining searchrelated phenomena, theories specific to visual search (e.g., models proposing guided
search [Cave & Wolfe, 1990; Wolfe, 2007] or similarity [Duncan & Humphreys,
1989]) seem to have stronger explanatory power than do filter or resource theories.
However, these two kinds of theories are not altogether incompatible. Although the
findings from research on visual search do not conflict with filter or resource theories, the task-specific theories more specifically describe the processes at work during
visual search.
Divided Attention in Everyday Life
Divided attention plays an important role in our lives. How often are you engaged in
more than one task at a time? Consider driving a car, for example. You need to be
constantly aware of threats to your safety. Suppose you fail to select one such threat,
such as a car that runs a red light and is headed directly toward you as you enter
an intersection. The result is that you may become an innocent victim of a horrible
car accident. Moreover, if you are unsuccessful in dividing your attention, you may
cause an accident. Most automobile accidents are caused by failures in divided
attention.
Some intriguing studies are based on our own set of everyday experiences. One
widely used paradigm makes use of a simulation of the driving situation (Strayer &
Johnston, 2001, see also Fisher & Pollatsek, 2007). Researchers had participants perform a tracking task. The participants had control of a joystick, which moved a cursor on a computer screen. The participants needed to keep the cursor in position on
a moving target. At various times, the target would flash either green or red. If the
color was green, the participants were to ignore the signal. If the color was red, however, the participants were to push a simulated brake. The simulated brake was a
CHAPTER 4 • Attention and Consciousness
button on the joystick. In one condition, participants only had to accomplish this
one task. In another condition, participants were involved in a second task. This
procedure created a dual-task situation. The participants either listened to a radio
broadcast while doing the task or talked on a cell phone to an experimental confederate (a collaborator of the experimenter). Participants talked roughly half the time
and also listened roughly half the time. Two different topics were used to ensure that
the results were not a result of the topic of conversation.
As shown in Figure 4.12, the probability of a miss in the face of the red
signal increased substantially in the cell-phone dual-task condition relative to the
0.10
Single task
0.09
Dual task
Probability of a miss
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
Cell phone
Radio control
625
Single task
Mean reaction time (msec)
158
600
Dual task
575
550
525
500
475
450
Cell phone
Figure 4.12
Radio control
Dual-Task Performance During Driving.
Top panel: Dual-task performance significantly increased the probability of a miss in the cellphone condition but not in the radio-control condition.
Bottom panel: Reaction time increased significantly for a dual task in the cell-phone condition
but not in the radio-control condition.
Source: From Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction: Dual-task studies of simulated
driving and conversing on a cellular telephone. Psychological Science, 12, 463. Reprinted by permission of
Blackwell Publishing.
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single-task condition. Reaction times were also substantially slower in this condition
than in the single-task condition. In contrast, there was no significant difference between probabilities of a miss in the single-task and radio dual-task condition, nor
was there a significant difference in reaction time in this condition. Thus, use of
cell phones appears to be substantially more risky than listening to the radio while
driving (see also Charltona, 2009; Drews, 2008). So when you are driving, you are
better off not using your cell phone.
There are also studies that analyze data from real-world incidents. A study of
2700 crashes in the state of Virginia between June and November of 2002 investigated causes of accidents (Warner, 2004). Here are some of the main factors that
resulted in accidents, with the percentage of accidents for which each was
responsible:
•
•
•
•
•
•
rubbernecking (viewing accidents that have already occurred), 16%;
driver fatigue, 12%;
looking at scenery or landmarks, 10%;
distractions caused by passengers or children, 9%;
adjusting a radio, tape, or CD player, 7%; and
cell phone use, 5%.
On an average, distractions occurring inside the vehicle accounted for 62% of
the distractions reported. Distractions outside the vehicle accounted for 35%. The
other 3% were of undetermined cause. The causes of accidents differed somewhat
for rural versus urban areas. Accidents in rural areas were more likely to be due to
driver fatigue, insects entering or striking the vehicle, or pet distractions. In urban
areas, crashes were more likely to result from rubbernecking, traffic, or cell-phone
use (Cohen & Graham, 2003; Figure 4.13).
As many as 21% of accidents and near-accidents involve at least one driver
talking on a cell phone, although the conversation may or may not have been the
cause of the accident (Seo & Torabi, 2004). Other research has indicated that,
when time on task and driving conditions are controlled for, the effects of talking
on a cell phone can be as detrimental as driving while intoxicated (Strayer, Drews,
& Crouch, 2006). Still other research has found that, compared with people not on
a cell phone, people talking on a cell phone exhibit more anger, through honking
and facial expressions, when presented with a frustrating situation (McGarva, Ramsey, & Shear, 2006). Increased aggression has been linked with increased accidents
(Deffenbacher et al., 2003). Therefore, it is likely that people who talk on the
phone while driving are more prone to anger and, as a result, more accidents. These
findings, combined with those on the effects of divided attention, help to explain
why an increase in accidents is seen when cell phones are involved.
Factors That Influence Our Ability to Pay Attention
The existing theoretical models of attention may be too simplistic and mechanistic
to explain the complexities of attention. There are many other variables that have
an impact on our ability to concentrate and pay attention. Here are some of them:
• Anxiety: Being anxious, either by nature (trait-based anxiety) or by situation
(state-based anxiety), places constraints on attention (Eysenck & Byrne, 1992;
Reinholdt-Dunne et al., 2009).
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Figure 4.13
Divided Attention: Driving and Talking on the Cell Phone.
Illustrating a failure of divided attention, accidents often happen because drivers are
engaged in other activities like cell phone conversations. Drivers who rubberneck at the
scene of an accident are another major cause of further accidents.
• Arousal: Your overall state of arousal affects attention as well. You may be tired,
drowsy, or drugged, which may limit attention. Being excited sometimes enhances attention (MacLean et al., 2009).
• Task difficulty: If you are working on a task that is very difficult or novel for
you, you’ll need more attentional resources than when you work on an easy or
highly familiar task. Task difficulty particularly influences performance during
divided attention.
• Skills: The more practiced and skilled you are in performing a task, the more
your attention is enhanced (Spelke, Hirst, & Neisser, 1976).
In sum, certain attentional processes occur outside our conscious awareness. Others
are subject to conscious control. The psychological study of attention has included diverse phenomena, such as vigilance, search, selective attention, and divided attention
during the simultaneous performance of multiple tasks. To explain this diversity of attentional phenomena, current theories emphasize that a filtering mechanism appears to
govern some aspects of attention. Limited modality-specific attentional resources appear to influence other aspects of attention. Clearly, findings from cognitive research
have yielded many insights into attention, but additional understanding also has been
gained through the study of attentional processes in the brain.
Neuroscience and Attention: A Network Model
Imagine how hard it is to synthesize all those diverse studies investigating the full
range of attentional processes in the brain. Is attention a function of the entire
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brain, or is it a function of discrete attention-governing modules in the brain? According to Michael Posner, the attentional system in the brain “is neither a property
of a single brain area nor of the entire brain” (Posner & Dehaene, 1994, p. 75). In
2007, Posner teamed up with Mary Rothbart and they conducted a review of neuroimaging studies in the area of attention to investigate whether the many diverse results of studies conducted pointed to a common direction. They found that what at
first seemed like an unclear pattern of activation could be effectively organized into
areas associated with the three subfunctions of attention: alerting, orienting, and executive attention. The researchers organized the findings to describe each of these
functions in terms of the brain areas involved, the neurotransmitters that modulate
the changes, and the results of dysfunction within this system.
Alerting: Alerting is defined as being prepared to attend to some incoming
event, and maintaining this attention. Alerting also includes the process of getting
to this state of preparedness. The brain areas involved in alerting are the right frontal and parietal cortexes as well as the locus coeruleus. The neurotransmitter norepinephrine is involved in the maintenance of alertness. If the alerting system does not
work properly, people develop symptoms of ADHD; in the process of regular aging,
dysfunctions of the alerting system may develop as well.
Orienting: Orienting is defined as the selection of stimuli to attend to. This
kind of attention is needed when we perform a visual search. You may be able to
observe this process by means of a person’s eye movements, but sometimes attention is covert and cannot be observed from the outside. The orienting network
develops during the first year of life. The brain areas involved in the orienting
function are the superior parietal lobe, the temporal parietal junction, the frontal
eye fields, and the superior colliculus. The modulating neurotransmitter for orienting is acetylcholine. Dysfunction within this system can be associated with
autism.
Executive Attention: Executive attention includes processes for monitoring
and resolving conflicts that arise among internal processes. These processes include thoughts, feelings, and responses. The brain areas involved in this final
and highest order of attentional process are the anterior cingulate, lateral ventral,
and prefrontal cortex as well as the basal ganglia. The neurotransmitter most involved in the executive attention process is dopamine. Dysfunction within this
system is associated with Alzheimer’s disease, borderline personality disorder, and
schizophrenia.
Intelligence and Attention
Attention also plays a role in intelligence (Hunt, 2005; Stankov, 2005). One model of
intelligence that takes attention into account is the Planning, Attention, and Simultaneous–Successive Process Model of Human Cognition (PASS; Das, Naglieri, & Kirby,
1994; see also Davidson & Kemp, 2010). Based on Luria’s (1973) theory of intelligence, it assumes that intelligence consists of an assortment of functional units that
are the basis for specific actions (Naglieri & Kaufman, 2001). According to the PASS
model, there are three distinct processing units and each is associated with specific areas
of the brain: arousal and attention, simultaneous and successive processing, and planning
(Das et al., 1994; Naglieri & Kaufman, 2001). The first unit, arousal and attention, is
primarily attributed to the brainstem, diencephalon, and medial cortical regions of the
brain. The researchers suggest that arousal is an essential antecedent to selective and
divided attention.
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Researchers have considered both the speed and the accuracy of information
processing to be important factors in intelligence. Attention always plays a
role because people must pay attention to a stimulus and then decide how to react
to it. Let’s look at how attention influences processing time and accuracy of
responses.
Inspection Time
Inspection time is the amount of time it takes you to inspect items and make a
decision about them (Gregory, Nettelbeck & Wilson, 2009; Neubauer & Fink,
2005). Essentially, the task requires concentrated bursts of focused attention. Here
is a typical way researchers measure inspection time: For each of a number of trials,
a computer monitor displays a fixation cue (a dot in the area where a target figure
will appear) for half a second. Then there is a short pause. Afterward, the computer
presents the target stimulus—two lines of differing lengths joined by a vertical bar at
the top—for a particular interval of time. Finally, the computer presents a visual
mask (a stimulus that erases the trace in iconic memory). The task of the participant
is to decide which of the two lines is longer. The answer is indicated by pressing
a left-hand or right-hand button on a keypad. The key variable here is actually
the length of time for the presentation of the target stimulus, not the speed of
responding by pressing the button. The inspection time is the length of time for
presentation of the target stimulus after which the participant still responds with at
least 90% accuracy. Nettelbeck found that shorter inspection times correlate with
higher scores on intelligence tests (e.g., various subscales of the Wechsler Adult
Intelligence Scale) among differing populations of participants (Nettelbeck, 1987;
Williams et al., 2009).
Reaction Time
Some investigators have proposed that intelligence can be understood in terms of
speed of neuronal conduction (e.g., Jensen, 1979, 1998). In other words, the smart
person is someone whose neural circuits conduct information rapidly. When Arthur
Jensen proposed this notion, direct measures of neural-conduction velocity were
not readily available. So Jensen primarily studied a proposed proxy for measuring
neural-processing speed. The proxy was choice reaction time—the time it takes to select one answer from among several possibilities. In such a task, one needs to attend
in a focused and concentrated way on visual displays. Consider a typical choicereaction-time paradigm.
The participant is seated in front of a set of lights on a board. When one of the
lights flashes, he or she extinguishes it by pressing as rapidly as possible a button
beneath the correct light. The experimenter would then measure the participant’s
speed in performing this task.
Participants with higher IQs are faster than participants with lower IQs in their
choice reaction time (CRT) (Jensen, 1982; Schmiedek et al., 2007). These findings
may be a function of increased central nerve-conduction velocity, although at
present this proposal remains speculative (Budak et al., 2005; Reed & Jensen,
1991, 1993; see also Rostad et al., 2007). Interestingly, a study has found even the
speed of the patellar reflex (knee-jerk response) to be significantly correlated with
intelligence, although this reflex does not necessitate any conscious thought
(McRorie & Cooper, 2001).
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When Our Attention Fails Us
The real importance of attention becomes clear in situations in which we cannot concentrate. Many studies involve normal participants. But cognitive neuropsychologists
also have learned a great deal about attentional processes in the brain by studying people who do not show normal attentional processes, such as people who show specific
attentional deficits and who are found to have either lesions or inadequate blood flow
in key areas of the brain. Overall, attention deficits have been linked to lesions in the
frontal lobe and in the basal ganglia (Lou, Henriksen, & Bruhn, 1984); visual attentional deficits have been linked to the posterior parietal cortex and the thalamus, as
well as to areas of the midbrain related to eye movements (Posner & Petersen, 1990;
Posner et al., 1988). Work with split-brain patients (e.g., Ladavas et al., 1994; Luck
et al., 1989) also has led to some interesting findings regarding attention and brain
function, such as the observation that the right hemisphere seems to be dominant
for maintaining alertness and that the attentional systems involved in visual search
seem to be distinct from other aspects of visual attention.
In the following sections, we will consider two examples of failing attention: attention deficit hyperactivity disorder and change/inattentional blindness.
Attention Deficit Hyperactivity Disorder (ADHD)
Most of us take for granted our ability to pay attention and to divide our attention
in adaptive ways. But not everyone can do so. People with attention deficit hyperactivity disorder (ADHD) have difficulties in focusing their attention in ways that enable
them to adapt in optimal ways to their environment (Attention deficit hyperactivity
disorder, 2009; see also Swanson et al., 2003).
The condition was first described by Dr. Heinrich Hoffman in 1845. Today, it has
been widely investigated. No one knows for sure the cause of ADHD. It may be a partially heritable condition. There is some evidence of a link to maternal smoking and
drinking of alcohol during pregnancy (Hausknecht et al., 2005; Obel et al., 2009;
Rodriguez & Bohlin, 2005). Lead exposure on the part of the child may also be associated with ADHD. Brain injury is another possible cause, as are food additives—in
particular, sugar and certain dyes (Cruz & Bahna, 2006; Nigg et al., 2008). There are
noted differences in the frontal-subcortical cerebellar catecholaminergic circuits and in
dopamine regulation in people with ADHD (Biederman & Faraone, 2005).
The three primary symptoms of ADHD are inattention, hyperactivity (i.e., levels of activity that exceed what is normally shown by children of a given age),
and impulsiveness. There are three main types of ADHD, depending on which
symptoms are predominant: (a) hyperactive-impulsive, (b) inattentive, and (c) a
combination of hyperactive-impulsive and inattentive behavior. We will focus on
the inattentive type here because it is most relevant to the topic of this chapter.
Children with the inattentive type of ADHD show several distinctive symptoms:
They are easily distracted by irrelevant sights and sounds.
They often fail to pay attention to details.
They are susceptible to making careless mistakes in their work.
They often fail to read instructions completely or carefully.
They are susceptible to forgetting or losing things they need for tasks, such as
pencils or books.
• They tend to jump from one incomplete task to another.
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Up to 20% of all children worldwide may be affected by ADHD (attention deficit hyperactivity disorder).
Studies have shown that children with ADHD exhibit slower and more variable reaction times than their siblings who are not affected by the disorder (Andreou, 2007).
ADHD typically first displays itself during the preschool or early school years. It is
estimated that about 5% of children worldwide have the disorder, though estimates
range widely from less than 3% to more than 20% (Polanczyk & Jensen, 2008). The
disorder does not typically end in adulthood, although it may vary in its severity,
becoming either more or less severe. There is some evidence that the incidence of
ADHD has increased in recent years. During the period from 2000 to 2005, the prevalence of medicinal treatment increased by more than 11% each year (Castle et al.,
2007). The reasons for this increase are not clear. Various hypotheses have been put
forward, including increased watching of fast-paced television shows, use of fast-paced
video games, additives in foods, and increases in unknown toxins in the environment.
ADHD is most often treated with a combination of psychotherapy and drugs.
Some of the drugs currently used to treat ADHD are Ritalin (methylphenidate), Metadate (methylphenidate), and Strattera (atomoxetine). This last drug differs from
other drugs used to treat ADHD in that it is not a stimulant. Rather, it affects the
neurotransmitter norepinephrine. The stimulants, in contrast, affect the neurotransmitter dopamine. Interestingly, in children, the rate of boys who are given medication for treatment of ADHD is more than double that of girls. However, in adults,
the use of ADHD medication is approximately equal for both sexes (Castle et al.,
2007). A number of studies have noted that, although medication is a useful tool
in the treatment of ADHD, it is best used in combination with behavioral interventions (Corcoran & Dattalo, 2006; Rostain & Tamsay, 2006).
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The theory of multiple intelligences (Gardner, 1985) has proven to be especially
helpful in the treatment and support of children with ADHD. Gardner has suggested
that intelligence comprises multiple independent constructs, not just a single, unitary construct. However, instead of speaking of multiple abilities that together constitute intelligence (e.g., Thurstone, 1938), this theory distinguishes eight distinct
intelligences that are relatively independent of each other: linguistic, logicalmathematical, naturalist, interpersonal, intrapersonal, spatial, musical, and bodilykinesthetic intelligences. Each intelligence is alleged to form a separate system of
functioning, although these systems can interact to produce what we see as intelligent performance. By concentrating on the students’ abilities (or predominant intelligences) in educational interventions, the achievements of students with ADHD
can be increased and their strengths can be emphasized (Davidson & Kemp, 2010;
Schirduan & Case, 2004).
Change Blindness and Inattentional Blindness
Evolutionarily, our ability to spot predators as well as to detect food sources has been
a great advantage for our survival. Adaptive behavior requires us to be attentive to
changes in our environment because changes cue us to both opportunities and dangers. It thus may be surprising to discover that people can show remarkable levels of
change blindness, an inability to detect changes in objects or scenes that are being
viewed (Galpin et al., 2009; O’Regan, 2003). Closely related to change blindness is
inattentional blindness, which is a phenomenon in which people are not able to see
things that are actually there (Bressan & Pizzighello, 2008). You can find some examples for change blindness and inattentional blindness in Believe It or Not at the
very beginning of Chapter 1. Change and inattentional blindness are of major importance in traffic situations or during medical screenings, for example, where an
overlooked motorcycle or a mass in the body can have potentially fatal consequences. For more on change blindness, see Chapter 3.
Spatial Neglect—One Half of the World Goes Amiss
Imagine you are in a zoo with an acquaintance and you both look at the cages containing animals. Meanwhile, you are making comments to each other about the animals’ behavior. However, you soon notice that your friend is not aware of anything
that is occurring in the left side of your visual fields. It is not only that he does not
see the animals there; he is not even aware of their being there.
This condition is called spatial neglect or hemi-neglect. It is an attentional dysfunction in which participants ignore the half of their visual field that is contralateral to (on the opposite side of) the hemisphere of the brain that has a lesion. It is a
result mainly of unilateral lesions in the parietal and frontal lobes, most often in the
right hemisphere. One way to test for neglect is to give patients who are suspected of
suffering from neglect a sheet of paper with a number of horizontal lines. Patients are
then asked to bisect the lines precisely in the middle of each. Patients with lesions
in the right hemisphere tend to bisect the lines to the right of the midline. Patients
with lesions in the left hemisphere tend to bisect the lines to the left of the midline.
The reason is that the former group of patients does not see all of the lines to the
left, whereas the latter group does not see all of the lines to the right. Sometimes
patients miss the lines altogether (i.e., patients who neglect the entire visual field).
If patients are asked to copy little pictures they are presented with, they often draw
only one side of the picture (Figure 4.14).
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Figure 4.14
Drawing by a Person with Hemispatial Neglect.
This drawing is from a patient who is suffering from neglect. As you can see, he ignores part
of the clock.
Interestingly, when patients are presented with stimuli only to their right or their
left side, they often can perceive the stimuli, no matter which side they are on. This
means that they have no major visual-field defects. However, when stimuli are present
in both sides of the visual field, people with hemi-neglect suddenly ignore the stimuli
that are contralateral to their lesion (i.e., if the lesion is in the right hemisphere, they
neglect stimuli in the left visual field). This phenomenon is called “extinction.” The
reason for extinction may be that patients are not able to disengage their attention
from the stimulus in the ipsilateral field (the part of the visual field where the lesion
is) in order then to shift their attention to the contralateral visual field. Their attention gets “stuck” on the ipsilateral object so that they cannot shift attention to stimuli
that appear on the contralateral side. Fascinatingly, this finding holds true not only for
people’s perceptions in the external world, but also for their memories.
In a 1977 study conducted by Bisiach and Luzzatti, participants with neglect
were asked to describe the main square in their town. They described only one side
of the square, although when asked to describe it from opposite ends they demonstrated that they knew how both sides of the square looked.
There is no full consensus regarding which part of the brain is responsible for
the symptoms of neglect. Recent studies indicate that the posterior superior temporal
gyrus, insula, and basal ganglia, as well as the superior longitudinal fasciculus in the
parietal lobe are most likely connected with spatial neglect (Hillis, 2005, 2006;
Karnath et al., 2004; Shinoura et al., 2009).
CONCEPT CHECK
1. Why is attention important for humans?
2. What are the mistakes we can make when trying to detect a signal?
3. What is vigilance?
4. What is a feature search, and how does it differ from a conjunction search?
5. What is the difference between divided and selective attention?
6. What are filter theories of attention?
Dealing with an Overwhelming World—Habituation and Adaptation
167
Dealing with an Overwhelming World—Habituation
and Adaptation
Crossing a street, we need to see that suddenly there is a car racing around the corner and in our direction. When we interact with our family and friends, we want to
be aware of changes in their emotions and behavior so we can respond to them adequately. And yet, if we responded to every little change and stimulus in our environment, we would be quickly and completely overwhelmed.
The authors live close to a major hospital in Boston, and our ability to filter out
the noise of the many ambulances that are coming in, day and night, helps us preserve our good night’s sleep. So in a way, it is sometimes a blessing if there are stimuli to which we habituate (i.e., to which we get accustomed) so that we do not
notice them anymore.
Habituation involves our becoming accustomed to a stimulus so that we gradually
pay less and less attention to it. The counterpart to habituation is dishabituation.
In dishabituation, a change in a familiar stimulus prompts us to start noticing
the stimulus again. Both processes occur automatically. The processes involve no
conscious effort. The relative stability and familiarity of the stimulus govern these
processes. Any aspects of the stimulus that seem different or novel (unfamiliar)
PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
OVERCOMING BOREDOM
Habituation is not without faults. Becoming bored during a lecture or while reading a textbook is a sign of habituation. Your attention may start to wander to the background
noises, or you may find that you have read a paragraph or two with no recollection of
the content. Fortunately, you can dishabituate yourself with very little effort. Here are a
few tips on how to overcome the negative effects of boredom.
1. Take a break or alternate between different tasks. If you do not remember the last
few paragraphs you read in your text, it is time to stop for a few minutes. Go back
and mark the last place in the text you do remember and put the book down. If you
feel like a break is a waste of valuable time, do some other work for a while.
2. Take notes while reading or listening. Note-taking focuses attention on the material
more than simply listening or reading. If necessary, try switching from script to printed
handwriting to make the task more interesting.
3. Adjust your attentional focus to increase stimulus variability. Is the instructor’s voice
droning on endlessly so that you cannot take a break during lecture? Try noticing other
aspects of your instructor, like hand gestures or body movements, while still paying
attention to the content. Create a break in the flow by asking a question—even just
raising your hand can make a change in a lecturer’s speaking pattern. If all else fails,
you may have to force yourself to be interested in the material. Think about how you
can use the material in your everyday life. Also, sometimes just taking a few deep
breaths or closing your eyes for a few seconds can change your internal arousal levels.
What other tasks in your life tend to be boring? How can you use the tips above to benefit
more from these tasks?
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either prompt dishabituation or make habituation less likely to occur in the first
place. For example, suppose that a radio is playing instrumental music while you
study your cognitive psychology textbook. At first the sound might distract you.
But after a while you become habituated to the sound and scarcely notice it. If
the loudness of the noise were suddenly to change drastically, however, immediately you would dishabituate to it. The once familiar sound to which you had
been habituated would become unfamiliar. It thus would enter your awareness.
Habituation is not limited to humans. It is found in organisms as simple as the
mollusk Aplysia (Castellucci & Kandel, 1976).
We usually exert no effort to become habituated to our sensations of stimuli in
the environment. Nonetheless, although we usually do not consciously control habituation, we can do so. In this way, habituation is an attentional phenomenon that
differs from the physiological phenomenon of sensory adaptation. Sensory adaptation is a lessening of attention to a stimulus that is not subject to conscious control.
It occurs directly in the sense organ, not in the brain. We can exert some conscious
control over whether we notice something to which we have become habituated,
but we have no conscious control over sensory adaptation. For example, we cannot
consciously force ourselves to smell an odor to which our senses have become
adapted. Nor can we consciously force our pupils to adapt—or not adapt—to differing degrees of brightness or darkness. In contrast, if someone asked us,
“Who’s the lead guitarist in that song?” we can once again notice background music.
Table 4.3 provides some of the other distinctions between sensory adaptation and
habituation.
Two factors that influence habituation are internal variation within a stimulus
and subjective arousal. Some stimuli involve more internal variation than do others.
For example, background music contains more internal variation (changing melodies, harmonies, and rhythms) than does the steady drone of an air conditioner.
The relative complexity of the stimulus (e.g., an ornate, intricate oriental rug versus
Table 4.3
Differences between Sensory Adaptation and Habituation
Responses involving physiological adaptation take place mostly in our sense organs, whereas responses involving
cognitive habituation take place mostly in our brains (and relate to learning).
Adaptation
Habituation
Not accessible to conscious control
Example: You cannot decide how quickly to adapt to a
particular smell or a particular change in light intensity.
Accessible to conscious control
Example: You can decide to become aware of background
conversations to which you had become habituated.
Tied closely to stimulus intensity
Example: The more the intensity of a bright light increases,
the more strongly your senses will adapt to the light.
Not tied very closely to stimulus intensity
Example: Your level of habituation will not differ much in
your response to the sound of a loud fan and to that of a
quiet air conditioner.
Unrelated to the number, length,
and recency of prior exposures
Example: The sense receptors in your skin will respond to
changes in temperature in basically the same way no matter how many times you have been exposed to such
changes and no matter how recently you have experienced
such changes.
Tied very closely to the number, length,
and recency of prior exposures
Example: You will become more quickly habituated to the
sound of a chiming clock when you have been exposed to
the sound more often, for longer times, and on more recent
occasions.
Automatic and Controlled Processes in Attention
169
a gray carpet) does not seem to be important to habituation. Rather, what matters is
the amount of change within the stimulus over time. For example, a mobile involves
more change than does an ornate but rigid sculpture. Thus, it is also relatively difficult to remain continually habituated to the frequently changing noises coming from
a television. But it is relatively easy to become habituated to a constantly running
fan. The reason is that the voices typically speak animatedly and with great inflectional expression. They are constantly changing, whereas the sound a fan makes remains constant with little to no variation.
Psychologists can observe habituation occurring at the physiological level by
measuring our degree of arousal. Arousal is a degree of physiological excitation, responsivity, and readiness for action, relative to a baseline. Arousal often is measured
in terms of heart rate, blood pressure, electroencephalograph (EEG) patterns, and
other physiological signs. Consider what happens, for example, when an unchanging
visual stimulus remains in our visual field for a long time. Our neural activity (as
shown on an EEG) in response to that stimulus decreases. Both neural activity and
other physiological responses (e.g., heart rate) can be measured. These measurements
detect heightened arousal in response to perceived novelty or diminished arousal in
response to perceived familiarity.
Psychologists in many fields use physiological indications of habituation to study
a wide array of psychological phenomena in people (e.g., infants, or comatose
patients) who cannot provide verbal reports of their responses. Physiological indicators of habituation tell the researcher whether the person notices changes in the
stimulus. Such changes might occur in the color, pattern, size, or form of a stimulus.
These indicators signal whether the person notices the changes at all, as well as what
specific changes the person notices in the stimulus.
Without habituation, our attentional system would be much more greatly taxed.
How easily would we function in our highly stimulating environments if we could
not habituate to familiar stimuli? Imagine trying to listen to a lecture if you could
not habituate to the sounds of your own breathing, the rustling of papers and books,
or the faint buzzing of fluorescent lights.
An example of the failure to habituate can be seen in persons who suffer from
tinnitus (ringing in the ears). People who complain of having tinnitus seem to have
problems habituating to auditory stimuli. Many people have ringing in their ears,
and if they are placed in a quiet room, will report a buzzing or other sounds. However, people who chronically suffer from tinnitus have difficulty adapting to the
noise (Bessman et al., 2009; Walpurger et al., 2003). Evidence also indicates that
people with attention deficit hyperactivity disorder (ADHD) have difficulty habituating to many types of stimuli. This difficulty helps to explain why ordinary stimuli,
such as the buzzing of fluorescent lights, can be distracting to a person with ADHD
(Jansiewicz et al., 2004).
Automatic and Controlled Processes in Attention
As we have seen, our attention is capable of processing only so many things at once.
There are attentional filters that filter out irrelevant stimuli to enable us to process
in depth what is important to us. To help us navigate our environment more successfully, we automatize many processes so that we can execute them without using
up resources that then can be spent on other processes. Therefore, it is useful to
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differentiate cognitive processes in terms of whether they do or do not require conscious control (Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977).
Automatic and Controlled Processes
Automatic processes like writing your name involve no conscious control (Palmeri,
2003). For the most part, they are performed without conscious awareness. Nevertheless, you may be aware that you are performing them. They demand little or no
effort or even intention. Multiple automatic processes may occur at once, or at least
very quickly, and in no particular sequence. Thus, they are termed parallel processes.
You are able to read this text while at the same time sharpening your pencil and
scratching your leg with your foot.
In contrast, controlled processes are accessible to conscious control and even
require it. Such processes are performed serially, for example, when you want to
compute the total cost of a trip you are about to book online. In other words, controlled processes occur sequentially, one step at a time. They take a relatively long
time to execute, at least as compared with automatic processes.
Three attributes characterize automatic processes (Posner & Snyder, 1975).
First, they are concealed from consciousness. Second, they are unintentional. Third,
they consume few attentional resources.
An alternative view of attention suggests a continuum of processes between fully
automatic processes and fully controlled processes. For one thing, the range of controlled processes is so wide and diverse that it would be difficult to characterize all
the controlled processes in the same way (Logan, 1988). Also, some automatic processes are easy to retrieve into consciousness and can be controlled intentionally,
whereas others are not accessible to consciousness and/or cannot be controlled intentionally. Table 4.4 summarizes the characteristics of controlled versus automatic
processes.
Many tasks that start off as controlled processes eventually become automatic
ones as a result of practice (LaBerge, 1975, 1990; Raz, 2007). This process is called
automatization (also termed proceduralization). For example, driving a car is initially
a controlled process. Once we master driving, however, it becomes automatic under
normal driving conditions. Such conditions involve familiar roads, fair weather, and
little or no traffic. Similarly, when you first learn to speak a foreign language, you
need to translate word-for-word from your native tongue. Eventually, however, you
begin to think in the second language. This thinking enables you to bypass the
intermediate-translation stage. It also allows the process of speaking to become automatic. Your conscious attention can revert to the content, rather than the process,
of speaking. A similar shift from conscious control to automatic processing occurs
when acquiring the skill of reading. However, when conditions change, the same
activity may again require conscious control. In the driving example, if the roads
become icy, you will likely need to pay attention to when you need to brake or accelerate. Both tasks usually are automatic when driving.
According to Sternberg’s theory of triarchic intelligence (1999), relatively novel
tasks that have not been automatized—such as visiting a foreign country, mastering
a new subject, or acquiring a foreign language—make more demands on intelligence
than do tasks for which automatic procedures have been developed. A completely
unfamiliar task may demand so much of the person as to be overwhelming.
Automatic and Controlled Processes in Attention
171
IN THE LAB OF JOHN F. KIHLSTROM
Posthypnotic Amnesia
Others were equally likely to elicit control
words that had not been studied. Despite
Hypnosis is a special state of conscioustheir inability to remember the words they
ness in which subjects may see things that
had just studied, the hypnotizable, amnesic
aren’t there, fail to see things that are
subjects produced items from the study list at
there, and respond to posthypnotic sugthe same rate as the insusceptible, nonamnegestions without knowing what they are
sic subjects. This shows that posthypnotic
doing or why (Kihlstrom, 2007, 2008).
amnesia is a disruption of episodic, but not
Afterward, they may be unable to rememJOHN F. KIHLSTROM
semantic, memory. Even more important, the
ber the things they did while they were
subjects showed semantic priming, respondhypnotized—a phenomenon known as posthypnotic
ing with items from the study list more often compared to
amnesia, which has been a major focus of my work.
other items that they had not previously studied. The magFirst, however, we have to find the right subjects.
nitude of the priming effect was the same in the hypnotizUnfortunately, there is no way to predict in advance
able, amnesic subjects as it was in the insusceptible,
who can experience hypnosis and who cannot. The
nonamnesic subjects. In other words, posthypnotic amneonly way to find out is to try hypnosis and see if it works.
sia entails a dissociation between explicit and implicit exFor this purpose, we rely on a set of standardized scales
pressions of episodic memory (Schacter, 1987).
of hypnotic susceptibility. These are performance-based
While explicit and implicit memory is dissociated in
tests structured much like tests of intelligence. Each scale
other forms of amnesia, the dissociation observed in postbegins with an induction of hypnosis, followed by a
hypnotic amnesia has some features that make it special.
series of suggestions for various hypnotic experiences.
Most studies of implicit memory in neurologically intact
Response to each suggestion is evaluated according to
subjects employ highly degraded encoding conditions,
standardized, behavioral criteria, yielding a total score
such as shallow processing, to impair explicit memory.
representing the person’s ability to experience hypnosis.
But in our experiments, the subjects deliberately memoFrom this point on, however, our experiments on cogrized the list to a strict criterion of learning before the amnition look just like anyone else’s—except that our subjects
nesia suggestion was given, and they remembered the
are hypnotized. In one study using a familiar verballist perfectly well after the amnesia suggestion was canlearning paradigm (Kihlstrom, 1980), the subjects memoceled. Thus, implicit memory can be dissociated from exrized a list of 15 familiar words, such as girl or chair, and
plicit memory even under deep processing conditions.
then received a suggestion that “You will not be able to
More important, most studies of implicit memory in
remember that you learned any words while you were hypamnesia focus on repetition priming, which can be
notized … until I say to you, ‘Now you can remember
mediated by a perception-based representation of the
everything.‘” After coming out of hypnosis, highly hypnotizprime. Accordingly, some of the most popular theories
able subjects remembered virtually none of the study list,
of implicit memory focus on perceptual representation syswhereas insusceptible subjects, who had gone through
tems in the brain. But in our original study, the priming
the same procedures, remembered it almost perfectly.
was semantic in nature and must have been mediated
This shows that the occurrence of posthypnotic amnesia
by a meaning-based representation of the prime. In this
is highly correlated with hypnotizability.
way, studies of hypnosis remind us that a comprehensive
Then, we presented the subjects with a word associatheory of implicit memory is going to have to go beyond
tion test, in which they were asked to report the first word
repetition priming and beyond perceptual representation
that came to mind. Some of the cues were words like boy
systems.
or chair, which were likely to elicit items from the study list.
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CHAPTER 4 • Attention and Consciousness
Table 4.4
Controlled versus Automatic Processes
There is probably a continuum of cognitive processes, from fully controlled processes to fully automatic ones; these
features characterize the polar extremes of each.
Characteristics
Controlled Processes
Automatic Processes
Amount of intentional effort
Require intentional effort
Require little or no intention or effort (and
intentional effort may even be required to
avoid automatic behaviors)
Degree of conscious
awareness
Require full conscious awareness
Generally occur outside of conscious
awareness, although some automatic processes may be available to consciousness
Use of attentional resources
Consume many attentional resources
Consume negligible attentional resources
Type of processing
Performed serially (one step at a time)
Performed by parallel processing (i.e., with
many operations occurring simultaneously
or at least in no particular sequential order)
Speed of processing
Relatively time-consuming execution, as
compared with automatic processes
Relatively fast
Relative novelty of tasks
Novel and unpracticed tasks or tasks with
many variable features
Familiar and highly practiced tasks, with
largely stable task characteristics
Level of processing
Relatively high levels of cognitive processing (requiring analysis or synthesis)
Relatively low levels of cognitive processing
(minimal analysis or synthesis)
Difficulty of tasks
Usually difficult tasks
Usually relatively easy tasks, but even relatively complex tasks may be automatized,
given sufficient practice
Process of acquisition
With sufficient practice, many routine and relatively stable procedures may become automatized, such that highly controlled processes may become partly or even wholly automatic; naturally, the amount of practice required for automatization increases dramatically
for highly complex tasks
Suppose, for example, you were visiting a foreign country. You probably would not
profit from enrolling in a course with unfamiliar abstract subject matter taught in a
language you do not understand. The most intellectually stimulating tasks are those
that are challenging and demanding but not overwhelming.
How Does Automatization Occur?
How do processes become automatized? A widely accepted view has been that during the course of practice, implementation of the various steps becomes more efficient. The individual gradually combines individual effortful steps into integrated
components that are further integrated until the whole process is one single operation (Anderson, 1983; Raz, 2007). This operation requires few or no cognitive resources, such as attention. This view of automatization seems to be supported by
one of the earliest studies of automatization (Bryan & Harter, 1899). This study
investigated how telegraph operators gradually automatized the task of sending and
receiving messages. Initially, new operators automatized the transmission of individual letters. However, once the operators had made the transmission of letters
Automatic and Controlled Processes in Attention
173
automatic, they automatized the transmission of words, phrases, and then other
groups of words.
An alternative explanation, called “instance theory,” has been proposed by Logan
(1988). Logan suggested that automatization occurs because we gradually accumulate
knowledge about specific responses to specific stimuli. For example, when a child
first learns to add or subtract, he or she applies a general procedure—counting—for
handling each pair of numbers. Following repeated practice, the child gradually
stores knowledge about particular pairs of particular numbers. Eventually, the child
can retrieve from memory the specific answers to specific combinations of numbers.
Nevertheless, he or she still can fall back on the general procedure (counting) as
needed. Similarly, when learning to drive, the person can draw on an accumulated
wealth of specific experiences. These experiences form a knowledge base from which
the person quickly can retrieve specific procedures for responding to specific stimuli,
such as oncoming cars or stoplights. Preliminary findings suggest that Logan’s instance
theory may better explain specific responses to specific stimuli, such as calculating
arithmetic combinations (Logan, 1988).
The effects of practice on automatization show a negatively accelerated curve.
In such a curve, early practice effects are great. Later practice effects make less and
less difference in the degree of automatization. A graph of improvement in performance would show a steeply rising curve early on, and the curve would eventually
level off (Figure 4.15). Clearly, automatic processes generally govern familiar, wellpracticed as well as easy tasks. Controlled processes govern relatively novel as well as
difficult tasks. Because highly automatized behaviors require little effort or conscious
control, we often can engage in multiple automatic behaviors. But we rarely can engage in more than one labor-intensive controlled behavior.
Practice effects (arbitrary units)
100
4 units
9 units
80
12 units
60
16 units
40
20 units
20
25 units
0
1
Figure 4.15
2
3
4
5
6
7
Blocks of trials
8
9
10
The Practice Effect.
The rate of improvement caused by practice effects shows a pattern of negative acceleration.
The negative acceleration curve attributed to practice effects is similar to the curve shown
here, indicating that the rate of learning slows down as the amount of learning increases,
until eventually learning peaks at a stable level.
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CHAPTER 4 • Attention and Consciousness
Automatization in Everyday Life
Automatization of tasks like reading is not guaranteed, even with practice. In the
case of dyslexia, for example, automatization is impaired. Persons who have dyslexia
frequently have difficulty completing tasks, in addition to reading, that are normally
automated (Brambati et al., 2006; Ramus et al., 2003; van der Leij, de Jong, &
Rijswijk-Prins, 2001).
Sometimes, automatization in reading can work against us, however. One demonstration of this is the Stroop effect, which is named after John Ridley Stroop
(1935). The task works as follows: Quickly read aloud the following words: brown,
blue, green, red, purple. Easy, isn’t it? Now quickly name aloud the colors shown in
part (a) of the top figure on the back endpaper of this book. In this figure, the colored ink matches the name of the color word. This task, too, is easy. Now, look at
part (c) of the same figure. Here, the colors of the inks differ from the color names
that are printed with them. Again, name the ink colors you see, out loud, as quickly
as possible.
You probably will find the task very difficult: Each of the written words interferes with your naming the color of the ink. The Stroop effect demonstrates the psychological difficulty in selectively attending to the color of the ink and trying to
ignore the word that is printed with the ink of that color. One explanation of why
the Stroop test may be particularly difficult is that, for you and most other adults,
reading is now an automatic process. It is not readily subject to your conscious control (MacLeod, 1996, 2005). For that reason, you find it difficult intentionally to
refrain from reading and instead to concentrate on identifying the color of the ink,
disregarding the word printed in that ink color. An alternative explanation is that
the output of a response occurs when the mental pathways for producing the response are activated sufficiently (MacLeod, 1991). In the Stroop test, the color
word activates a cortical pathway for saying the word. In contrast, the ink-color
name activates a pathway for naming the color. But the former pathway interferes
with the latter. In this situation, it takes longer to gather sufficient strength of activation to produce the color-naming response and not the word-reading response.
A number of variations of the Stroop effect exist, including the number Stroop,
the directional Stroop, the animal Stroop, and the emotional Stroop. Theses tasks are
very similar to the standard Stroop. For example, in the number Stroop, number words
are used. Thus, the word two might be written three times, two two two, and the participant be asked to count the number of words. As with the standard Stroop task, reading
sometimes interferes with the counting task (Girelli et al., 2001; Kaufmann & Nuerk,
2006). One of the most extensively used Stroop variations is the emotional Stroop. In
this task, the standard task is modified so that the color words are replaced with either
emotional or neutral words. Participants are asked to name the colors of the words.
Researchers find that there is a longer delay in color naming for emotional words as
compared with neutral words. These findings suggest that the automatic reading of
emotional words causes more interference than reading of neutral words (Bertsch
et al., 2009; Phaf & Kan, 2007; Thomas, Johnstone, & Gonsalvez, 2007).
In some situations, however, automatic processes may be life saving. Therefore,
it is important to automate safety practices (Norman, 1976). This is particularly true
for people engaging in high-risk occupations, such as pilots, undersea divers, and
firefighters. For example, novice divers often complain about the frequent repetition
of various safety procedures within the confines of a swimming pool, like releasing a
Automatic and Controlled Processes in Attention
175
cumbersome weight belt. However, the practice is important so the divers can rely
on automatic processes in the face of potential panic should they confront a lifethreatening deep-sea emergency.
But there are other situations where automatization may result in “mindlessness”
and may be life threatening (Kontogiannis & Malakis, 2009; Krieger, 2005; Langer,
1989, 1997): In 1982, a pilot and copilot went through a routine checklist prior to
takeoff. They mindlessly noted that the anti-icer was “off,” as it should be under
most circumstances. But it should not have been off under the icy conditions in
which they were preparing to fly. The flight ended in a crash that killed 74 passengers. Typically, our absentminded implementation of automatic processes has far less
lethal consequences. For example, when driving, we may end up routinely driving
home instead of stopping by the store, as we had intended to do. Or we may pour
a glass of milk and then start to put the carton of milk in the cupboard rather than
in the refrigerator.
Mistakes We Make in Automatic Processes
An extensive analysis of human error shows that errors can be classified either as
mistakes or as slips (Reason, 1990). Mistakes are errors in choosing an objective or
in specifying a means of achieving it. Slips are errors in carrying out an intended
means for reaching an objective. Suppose you decided that you did not need to
study for an examination. Thus, you purposely left your textbook behind when leaving for a long weekend. But then you discovered at the time of the exam that you
should have studied. In Reason’s terms, you made a mistake. However, suppose instead you fully intended to bring your textbook with you. You had planned to study
extensively over the long weekend, but in your haste to leave, you accidentally left
the textbook behind. That would be a slip. In sum, mistakes involve errors in intentional, controlled processes. Slips often involve errors in automatic processes (Reason, 1990).
There are several kinds of slips (Norman, 1988; Reason, 1990; see Table 4.5). In
general, slips are most likely to occur when two circumstances occur. First, when we
must deviate from a routine and automatic processes inappropriately override intentional, controlled processes. Second, when our automatic processes are interrupted.
Such interruptions are usually a result of external events or data, but sometimes they
are a result of internal events, such as highly distracting thoughts. Imagine that you
are typing a paper after an argument with a friend. You may find yourself pausing in your
typing as thoughts about what you should have said interrupt your normally automatic
process of typing. Automatic processes are helpful to us under many circumstances.
They save us from needlessly focusing attention on routine tasks, such as tying our shoes
or dialing a familiar phone number. We are thus unlikely to forgo them just to avoid
occasional slips. Instead, we should attempt to minimize the costs of these slips.
How can we minimize the potential for negative consequences of slips? In everyday situations, we are less likely to slip when we receive appropriate feedback from
the environment. For example, the milk carton may be too tall for the cupboard
shelf, or a passenger may say, “I thought you were stopping at the store before going
home.” If we can find ways to obtain useful feedback, we may be able to reduce the
likelihood that harmful consequences will result from slips. A particularly helpful
kind of feedback involves forcing functions. These are physical constraints that
make it difficult or impossible to carry out an automatic behavior that may lead to
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CHAPTER 4 • Attention and Consciousness
Table 4.5
Slips Associated with Automatic Processes
Occasionally, when we are distracted or interrupted during implementation of an automatic process, slips occur.
However, in proportion to the number of times we engage in automatic processes each day, slips are relatively rare
events (Reason, 1990).
Type of Error
Description of Error
Example of Error
Capture errors
We intend to deviate from a routine activity
we are implementing in familiar surroundings,
but at a point where we should depart from
the routine we fail to pay attention and to
regain control of the process; hence, the automatic process captures our behavior, and
we fail to deviate from the routine.
Psychologist William James (1890/1970,
cited in Langer, 1989) gave an example in
which he automatically followed his usual
routine, undressing from his work clothes, then
putting on his pajamas and climbing into
bed—only to realize that he had intended to
remove his work clothes to dress to go out
to dinner.
Omissions*
An interruption of a routine activity may cause
us to skip a step or two in implementing the
remaining portion of the routine.
When going to another room to retrieve
something, if a distraction (e.g., a phone call)
interrupts you, you may return to the first room
without having retrieved the item.
Perseverations*
After an automatic procedure has been completed, one or more steps of the procedure
may be repeated.
If, after starting a car, you become distracted,
you may turn the ignition switch again.
Description errors
An internal description of the intended behavior leads to performing the correct action
on the wrong object.
When putting away groceries, you may end
up putting the ice cream in the cupboard and
a can of soup in the freezer.
Data-driven errors
Incoming sensory information may end up
overriding the intended variables in an automatic action sequence.
While intending to dial a familiar phone
number, if you overhear someone call out another series of numbers, you may end up dialing some of those numbers instead of the
ones you intended to dial.
Associative-activation
errors
Strong associations may trigger the wrong
automatic routine.
When expecting someone to arrive at the
door, if the phone rings, you may call out,
“Come in!”
Loss-of-activation
errors
The activation of a routine may be insufficient
to carry it through to completion.
All too often, each of us has experienced the
feeling of going to another room to do something and getting there only to ask ourselves,
“What am I doing here?” Perhaps even worse
is the nagging feeling, “I know I should be
doing something, but I can’t remember what.”
Until something in the environment triggers our
recollection, we may feel extremely frustrated.
*
Omissions and perseverations may be considered examples of errors in the sequencing of automatic processes. Related errors include
inappropriately sequencing the steps, as in trying to remove socks before taking off shoes.
a slip (Norman, 1988). For example, some modern cars make it difficult or impossible to drive the car without wearing a seatbelt. You can devise your own forcing
functions. You may post a small sign on your steering wheel as a reminder to run
an errand on the way home. Or you may put items in front of the door to block
your exit so that you cannot leave without the items you want.
Consciousness
177
Over a lifetime, we automatize countless everyday tasks. However, one of the
most helpful pairs of automatic processes first appears within hours after birth: habituation and its complementary opposite, dishabituation.
Consciousness
Not everything we do, reason, and perceive is necessarily conscious. We may be unaware of stimuli that alter our perceptions and judgments or unable to come up with
the right word in a sentence even though we know that we know the right word.
This section will explore the consciousness of mental processes and how preconscious processing can influence our mind.
The Consciousness of Mental Processes
No serious investigator of cognition believes that people have conscious access to
very simple mental processes. For example, none of us has a good idea of the means
by which we recognize whether a printed letter such as A is an uppercase or lowercase one. But now consider more complex processing. How conscious are we of our
complex mental processes? Cognitive psychologists have differing views on how this
question is best answered.
One view (Ericsson & Simon, 1984) is that people have quite good access to
their complex mental processes. Simon and his colleagues, for example, have used
protocol analysis in analyzing people’s solving of problems, such as chess problems and
so-called cryptarithmetic problems, in which one has to figure out what numbers
substitute for letters in a mathematical computation problem. These investigations
have suggested to Simon and his colleagues that people have quite good conscious
access to their complex information processes.
A second view is that people’s access to their complex mental processes is not
very good (e.g., Nisbett & Wilson, 1977). In this view, people may think they know
how they solve complex problems, but their thoughts are frequently erroneous.
According to Nisbett and Wilson, we typically are conscious of the products of our
thinking, but only vaguely conscious, if at all, of the processes of thinking. For example, suppose you decide to buy one model of bicycle over another. You certainly
will know the product of the decision—which model you bought. But you may have
only a vague idea of how you arrived at that decision. Indeed, according to this
view, you may believe you know why you made the decision, but that belief is likely
to be flawed. Advertisers depend on this second view. They try to manipulate your
thoughts and feelings toward a product so that, whatever your conscious thoughts
may be, your unconscious ones will lead you to buy their product over that of a competitor. The essence of the second view is that people’s conscious access to their
thought processes, and even their control over their thought processes, is quite minimal (Levin, 2004; Wegner, 2002; Wilson, 2002). Consider the problem of getting
over someone who has terminated an intimate relationship with you. One technique
that is sometimes used to get over someone is thought suppression. As soon as you
think of the person, you try to put the individual out of your mind. There is one
problem with this technique, but it is a major one: It often does not work. Indeed,
the more you try not to think about the person, the more you may end up thinking
about him or her and having trouble getting the person off your mind. Research has
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CHAPTER 4 • Attention and Consciousness
actually shown that trying not to think about something usually does not work
(Tomlinson et al., 2009; Wegner, 1997a, 1997b). Ironically, the more you try not
to think about someone or something, the more “obsessed” you may become with
the person or object.
Preconscious Processing
Some information that currently is outside our conscious awareness still may be available to consciousness or at least to cognitive processes. For example, when you comb
your hair while getting ready for a first date, you are still able to do the combing although your mind in all likelihood will be completely elsewhere, namely, on the date.
The information about how to comb your hair is available to you even if you are not
consciously combing. Information that is available for cognitive processing but that
currently lies outside conscious awareness exists at the preconscious level of awareness.
Preconscious information includes stored memories that we are not using at a given
time but that we could summon when needed. For example, when prompted, you
can remember what your bedroom looks like. But obviously you are not always consciously thinking about your bedroom (unless, perhaps, you are extremely tired). Sensations, too, may be pulled from preconscious to conscious awareness. For example,
before you read this sentence, were you highly aware of the sensations in your right
foot? Probably not. However, those sensations were available to you.
Studying the Preconscious—Priming
How can we study things that currently lie outside conscious awareness? Psychologists have solved this problem by studying a phenomenon known as priming. In
priming, participants are presented with a first stimulus (the prime), followed by a
break that can range from milliseconds to weeks or months. Then, the participants
are presented with a second stimulus and make a judgment (e.g., are both the first
and the second stimulus the same?) to see whether the presentation of the first
stimulus affected the perception of the second (Neely, 2003). The thought behind
this procedure is that the presentation of the first stimulus may activate related concepts in memory that are then more easily accessible. Suppose, for example, someone
is talking to you about how much he has enjoyed watching television since buying a
satellite dish. He speaks at length about the virtues of satellite dishes. Later, you hear
the word dish. You are probably more likely to think of a satellite dish, as opposed to
a dish served at dinner, than is someone who did not hear the prior conversation
about satellite dishes. Most priming is positive in that the first stimulus facilitates
later recognition. But priming on occasion may be negative and impede later recognition. For example, if you are asked to solve several algebra problems that can be
solved by the same formula, and then you are asked to solve another problem that
requires another formula, you may be negatively primed relative to someone who did
not solve the first set of problems with the now-irrelevant formula.
Sometimes we are aware of the priming stimuli. However, priming occurs even
when the priming stimulus is presented in a way that does not permit its entry into
conscious awareness (e.g., it is presented too briefly to be registered consciously).
Let us look at some studies that have used priming. Marcel (1983a, 1983b), for
example, observed processing of stimuli that were presented too briefly to be detected
in conscious awareness (Marcel, 1983a, 1983b). In one study, Marcel presented participants with a prime that had two different meanings. One such prime could be the
Consciousness
179
word palm which can refer both to a body part and a plant. Afterward, participants
were presented with another word that they were asked to classify into various categories. For participants who had consciously seen the prime, the mental pathway to one
of the two meanings (e.g., plant) became activated and facilitated (speeded up) the
classification of a subsequent related word. The pathway to the other meaning (e.g.,
body part) showed a negative priming effect in that it inhibited (slowed down) the
classification of a subsequent unrelated word. For example, if the word palm was presented, the word either facilitated or inhibited the classification of the word wrist, depending on whether the participant associated palm with hand or with tree. In
contrast, if the word palm was presented so briefly that the person was unaware of
seeing the word, both meanings of the word appeared to be activated.
Another example of possible priming effects and preconscious processing can be
found in a study described as a test of intuition. This study used a “dyad of triads” task
(Bowers et al., 1990). Participants were presented with pairs (dyads) of three-word
groups (triads). One of the triads in each dyad was a potentially coherent grouping.
The other triad contained random and unrelated words. For example, the words in
Group A, a coherent triad, might have been playing, credit, and report. The words in
Group B, an incoherent triad, might have been still, pages, and music. (The words in
Group A can be meaningfully paired with a fourth word—card [playing card, credit
card, report card]; the words in Group B bear no such relationship.) After presentation of the dyad of triads, participants were shown various possible choices for a fourth
word related to one of the two triads. The participants then were asked to identify
which of the two triads was coherent and related to a fourth word, and which fourth
word linked the coherent triad. Some participants could not figure out the unifying
fourth word for a given pair of triads. They were nevertheless asked to indicate
which of the two triads was coherent. When participants could not ascertain the
unifying word, they still were able to identify the coherent triad at a level well
above chance. They seemed to have some preconscious information available to
them. This information led them to select one triad over the other. They did so
even though they did not consciously know what word unified that triad.
The examples described here involve visual priming. Priming, however, does not
have to be visual. Priming effects can be demonstrated using aural material as well.
Experiments exploring auditory priming reveal the same behavioral effects as visual
priming. Using neuroimaging methods, investigators have discovered that similar
brain areas are involved in both types of priming (Badgaiyan, Schacter, & Alpert,
1999; Bergerbest, Ghahremani, & Gabrieli, 2004).
An interesting application of auditory priming was used with patients under anesthesia. While under anesthesia, these patients were presented lists of words. After
awakening from anesthesia, the patients were asked yes/no questions and word-stem
completion questions about the words they heard. The patients performed at chance
on the yes/no questions. They reported no conscious knowledge of the words. However, on the word-stem completion task, patients showed evidence of priming. The
patients frequently completed the word-stems with the items they were presented
while they were under anesthesia. These findings reveal that, even when the patient
has absolutely no recollection of an aural event, that event still can affect performance (Deeprose et al., 2005).
What’s That Word Again? The Tip-of-the-Tongue Phenomenon
Unfortunately, sometimes pulling preconscious information into conscious awareness is not easy. Most of you probably have experienced the tip-of-the-tongue
CHAPTER 4 • Attention and Consciousness
phenomenon, in which you try to remember something that is stored in memory but
that cannot readily be retrieved. Psychologists have tried to generate experiments
that measure this phenomenon (see Hanley & Chapman, 2008, for example). In
one classic study (Brown & McNeill, 1966), participants were read a large number
of dictionary definitions. For example, they might have been given the clue, “an instrument used by navigators to measure the angle between a heavenly body and a
horizon.” The subjects then were asked to identify the corresponding words having
these meanings. This procedure constituted a game similar to the television show
Jeopardy. Some participants could not come up with the word but thought they
knew it. Still, they often could identify the first letter, the number of syllables, or approximate the word’s sounds. For example, it begins with an s, has two syllables, and
sounds like sextet. Eventually, some participants realized that the sought-after word
was sextant. These results indicate that particular preconscious information, although
not fully accessible to conscious thinking, is still available to attentional processes.
The tip-of-the-tongue phenomenon is apparently universal. It is seen in speakers
of many different languages. Bilingual people experience more tip-of-the-tongues
than monolingual speakers which may be because bilinguals use either one of their
languages less frequently than do monolinguals (Pyers et al., 2009). It is also seen in
people with limited or no ability to read (Brennen, Vikan, & Dybdahl, 2007). Older
adults have more tip-of-the-tongue experiences compared with younger adults
(Galdo-Alvarez et al., 2009; Gollan & Brown, 2006). The anterior cingulateprefrontal cortices are involved when one is experiencing the tip-of-the-tongue
Hagen/www.CartoonStock.com
180
In the tip-of-the-tongue phenomenon, you cannot think of a word or phrase that is stored in your memory
and usually easily accessible.
Consciousness
181
phenomenon. This is likely due to high-level cognitive mechanisms being activated
in order to resolve the retrieval failure (Maril, Wagner, & Schacter, 2001).
When Blind People Can See
Preconscious perception also has been observed in people who have lesions in some
areas of the visual cortex (Rees, 2008; Ro & Rafal, 2006). Typically, the patients are
blind in areas of the visual field that correspond to the lesioned areas of the cortex.
Some of these patients, however, seem to show blindsight—traces of visual perceptual ability in blind areas (Kentridge, 2003). When forced to guess about a stimulus
in the “blind” region, they correctly guess locations and orientations of objects at
above-chance levels (Weiskrantz, 1994, 2009). Similarly, when forced to reach for
objects in the blind area, “cortically blind participants … will nonetheless preadjust
their hands appropriately to size, shape, orientation and 3-D location of that object
in the blind field” (Marcel, 1986, p. 41). Yet they fail to show voluntary behavior,
such as reaching for a glass of water in the blind region, even when they are thirsty.
Some visual processing seems to occur even when participants have no conscious
awareness of visual sensations.
An interesting example of blindsight can be found in a case study of a patient
called D. B. (Weiskrantz, 2009). The patient was blind on the left side of his visual
field as an unfortunate result of an operation. That is, each eye had a blind spot on
the left side of its visual field. Consistent with this damage, D. B. reported no awareness
of any objects placed on his left side or of any events that took place on this side. But
despite his unawareness of vision on this side, there was evidence of vision. The investigator would present objects to the left side of the visual field and then present D. B.
with a forced-choice test in which the patient had to indicate which of two objects had
been presented to this side. D. B. performed at levels that were significantly better than
chance. In other words, he “saw” despite his unawareness of seeing.
Another study paired presentations of a visual stimulus with electric shocks (Hamm
et al., 2003). After multiple pairings, the patient began to experience fear when the
visual stimulus was presented, even though he could not explain why he was afraid.
Thus, the patient was processing visual information, although he could not see.
One explanation for blindsight is the following: The information from the retina
is forwarded to the visual cortex which is damaged in cortically blind people. It
seems, however, that a part of the visual information bypasses the visual cortex and
is sent to other locations in the cortex. The information from these locations is unconsciously accessible, although it seems to be conscious only when it is processed in
the visual cortex (Weiskrantz, 2007).
The preceding examples show that at least some cognitive functions can occur
outside of conscious awareness. We appear able to sense, perceive, and even respond
to many stimuli that never enter our conscious awareness (Marcel, 1983a). Just what
kinds of processes do or do not require conscious awareness?
CONCEPT CHECK
1. Why is habituation important?
2. How do we become habituated to stimuli?
3. How do mental processes become automated?
4. What is priming and how can it be studied?
5. What symptoms do patients have who exhibit blindsight?
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CHAPTER 4 • Attention and Consciousness
Key Themes
The study of attention and consciousness highlights several key themes in cognitive
psychology.
Structures versus processes. The brain contains various structures and systems
of structures, such as the reticular activating system, that generate the processes that
contribute to attention. Sometimes, the relationship between structure and process is
not entirely clear, and it is the job of cognitive psychologists to better understand it.
For example, blindsight is a phenomenon in which a process occurs—sight—in the
absence of the structures in the brain that would seem to be necessary for the sight
to take place.
Validity of causal inferences versus ecological validity. Should research on
vigilance be conducted in a laboratory to achieve careful experimental control?
Or should the research of high-stakes vigilance situations be studied ecologically?
For example, a study in which military officers are examining radar screens for possible attacks against the country must have a high degree of ecological validity to
ensure that the results apply to the actual situation in which the military officers
find themselves. The stakes are too high to allow slippage. Yet, when vigilance in
the actual-life situation is studied, one cannot and would not want to make attacks
against the country happen. Therefore, it is necessary to use simulations that are as
realistic as possible. In this way, the ecological validity of conclusions drawn can
be ensured.
Biological versus behavioral methods. Blindsight is a case of a curious and as
yet poorly understood link. The biology does not appear to be there to generate
the behavior. Another interesting example is attention deficit hyperactivity disorder. Physicians now have available a number of drugs that treat ADHD. These
treatments enable children as well as adults to focus better on tasks that they
need to get done. But the mechanisms by which the drugs work are still poorly
understood. Indeed, somewhat paradoxically, most of the drugs used to treat
ADHD are stimulants, which, when given to children with ADHD, appear to
calm them down.
Summary
1. Can we actively process information even if
we are not aware of doing so? If so, what do
we do, and how do we do it? Whereas attention embraces all the information that an individual is manipulating (a portion of the
information available from memory, sensation, and other cognitive processes), consciousness comprises only the narrower range
of information that the individual is aware of
manipulating. Attention allows us to use our
limited active cognitive resources (e.g., because of the limits of working memory) judiciously, to respond quickly and accurately to
interesting stimuli, and to remember salient
information.
Conscious awareness allows us to monitor
our interactions with the environment, to link
our past and present experiences and thereby
sense a continuous thread of experience, and
to control and plan for future actions.
We actively can process information at the
preconscious level without being aware of doing
so. For example, researchers have studied the
phenomenon of priming, in which a given stimulus increases the likelihood that a subsequent
related (or identical) stimulus will be readily
Summary
processed (e.g., retrieval from long-term memory). In contrast, in the tip-of-the-tongue phenomenon, another example of preconscious
processing, retrieval of desired information
from memory does not occur, despite an ability
to retrieve related information.
Cognitive psychologists also observe distinctions in conscious versus preconscious attention
by distinguishing between controlled and automatic processing in task performance. Controlled processes are relatively slow, sequential
in nature, intentional (requiring effort), and
under conscious control. Automatic processes
are relatively fast, parallel in nature, and for
the most part outside of conscious awareness.
Actually, a continuum of processing appears
to exist, from fully automatic to fully controlled
processes. Two automatic processes that support
our attentional system are habituation and dishabituation, which affect our responses to
familiar versus novel stimuli.
2. What are some of the functions of attention?
One main function involved in attention is
identifying important objects and events in
the environment. Researchers use measures
from signal-detection theory to determine an
observer’s sensitivity to targets in various tasks.
For example, vigilance refers to a person’s ability to attend to a field of stimulation over a
prolonged period, usually with the stimulus to
be detected occurring only infrequently.
Whereas vigilance involves passively waiting
for an event to occur, search involves actively
seeking out a stimulus.
People use selective attention to track one
message and simultaneously to ignore others. Auditory selective attention (such as in the cocktail
party problem) may be observed by asking participants to shadow information presented dichotically. Visual selective attention may be observed
in tasks involving the Stroop effect. Attentional
processes also are involved during divided attention, when people attempt to handle more than
one task at once; generally, the simultaneous performance of more than one automatized task is
easier to handle than the simultaneous performance of more than one controlled task. However, with practice, individuals appear to be
capable of handling more than one controlled
183
task at a time, even engaging in tasks requiring
comprehension and decision making.
3. What are some theories cognitive psychologists have developed to explain attentional
processes? Some theories of attention involve
an attentional filter or bottleneck, according to
which information is selectively blocked out or
attenuated as it passes from one level of processing to the next. Of the bottleneck theories,
some suggest that the signal-blocking or signalattenuating mechanism occurs just after sensation and prior to any perceptual processing;
others propose a later mechanism, after at least
some perceptual processing has occurred.
Attentional-resource theories offer an alternative way of explaining attention; according to
these theories, people have a fixed amount of
attentional resources (perhaps modulated by sensory modalities) that they allocate according to
the perceived task requirements. Resource
theories and bottleneck theories actually may
be complementary. In addition to these general
theories of attention, some task-specific theories
(e.g., feature-integration theory, guided-search
theory, and similarity theory) have attempted
to explain search phenomena in particular.
4. What have cognitive psychologists learned
about attention by studying the human brain?
Early neuropsychological research led to the
discovery of feature detectors, and subsequent
work has explored other aspects of feature detection and integration processes that may be
involved in visual search. In addition, extensive
research on attentional processes in the brain
seems to suggest that the attentional system primarily involves two regions of the cortex, as
well as the thalamus and some other subcortical
structures; the attentional system also governs
various specific processes that occur in many
areas of the brain, particularly in the cerebral
cortex. Attentional processes may be a result
of heightened activation in some areas of the
brain, of inhibited activity in other areas of the
brain, or perhaps of some combination of activation and inhibition. Studies of responsivity to
particular stimuli show that even when an individual is focused on a primary task and is not
consciously aware of processing other stimuli,
the brain of the individual automatically
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CHAPTER 4 • Attention and Consciousness
responds to infrequent, deviant stimuli (e.g., an
odd tone). By using various approaches to the
study of the brain (e.g., PET, ERP, lesion studies,
and psychopharmacological studies), researchers
are gaining insight into diverse aspects of the
brain and also are able to use converging operations to begin to explain some of the phenomena they observe.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe some of the evidence regarding the
phenomena of priming and preconscious
perception.
2. Why are habituation and dishabituation of
particular interest to cognitive psychologists?
3. Compare and contrast the theories of visual search
described in this chapter. Choose one of the
theories of attention and explain how the evidence
from signal detection, selective attention, or divided attention supports or challenges the theory.
4. Design one task likely to activate the posterior
attentional system and another task likely to
activate the anterior attentional system.
5. Design an experiment for studying divided
attention.
6. How could advertisers use some of the principles
of visual search or selective attention to increase
the likelihood that people will notice their
messages?
7. Describe some practical ways in which you can
use forcing functions and other strategies for
lessening the likelihood that automatic processes will have negative consequences for you
in some of the situations you face.
Key Terms
arousal, p. 169
attention, p. 137
automatic processes, p. 170
automatization, p. 170
blindsight, p. 181
change blindness, p. 165
cocktail party problem, p. 148
conjunction search, p. 144
consciousness, p. 138
controlled processes, p. 170
dichotic presentation, p. 149
dishabituation, p. 167
distracters, p. 143
divided attention, p. 138
executive attention, p. 161
feature-integration theory, p. 145
feature search, p. 144
habituation, p. 167
priming, p. 178
search, p. 143
selective attention, p. 138
sensory adaptation, p. 168
signal, p. 140
signal detection, p. 138
signal-detection theory (SDT),
p. 140
Stroop effect, p. 174
tip-of-the-tongue phenomenon,
p. 179
vigilance, p. 142
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Prototypes
Absolute Identification
Implicit Learning
5
C
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A
P
T
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R
Memory: Models and Research Methods
CHAPTER OUTLINE
Tasks Used for Measuring Memory
Recall versus Recognition Tasks
Implicit versus Explicit Memory Tasks
Intelligence and the Importance of Culture
in Testing
Models of Memory
The Traditional Model of Memory
Sensory Store
Short-Term Store
Long-Term Store
The Levels-of-Processing Model
An Integrative Model: Working Memory
The Components of Working Memory
Neuroscience and Working Memory
Measuring Working Memory
Intelligence and Working Memory
Multiple Memory Systems
A Connectionist Perspective
Exceptional Memory and Neuropsychology
Outstanding Memory: Mnemonists
Deficient Memory
Amnesia
Alzheimer’s Disease
How Are Memories Stored?
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
185
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CHAPTER 5 • Memory: Models and Research Methods
Here are some of the questions we will explore in this chapter:
1. What are some of the tasks used for studying memory, and what do various tasks indicate about the
structure of memory?
2. What has been the prevailing traditional model for the structure of memory?
3. What are some of the main alternative models for the structure of memory?
4. What have psychologists learned about the structure of memory by studying exceptional memory
and the physiology of the brain?
n BELIEVE IT OR NOT
MEMORY PROBLEMS? HOW
ABOUT
FLYING LESS?
Travel across time zones can actually get you more than
just an annoying jet lag. Researchers have found that people who are subjected to jet lag frequently with less than
two weeks of recovering time perform worse on spatial
memory tests than people who have more time to recover
(Cho, 2001). Twenty flight attendants who serve on
flights across more than seven time zones at a regular
basis had MRI analyses to measure the size of their brain.
It turned out that those flight attendants who had only
5 days to recover from jet lag, as opposed to 14 days,
had a smaller temporal lobe, which is important to memory
functions, and performed worse on the spatial memory
tests. But why would the temporal lobe be smaller?
Cho presumes that this is the result of elevated stress
hormones: Flight attendants had significantly higher
salivary cortisol levels after repeated long-distance flights
than after short-distance flights, and cortisol is known to
cause harm to the temporal lobe. You need not worry, however, unless you travel repeatedly across many time zones
with few days to recover. People who may be affected,
however, are shift workers like doctors or nurses, because
their day and night rhythms are frequently disrupted.
In this chapter, we will explore how our memory
works and what factors improve or impair our memory
performance.
Here are some questions. Try and see if you can answer them:
Who is the president of the United States?
What is today’s date?
What did you have for breakfast?
What does your best friend look like, and what does your friend’s voice sound
like?
• What were some of your experiences when you first started college?
• How do you tie your shoelaces?
•
•
•
•
Those questions were pretty easy, right? Although retrieving the answers to these
questions seemed easy, it is actually quite amazing that we can remember so many
different facts and procedures without problems. In this chapter, we will see how we
store information and retrieve it from memory.
As you age, your memory changes. As the author’s grandmother got older, she
gradually experienced a change in her memory. Memories from the grandmother’s
childhood and other details from her early and middle life were as vividly present as
they had always been (your experiences when you started college), but she had more
and more problems remembering anything that had happened in the recent past
(what she had for breakfast earlier in the day). She would ask her grandchildren
several times during a visit how they were doing and where they were currently
Tasks Used for Measuring Memory
187
working, but she was quick to recall events that had happened to her when she was a
middle-aged adult.
Maybe you have seen symptoms like these in one of your older relatives? And
what is memory exactly, anyway?
Memory is the means by which we retain and draw on our past experiences to use
that information in the present (Tulving, 2000b; Tulving & Craik, 2000). As a
process, memory refers to the dynamic mechanisms associated with storing, retaining,
and retrieving information about past experience (Bjorklund, Schneider, & Hernández
Blasi, 2003; Crowder, 1976). Specifically, cognitive psychologists have identified three
common operations of memory: encoding, storage, and retrieval (Baddeley, 2002;
Brebion, 2007; Brown & Craik, 2000). Each operation represents a stage in memory
processing.
• In encoding, you transform sensory data into a form of mental representation.
• In storage, you keep encoded information in memory.
• In retrieval, you pull out or use information stored in memory. These memory
processes are discussed at length in Chapter 6.
This chapter introduces some of the tasks that researchers use for studying memory.
Then, we examine several models of how memory might work. First, we discuss the
traditional model of memory. This model includes the sensory, short-term, and longterm storage systems. Although this model still influences current thinking about
memory, we consider some interesting alternative perspectives and models of memory
before moving on to discuss exceptional memory and insights provided by
neuropsychology.
Tasks Used for Measuring Memory
In studying memory, researchers have devised various tasks that require participants
to remember arbitrary information (e.g., numerals or letter strings) in different ways.
Because this chapter includes many references to these tasks, we begin this section
with a discussion of these tasks so that you will know how memory is studied. The
tasks described fall into two major categories—recall versus recognition memory and
implicit versus explicit memory.
Recall versus Recognition Tasks
In recall, you produce a fact, a word, or other item from memory. Fill-in-the-blank
and most essay tests require that you recall items from memory. For example, suppose
you want to measure people’s memory for late-night comedians. You could ask people to name a TV comedian. In recognition, you select or otherwise identify an item
as being one that you have been exposed to previously. (See also Table 5.1 for examples and explanations of each type of task.) For example, you could ask people
which of the following is a late-night comic: Jennifer Lopez, Jay Leno, Guy Ritchie,
Cameron Diaz. Multiple-choice and true-false tests involve some degree of
recognition.
Three main types of recall tasks are used in experiments (Lockhart, 2000): serial
recall, free recall, and cued recall. In serial recall, you recall items in the exact order
in which they were presented. For example, you could ask people to remember
the following list of comedians in order: Stephen Colbert, Jon Stewart, David
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CHAPTER 5 • Memory: Models and Research Methods
Table 5.1
Types of Tasks Used for Measuring Memory
Some memory tasks involve recall or recognition of explicit memory for declarative knowledge. Other tasks involve
implicit memory and memory for procedural knowledge.
Memory Tasks
Description of What
the Tasks Require
Example
Explicit-memory tasks
You must consciously recall
particular information.
Who wrote Hamlet?
Declarativeknowledge tasks
You must recall facts.
What is your first name?
Recall tasks
You must produce a fact, a
word, or other item from
memory.
Fill-in-the-blank tests require that you recall items from
memory. For example, “The term for persons who suffer
severe memory impairment is _______.”
Serial-recall task
You must repeat the items in a
list in the exact order in which
you heard or read them.
If you were shown the digits 2-8-7-1-6-4, you would be
expected to repeat “2-8-7-1-6-4,” in exactly that order.
Free-recall task
You must repeat the items in a
list in any order in which you
can recall them.
If you were presented with the word list “dog, pencil, time,
hair, monkey, restaurant,” you would receive full credit if you
repeated “monkey, restaurant, dog, pencil, time, hair.”
Cued-recall task
You must memorize a list of
paired items; then when you
are given one item in the pair,
you must recall the mate for
that item.
Suppose that you were given the following list of pairs:
“time-city, mist-home, switch-paper, credit-day, fist-cloud,
number-branch.” Later, when you were given the stimulus
“switch,” you would be expected to say “paper,” and
so on.
Recognition tasks
You must select or otherwise
identify an item as being one
that you learned previously.
Multiple-choice and true-false tests involve recognition. For
example, “The term for people with outstanding memory
ability is (1) amnesics, (2) semanticists, (3) mnemonists, or
(4) retrograders.”
Implicit-memory tasks
You must draw on information
in memory without consciously
realizing that you are doing so.
Word-completion tasks tap implicit memory. You would be
presented with a word fragment, such as the first three letters
of a word; then you would be asked to complete the word
fragment with the first word that comes to mind. For example, suppose that you were asked to supply the missing three
letters to fill in these blanks and form a word: _e_or_.
Because you had recently seen the word memory, you
would be more likely to provide the three letters m-m-y for the
blanks than would someone who had not recently been
exposed to the word. (You have been “primed”; more on
priming later in this chapter.)
Tasks involving procedural knowledge
You must remember learned
skills and automatic behaviors,
rather than facts.
If you were asked to demonstrate a “knowing-how” skill,
you might be given experience in solving puzzles or in
reading mirror writing, and then you would be asked to
show what you remember of how to use those skills. Or you
might be asked to master or to show what you already remember about particular motor skills (e.g., riding a bicycle
or ice skating).
Tasks Used for Measuring Memory
189
Letterman, Conan O’Brien, Jay Leno—and ask them then to repeat the list back in
that order.
The second kind of task is free recall, in which you recall items in any order you
choose (Golomb et al., 2008). In this case, you would ask people to remember the
list of comedians above, in any order.
The third kind of task is cued recall, in which you are first shown items in pairs,
but during recall you are cued with only one member of each pair and are asked to
recall each mate. Cued recall is also called “paired-associates recall” (Lockhart,
2000). For example, you could ask people to learn the following pairings: Colbert–
apple, Stewart–grape, Letterman–lemon, O’Brien–peach, Leno–orange, and then ask
them to produce the pairing for Stewart (grape).
Psychologists also can measure relearning, which is the number of trials it takes
to learn once again items that were learned in the past. Relearning has also been
referred to as savings and can be observed in adults, children, and animals (Bauer,
2005; Sasaki, 2008). The relearning effect was also observed in fetal rats, whose limb
movements were restrained by yokes and who were given kinesthetic feedback to
influence their motor performance. These rats demonstrated shorter learning times
for motor movements they had previously learned (Robinson, 2005). This effect is
clearly extensively generalizable to many situations and participants. For example,
suppose you studied Spanish in high school and then did not study it again in college. You now need it to succeed on your job in communicating with customers. If
you relearn Spanish, you will experience a savings in time relative to what you experienced the first time you learned it.
Recognition memory is usually much better than recall (although there are some
exceptions, which are discussed in Chapter 6). You may have experienced the superiority of recognition memory when you answered an exam question requiring you to
remember a fact. You were not able to produce all the facts that were asked for, but
when you discussed that particular question with a fellow student after the exam and
he pointed out the correct answer, you immediately recognized it as correct and were
annoyed with yourself for not coming up with the answer while taking the test.
A study by Standing and colleagues (1970) demonstrated that participants could
recognize close to 2,000 pictures in a recognition-memory task. It is difficult to imagine anyone recalling 2,000 items of any kind they were just asked to memorize. As
you will see later in the section on exceptional memory, even with extensive training, the best measured recall performance is typically around 80 items.
Informing participants of the type of future test they will take can influence the
amount of learning that occurs. Specifically, anticipation of recall tasks generally elicits deeper levels of information processing than anticipation of recognition tasks.
For example, if you are going to have a French vocabulary test, you may study differently (and more intensively) if you need to recall English meanings of French words
than if you merely have to say whether a set of English definitions of French words
are correct or incorrect (recognition).
Some psychologists refer to recognition-memory tasks as tapping receptive knowledge. Receptive means “responsive to a stimulus.” In a recognition-memory task, you
respond to stimuli presented to you and decide whether you have seen them before
or not. Recall-memory tasks, in which you have to produce an answer, require expressive knowledge. Differences between receptive and expressive knowledge also
are observed in areas other than that of simple memory tasks (e.g., language, intelligence, and cognitive development).
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CHAPTER 5 • Memory: Models and Research Methods
© Katherine Welles 2010/Shutterstock.com
Implicit versus Explicit
Memory Tasks
Memory theorists distinguish between
explicit memory and implicit memory
(Mulligan, 2003). Each of the tasks previously discussed involves explicit
memory, in which participants engage
in conscious recollection. For example,
they might recall or recognize words,
facts, or pictures from a particular prior
set of items.
A related phenomenon is implicit
memory, in which we use information
from memory but are not consciously
aware that we are doing so (Berry, 2008;
McBride, 2007). You can read the word
in the photo on the left without problems
although a letter is missing.
Every day you engage in many tasks
that
involve your unconscious recollecImplicit memory helps us to complete incomplete words we encounter without
tion
of information. Even as you read
our even being consciously aware of it.
this book, you unconsciously are remembering various things—the meanings of particular words, some of the
cognitive-psychological concepts you read about in earlier chapters, and even how
to read. These recollections are aided by implicit memory. There are differences in
explicit memory over the life span; however, implicit memory does not show the same
changes. Specifically, infants and older adults often tend to have relatively poor
explicit memory but implicit memory that is comparable to that of young adults
(Carver & Bauer, 2001; Murphy, McKone, & Slee, 2003). In certain patient groups,
you also see deficiencies in explicit memory with spared implicit memory; these groups
will be discussed later in the chapter.
In the following section, we will examine two tasks that involve implicit memory—
priming tasks and tasks involving procedural knowledge. We will then have a look at the
process-dissociation model, which postulates that only one task is needed to measure both
implicit and explicit memory.
In the laboratory, implicit memory is sometimes examined by having people
perform word-completion tasks that are based on the priming effect. In a wordcompletion task, participants receive a word fragment, such as the first three letters
of a word. They then complete it with the first word that comes to mind. For example, suppose that you are asked to fill in the blanks with the five missing letters to
form a word: imp_ _ _ _ _. Because you recently have seen the word implicit, you
would be more likely to provide the five letters “l-i-c-i-t” for the blanks than would
someone who had not recently been exposed to the word. You have been primed.
Priming is the facilitation of your ability to utilize missing information. In general,
participants perform better when they have seen the word on a recently presented
list, although they have not been explicitly instructed to remember words from
that list (Tulving, 2000a). Priming even works in situations where you are not aware
Tasks Used for Measuring Memory
191
that you have seen the word before—that is, if the word was presented for a fraction
of a second or in some other degraded form.
Procedural memory, or memory for processes, can be tested in implicit-memory
tasks as well. Examples of procedural memory include the procedures involved in
riding a bike or driving a car. Consider when you drive to the mall: You probably
put the car into gear, use your blinkers, and stay in your lane without actively thinking about the task. Nor do you consciously need to remember what you should do at
a red light. Many of the activities that we do every day fall under the purview of
procedural memory; these can range from brushing your teeth to writing.
In the laboratory, procedural memory is sometimes examined with the rotary
pursuit task (Gonzalez, 2008; see Figure 5.1). The rotary pursuit task requires participants to maintain contact between an L-shaped stylus and a small rotating disk
(Costello, 1967). The disk is generally the size of a nickel, less than an inch in
diameter. This disk is placed on a quickly rotating platform. The participant must
track the small disk with the wand as it quickly spins around on a platform. After
learning with a specific disk and speed of rotation, participants are asked to complete
the task again, either with the same disk and the same speed or with a new disk or
speed. Verdolini-Marston and Balota (1994) noted that when a new disk or speed is
used, participants do relatively poorly. But with the same disk and speed, participants do as well as they had after learning the task, even if they do not remember
previously completing the task.
Another task used to examine procedural memory is mirror tracing. In the
mirror-tracing task, a plate with the outline of a shape drawn on it is put behind a
barrier where it cannot be seen. Beyond the barrier in the participant’s line of sight
is a mirror. When the participant reaches around the barrier, his or her hand and
the plate with the shape are within view. Participants then take a stylus and trace
the outline of the shape drawn on the plate. When first learning this task, participants have difficulty staying on the shape. Typically, there are many points at which
Figure 5.1 The Rotary Pursuit Task.
In the rotary pursuit task, subjects use an L-shaped stylus to track a small, rotating disk on a
spinning platform.
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the stylus leaves the outline. Moreover, it takes a relatively long time to trace the
entire shape. With practice, however, participants become quite efficient and accurate with this task. Participants’ retention of this skill gives us a way to study procedural memory (Rodrigue, Kennedy, & Raz, 2005).
The mirror-tracing task is also used to study the impact of sleep on procedural
memory. Patients suffering from schizophrenia often have memory deficits as well as
sleep problems. A study by Göder and colleagues (2008) found that when those patients received a medication that increased the duration of their slow-wave sleep,
their procedural memory performance increased as well.
The methods for measuring both implicit and explicit memory described here
and in Table 5.1 assume that implicit and explicit memory are separate and can be
measured by different tasks. Some researchers have challenged this assumption. They
assume that implicit and explicit memory both play a role in every response, even if
the task at hand is intended to measure only one type of memory. Thus, cognitive
psychologists have developed models that assume that both implicit and explicit
memory influence almost all responses. One of the first and most widely recognized
models in this area is the process-dissociation model (Daniels et al., 2006; Jacoby,
1991). The model assumes that implicit and explicit memory both have a role in virtually every response. Thus, only one task is needed to measure both these processes.
Although there are disagreements about exactly what the different measures
show, there is agreement that both implicit and explicit memory are important in
our everyday lives. Kaufman has also argued that implicit memory, like explicit
memory, is an important part of human intelligence (Kaufman, 2010).
Intelligence and the Importance of Culture in Testing
In many cultures of the world, quickness is not at a premium. In these cultures, people may believe that more intelligent people do not rush into things. Even in our
own culture, no one will view you as brilliant if you rush things that should not be
rushed. For example, it generally is not smart to decide on a marital partner, a job,
or a place to live in the 20 to 30 seconds you normally might have to solve an
intelligence-test problem. Thus, there exist no perfectly culture-fair tests of intelligence, at least at present. How then should we consider context when assessing
and understanding intelligence?
Several researchers have suggested that providing culture-relevant tests is possible (e.g., Baltes, Dittmann-Kohli, & Dixon, 1984; Jenkins, 1979; Keating, 1984).
Culture-relevant tests measure skills and knowledge that relate to the cultural
experiences of the test-takers. Baltes and his colleagues have designed tests measuring skill in dealing with the pragmatic aspects of everyday life. Designing culturerelevant tests requires creativity and effort, but it is probably not impossible. For
example, one study investigated memory abilities—one aspect of intelligence as our
culture defines it—in our culture versus the Moroccan culture (Wagner, 1978). The
study found that the level of recall depended on the content that was being remembered. Culture-relevant content was remembered more effectively than non-relevant
content. For example, when compared with Westerners, Moroccan rug merchants
were better able to recall complex visual patterns on black-and-white photos of Oriental rugs. Sometimes tests are not designed to minimize the effects of cultural differences. In such cases, the key to culture-specific differences in memory may be the
knowledge and use of metamemory strategies, rather than actual structural differences in memory (e.g., memory span and rates of forgetting) (Wagner, 1978).
Models of Memory
193
Rural Kenyan school children have substantial knowledge about natural herbal
medicines they believe fight illnesses. Western children, of course, would not be able
to identify any of these medicines (Sternberg et al., 2001; Sternberg & Grigorenko,
1997). In short, making a test culturally relevant appears to involve much more than
just removing specific linguistic barriers to understanding.
CONCEPT CHECK
1. What is the difference between a recall task and a recognition task?
2. What is explicit memory?
3. What is implicit memory?
4. Why does it make sense to consider culture when doing research on memory in
different countries?
Models of Memory
Researchers have developed several models to describe how our memory works. The
traditional “three-store model” is not the only way to conceptualize memory. The
following sections first present what we know about memory in terms of the threestore model. Then we examine the levels-of-processing model, and also consider an
integrative model of working memory. Subsequently, we will explore some more
conceptualizations of memory systems and lastly get to know a connectionist model.
Let’s begin with the traditional model of memory.
The Traditional Model of Memory
There are several major models of memory (McAfoose & Baune, 2009; Murdock,
2003). In the mid-1960s, based on the data available at the time, researchers proposed a model of memory distinguishing two structures of memory first proposed by
William James (1890, 1970): primary memory, which holds temporary information
currently in use, and secondary memory, which holds information permanently or
at least for a very long time (Waugh & Norman, 1965). Three years later, Richard
Atkinson and Richard Shiffrin (1968) proposed an alternative model that conceptualized memory in terms of three memory stores:
•
•
•
a sensory store, capable of storing relatively limited amounts of information
for very brief periods;
a short-term store, capable of storing information for somewhat longer periods
but of relatively limited capacity as well; and
a long-term store, of very large capacity, capable of storing information for very
long periods, perhaps even indefinitely (Richardson-Klavehn & Bjork, 2003).
The model differentiates among structures for holding information, termed stores,
and the information stored in the structures, termed memory. Today, cognitive psychologists commonly describe the three stores as sensory memory, short-term memory, and
long-term memory. Also, Atkinson and Shiffrin were not suggesting that the three stores
are distinct physiological structures. Rather, the stores are hypothetical constructs—
concepts that are not themselves directly measurable or observable but that serve as
mental models for understanding how a psychological phenomenon works. Figure 5.2
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CHAPTER 5 • Memory: Models and Research Methods
Sensory registers
Visual
Environmental
input
Short-term memory
(STM)
Temporary
working memory
Auditory
Control processes:
Rehearsal
Long-term memory
(LTM)
Permanent
memory store
Haptic
Retrieval strategies
Response output
Figure 5.2 Atkinson and Shiffrin’s Memory Model.
Richard Atkinson and Richard Shiffrin proposed a theoretical model for the flow of information through the human
information processor.
Source: Illustration by Allen Beechel, adapted from “The Control of Short-Term Memory,” by Richard C. Atkinson and Richard M.
Shiffrin. Copyright © 1971 by Scientific American, Inc. All rights reserved. Reprinted with permission.
shows a simple information-processing model of these stores (Atkinson & Shiffrin, 1971).
This Atkinson-Shiffrin model emphasizes the passive storage areas in which memories are
stored; but it also alludes to some control processes that govern the transfer of information
from one store to another. In the following sections, we take a closer look at the sensory
store, the short-term store, and the long-term store.
Sensory Store
The sensory store is the initial repository of much information that eventually enters the
short- and long-term stores. Strong (although not undisputed; see Haber, 1983) evidence argues in favor of the existence of an iconic store. The iconic store is a discrete
visual sensory register that holds information for very short periods. Its name derives
from the fact that information is stored in the form of icons. These in turn are visual
images that represent something. Icons usually resemble whatever is being represented.
If you have ever “written” your name with a lighted sparkler (or stick of incense)
against a dark background, you have experienced the persistence of a visual memory.
You briefly “see” your name, although the sparkler leaves no physical trace. This visual
persistence is an example of the type of information held in the iconic store.
Sperling’s Discovery The initial discovery regarding the existence of the iconic
store came from a doctoral dissertation by a graduate student at Harvard University
named George Sperling (1960). He addressed the question of how much information
we can encode in a single, brief glance at a set of stimuli. Sperling flashed an array of
letters and numbers on a screen for a mere 50 milliseconds (thousandths of a second). Participants were asked to report the identity and location of as many of the
symbols as they could recall. Sperling could be sure that participants got only one
glance because previous research had shown that 0.050 seconds is long enough for
only a single glance at the presented stimulus.
Sperling found that when participants were asked to report on what they saw,
they remembered only about four symbols. The finding confirmed an earlier one
Models of Memory
195
made by Brigden in 1933. The number of symbols recalled was pretty much the same,
without regard to how many symbols had been in the visual display. Some of Sperling’s participants mentioned that they had seen all the stimuli clearly. But while reporting what they saw, they forgot the other stimuli. Sperling then conceived an
ingenious idea for how to measure what the participants saw. The procedure used by
Brigden and in the first set of studies by Sperling is a whole-report procedure. In this
procedure, participants report every symbol they have seen. Sperling then introduced a
partial-report procedure. Here, participants need to report only part of what they see.
Sperling found a way to obtain a sample of his participants’ knowledge. He then
extrapolated from this sample to estimate their total knowledge. His logic was similar
to that of school examinations, which also are used as samples of an individual’s total knowledge of course material. Sperling presented symbols in three rows of four
symbols each. Figure 5.3 shows a display similar to one that Sperling’s participants
might have seen. Sperling informed participants that they would have to recall only
a single row of the display. The row to be recalled was signaled by a tone of high,
medium, or low pitch. The pitches corresponded to the need to recall the top, middle,
or bottom row, respectively.
To estimate the duration of iconic memory, Sperling manipulated the interval between the display and the tone. The range of the interval was from 0.10 seconds before
the onset of the display to 1.0 second after the offset of the display. The partial-report
procedure dramatically changed how much participants could recall. Sperling then
multiplied the number of symbols recalled with this procedure by three. The reason
was that participants had to recall only one third of the information presented but did
not know beforehand which of the three lines they would be asked to report.
Using this partial-report procedure, Sperling found that participants had available
roughly 9 of the 12 symbols if they were cued immediately before or immediately after
the appearance of the display. However, when they were cued one second later, their
recall was down to 4 or 5 of the 12 items. This level of recall was about the same as
that obtained through the whole-report procedure. These data suggest that the iconic
store can hold about 9 items. They also suggest that information in this store decays
very rapidly (Figure 5.4). Indeed, the advantage of the partial-report procedure is
H
B
S
T
A
H
M G
E
L
W C
Figure 5.3 Display from a Visual-Recall Task.
This symbolic display is similar to the one used for George Sperling’s visual-recall task.
Source: From Psychology, 2nd ed., by Margaret W. Matlin, Copyright © 1995 by Holt, Rinehart and Winston.
Reproduced by permission of the publisher.
CHAPTER 5 • Memory: Models and Research Methods
12
100
10
75
8
50
6
Percentage
Number of letters recalled
196
4
25
2
0
1.0
–.10 0 .15 .30
Delay of tone (seconds)
0
Figure 5.4 Results of Sperling’s Experiment.
The figure shows the average number of letters recalled (left axis; percentage equivalents indicated on right axis) by a subject, based on using the partial-report procedure, as a function
of the delay between the presentation of the letters and the tone signaling when to demonstrate recall. The bar at the lower-right corner indicates the average number of letters recalled
when subjects used the whole-report procedure. (After Sperling, 1960.)
reduced drastically by 0.3 seconds of delay. It essentially is obliterated by 1 second of
delay for onset of the tone.
Sperling’s results suggest that information fades rapidly from iconic storage. Why
are we subjectively unaware of such a fading phenomenon? First, we rarely are
subjected to stimuli such as the ones in his experiment. They appeared for only
50 milliseconds and then disappeared before participants needed to recall them.
Second and more important, however, we are unable to distinguish what we see in
iconic memory from what we actually see in the environment. What we see in iconic
memory is what we take to be in the environment. Participants in Sperling’s experiment generally reported that they could still see the display up to 150 milliseconds
after it actually had been terminated.
Elegant as it was, Sperling’s use of the partial-report procedure was imperfect. It
still suffered, at least to some small extent, from the problem inherent in the fullreport procedure: Participants had to report multiple symbols. They may have experienced fading of memory during the report. Indeed, a distinct possibility of output
interference exists. In this case, the production of output interferes with the phenomenon being studied. That is, verbally reporting multiple symbols may interfere
with reports of iconic memory.
Subsequent Refinement In subsequent work, participants were shown displays of
two rows of eight randomly chosen letters for a duration of 50 milliseconds (Averbach & Coriell, 1961). In this investigation, a small mark appeared just above one
of the positions where a letter had appeared (or was about to appear). Its appearance
Models of Memory
197
was at varying time intervals before or after presentation of the letters. In this research, then, participants needed to report only a single letter at a time. The procedure thus minimized output interference. These investigators found that when the
mark appeared immediately before or after the stimulus display, participants could
report accurately on about 75% of the trials. Thus, they seemed to be holding about
12 items (75% of 16) in sensory memory. Sperling’s estimate of the capacity of
iconic memory, therefore, may have been conservative. The evidence in this study
suggests that when output interference is greatly reduced, the estimates of the capacity of iconic memory may greatly increase. Iconic memory may comprise as many as
12 items.
A second experiment (Averbach & Coriell, 1961) revealed an additional important characteristic of iconic memory: It can be erased. The erasable nature of iconic
memory definitely makes our visual sensations more sensible. We would be in serious
trouble if everything we saw in our visual environment persisted for too long. For
example, if we are scanning the environment at a rapid pace, we need the visual
information to disappear quickly so that our memory does not get overloaded.
The investigators found that when a stimulus was presented after a target letter
in the same position that the target letter had occupied, it could erase the visual
icon (Averbach & Coriell, 1961). This interference is called backward visual masking. Backward visual masking is mental erasure of a stimulus caused by the placement
of one stimulus where another one had appeared previously. If the mask stimulus is
presented in the same location as a letter and within 100 milliseconds of the presentation of the letter, the mask is superimposed on the letter. For example, F followed
by L would be E. At longer intervals between the target and the mask, the mask
erases the original stimulus. For example, only the L would remain if F and then L
had been presented. At still longer intervals between the target and the mask, the
mask no longer interferes. This non-interference is presumably because the target information already has been transferred to more durable memory storage.
To summarize, visual information appears to enter our memory system through
an iconic store. This store holds visual information for very short periods. In the
normal course of events, this information may be transferred to another store. Or it
may be erased. Erasure occurs if other information is superimposed on it before there
is sufficient time for the transfer of the information to another memory store. Erasure
or movement into another store also occurs with auditory information that is in
echoic memory.
Short-Term Store
Most of us have little or no introspective access to our sensory memory stores. Nevertheless, we all have access to our short-term memory store. It holds memories for a
few seconds and occasionally up to a couple of minutes. For example, can you remember the name of the researcher who discovered the iconic store? What about
the names of the researchers who subsequently refined this work? If you can recall
those names, you used some memory-control processes for doing so. According to
the Atkinson-Shiffrin model, the short-term store does more than hold onto a few
items. It also has some control processes available that regulate the flow of information to and from the long-term store, where we may hold information for longer periods. Typically, material remains in the short-term store for about 30 seconds, unless
it is rehearsed to retain it. Information is stored acoustically (by the way it sounds)
rather than visually (by the way it looks).
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How many items of information can we hold in short-term memory at any one
time? In general, our immediate (short-term) memory capacity for a wide range of
items appears to be about seven items, plus or minus two (Miller, 1956). An item
can be something simple, such as a digit, or something more complex, such as a
word. If we chunk together a string of, say, 20 letters or numbers into 7 meaningful
items, we can remember them. We could not, however, remember 20 items and repeat them immediately. For example, most of us cannot hold in short-term memory
this string of 21 numbers: 101001000100001000100. However, if we chunk this string
of numbers into larger units, such as 10, 100, 1,000, 10,000, 1,000, and 100. We probably will be able to reproduce easily the 21 numerals as 6 items (Miller, 1956).
Other factors also influence the capacity for temporary storage in memory. For
example, the number of syllables we pronounce with each item affects the number of
items we can recall. When each item has a larger number of syllables, we can recall
fewer items (Hulme et al., 2006). In addition, any delay or interference can cause
our seven-item capacity to drop to about three items. In general, the capacity limit
may be closer to three to five than it is to seven (Cowan, 2001).
Most studies have used verbal stimuli to test the capacity of the short-term store,
but people can also hold visual information in short-term memory. For example,
they can hold information about shapes as well as their colors and orientations.
What is the capacity of the short-term store of visual information? Is it less, the
same, or perhaps greater?
A team of investigators set out to discover the capacity of the short-term store
for visual information (Luck & Vogel, 1997; Vogel, Woodman, & Luck, 2001).
They presented experimental participants with two visual displays. The displays
were presented in sequence. The stimuli were of three types: colored squares, black
lines at varying orientations, and colored lines at different orientations. Thus, the
third kind of stimulus combined the features of the first two. The kind of stimulus
was the same in each of the two displays. For example, if the first display contained
colored squares, so did the second. The two displays could be either the same or different from each other. If they were different, then it was by only one feature. The
participants needed to indicate whether the two displays were the same or different.
The investigators found that participants could hold roughly four items in memory,
which were within the estimates suggested by Cowan (2001). The results were the
same whether just individual features were varied (i.e., colored squares, black lines at
varying orientation) or pairs of features were varied (i.e., colored lines at different
orientations). Thus, storage seems to depend on numbers of objects rather than
numbers of features.
This work contained a possible confound (i.e., other responsible factors that
cannot be easily disentangled from the supposed causal factor). In the stimuli with
colored lines at different orientations, the added feature was at the same spatial location as the original one. That is, color and orientation were, with respect to the
same object, in the same place in the display. A further study thus was done to separate the effects of spatial location from number of objects (Lee & Chun, 2001). In
this research, stimuli comprising boxes and lines could be either at separate locations
or at overlapping locations. The overlapping locations thus separated the objects
from the fixed locations. The research would enable one to determine whether people can remember four objects, as suggested in the previous work, or four spatial locations. The results were the same as in the earlier research. Participants still could
remember four objects, regardless of spatial locations. Therefore, memory was for
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199
objects, not spatial locations. Further, using American Sign Language, researchers
have found that short-term memory can hold approximately four items for signed
letters. This finding is consistent with earlier work on visual-spatial short-term memory. The finding makes sense, given the visual nature of these items (Bavelier et al.,
2006; Wilson & Emmorey, 2006).
Long-Term Store
We constantly use short-term memory throughout our daily activities. When most of
us talk about memory, however, we usually are talking about long-term memory.
Here we keep memories that stay with us over long periods, perhaps indefinitely.
All of us rely heavily on our long-term memory. We hold in it information we
need to get us by in our day-to-day lives—people’s names, where we keep things,
how we schedule ourselves on different days, and so on.
How much information can we hold in long-term memory? How long does the
information last? The question of storage capacity can be disposed of quickly because
the answer is simple. We do not know. Nor do we know how we would find out.
We can design experiments to tax the limits of short-term memory, but we do not
know how to test the limits of long-term memory and thereby find out its capacity.
Some theorists have suggested that the capacity of long-term memory is infinite, at
least in practical terms (Bahrick, 2000; Brady, 2008). It turns out that the question
of how long information lasts in long-term memory is not easily answerable. At present, we have no proof even that there is an absolute outer limit to how long information can be stored.
What is stored in the brain? Wilder Penfield addressed this question while performing operations on the brains of conscious patients afflicted with epilepsy. He
used electrical stimulation of various parts of the cerebral cortex to locate the origins
of each patient’s problem. In fact, his work was instrumental in plotting the motor
and sensory areas of the cortex, described in Chapter 2.
During the course of such stimulation, Penfield (1955, 1969) found that patients
sometimes would appear to recall memories from their childhoods. These memories
may not have been called to mind for many, many years. (Note that the patients
could be stimulated to recall episodes such as events from their childhood, not facts
such as the names of U.S. presidents.) These data suggested to Penfield that longterm memories might be permanent.
Some researchers have disputed Penfield’s interpretations (e.g., Loftus & Loftus,
1980). For example, they have noted the small number of such reports in relation to
the hundreds of patients on whom Penfield operated. In addition, we cannot be certain that the patients actually were recalling these events. They may have been inventing them. Other researchers, using empirical techniques on older participants,
found contradictory evidence.
Some researchers tested participants’ memory for names and photographs of
their high-school classmates (Bahrick, Bahrick, & Wittlinger, 1975). Even after
25 years, there was little forgetting of some aspects of memory. Participants tended
to recognize names as belonging to classmates rather than to outsiders. Recognition
memory for matching names to graduation photos was quite high. As you might expect, recall of names showed a higher rate of forgetting.
The term permastore refers to the very long-term storage of information, such as
knowledge of a foreign language (Bahrick, 1984a, 1984b; Bahrick et al., 1993) and
of mathematics (Bahrick & Hall, 1991).
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CHAPTER 5 • Memory: Models and Research Methods
Schmidt and colleagues (2000) studied the permastore effect for names of
streets near one’s childhood homes. Indeed, the author just returned to his childhood
home of more than 40 years ago and perfectly remembered the names of the nearby
streets. These findings indicate that permastore can occur even for information that
you have passively learned. Some researchers have suggested that permastore is a
separate memory system. Others, such as Neisser (1999), have argued that one longterm memory system can account for both. There is to date no resolution of the issue.
In any case, research on the immense capacity of long-term memory has motivated researchers, instructors, and teachers to come up with new methods to help
students memorize what they learn. Students do have great memory capacity, and
ideally, they should leave school with both the ability to think critically and also a
good knowledge base about which to think. To this end, new and motivating techniques are constantly being developed and include on-line quizzes that students can
take to test their knowledge, or the use of clickers (remote control devices that allow
students to communicate with their teacher in front via a computer system) with
which students can answer multiple-choice questions during class and can give feedback to the teacher (Miller, 2009).
The Levels-of-Processing Model
A radical departure from the three-stores model of memory is the levelsof-processing framework, which postulates that memory does not comprise three
or even any specific number of separate stores, but rather varies along a continuous
dimension in terms of depth of encoding (Craik & Lockhart, 1972, 2008). In other
words, there are theoretically an infinite number of levels of processing (LOP) at
which items can be encoded through elaboration—or successively deeper understanding of material to be learned. There are no distinct boundaries between one
level and the next. The emphasis in this model is on processing as the key to storage. The level at which information is stored will depend, in large part, on how it is
encoded. Moreover, the deeper the level of processing, the higher, in general, is the
probability that an item may be retrieved (Craik & Brown, 2000).
A set of experiments seems to support the LOP view (Craik & Tulving, 1975).
Participants received a list of words. A question preceded each word. Questions were
varied to encourage item elaboration on three different levels of processing. In progressive order of depth, they were physical, phonological, and semantic. Samples of the
words and the questions are shown in Table 5.2. The results of the research were
clear: The deeper the level of processing encouraged by the question, the higher
the level of recall achieved. Similar results emerged independently in Russia (Zinchenko, 1962, 1981).
The levels-of-processing framework can also be applied to nonverbal stimuli.
Melinda Burgess and George Weaver (2003) showed participants photos of faces
and asked them questions about the persons of the photo to induce either deep
or shallow processing. Faces that were deeply processed were better recognized on
a subsequent test than those that were studied at a lower level of processing.
A level-of-processing (or depth-of-processing) benefit can be seen for a variety of
populations, including in people with schizophrenia. People suffering from schizophrenia often suffer from memory impairments because they do not process words
semantically. Deeper processing helps them improve their memory (Ragland et al.,
2003).
Models of Memory
Table 5.2
201
Levels-of-Processing Framework
Among the levels of processing proposed by Fergus Craik and Endel Tulving are the physical, phonological, and
semantic levels.
Level of Processing
Basis for Processing
Example
Physical
Visually apparent features of the letters
Word:
TABLE
Question: Is the word written in capital letters?
Phonological
Sound combinations associated with the
letters (e.g., rhyming)
Word:
CAT
Question: Does the word rhyme with “MAT”?
Semantic
Meaning of the word
Word:
DAFFODIL
Question: Is the word a type of plant?
An even more powerful inducement to recall has been termed the self-reference
effect (Rogers, Kuiper, & Kirker, 1977). In the self-reference effect, participants show
very high levels of recall when asked to relate words meaningfully to the participants
by determining whether the words describe them. Even the words that participants
assess as not describing themselves are recalled at high levels. This high recall is a
result of considering whether the words do or do not describe the participants. However, the highest levels of recall occur with words that people consider selfdescriptive. Similar self-reference effects have been found by many other researchers
(e.g., Bower & Gilligan, 1979; Reeder, McCormick, & Esselman, 1987). Objects can
be better remembered, for example, if they belong to the participant (Cunningham
et al., 2008). Some researchers suggest that the self-reference effect is distinctive, but
others suggest that it is explained easily in terms of the LOP framework or other ordinary memory processes (e.g., Mills, 1983). Specifically, each of us has a very elaborate self-schema. This self-schema is an organized system of internal cues regarding
our attributes, our personal experiences, and ourselves. Thus, we can richly and elaborately encode information related to ourselves much more so than information
about other topics (Bellezza, 1984, 1992).
Despite much supporting evidence, the LOP framework as a whole has its
critics. For one thing, some researchers suggest that the particular levels may involve
INVESTIGATING COGNITIVE PSYCHOLOGY
Levels of Processing
Ask some friends or family members to help you with a memory experiment. Give half of
them the instruction to count the number of letters in the words you are about to recite.
Give the other half the instruction to think of three words related to the words you are
about to recite. Recite the following words about 5 seconds apart: beauty, ocean, competitor, bad, decent, happy, brave, beverage, artistic, dejected. About 5 or 10 minutes
later, ask your friends to write down as many of the 10 words as they can remember. In
general, those who were asked to think of three related words to the words you read
will remember more than those who were asked to count the number of letters in the
words. This is a demonstration of levels of processing. Those friends who thought of
three related words processed the words more deeply than those who merely counted
up the number of letters in the words. Words that are processed more deeply are remembered better.
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a circular definition. On this view, the levels are defined as deeper because the information is retained better. But the information is viewed as being retained better
because the levels are deeper. In addition, some researchers noted some paradoxes in
retention. For example, under some circumstances, strategies that use rhymes have
produced better retention than those using just semantic rehearsal. That means, focusing on superficial sounds and not underlying meanings can result in better retention than focusing on repetition of underlying meanings. But now imagine two
conditions—one in which participants encode the information acoustically (based
on rhymes) and retrieve it based on acoustic cues as well; and one in which participants both encode and retrieve the information semantically. For example, participants are presented with a word and then have to determine whether that word
rhymes with another word (acoustic encoding). For semantic encoding, they have
to determine whether that word belongs to a given category or fits into a given
sentence. Performance is greater for semantic retrieval than for acoustic retrieval
(Fisher & Craik, 1977).
In light of these criticisms and some contrary findings, the LOP model has been
revised. The sequence of the levels of encoding may not be as important as was
thought before. Two other variables may be of more importance: the way people
process (elaborate) the encoding of an item (e.g., phonological or semantic), and
the way the item is retrieved later on. The better the match between the type of
elaboration of the encoding and the type of task required for retrieval, the better
the retrieval results (Morris, Bransford, & Franks, 1977).
Furthermore, there appear to be two kinds of strategies for elaborating the encoding. The first is within-item elaboration. It elaborates encoding of the particular item
(e.g., a word or other fact) in terms of its characteristics, including the various levels of
processing. The second kind of strategy is between-item elaboration. It elaborates encoding by relating each item’s features (again, at various levels) to the features of items
already in memory. Thus, suppose you wanted to be sure to remember something in
particular. You could elaborate it at various levels for each of the two strategies.
P R A C T I C A L A P P L I C A T I O N S OF C O GNI T I VE P S YC HO LO GY
ELABORATION STRATEGIES
Elaboration strategies have practical applications: In studying, you may wish to match the
way in which you encode the material to the way in which you will be expected to retrieve it in the future, because the better the match between the way you encode the material and the way you will need to retrieve it later, the better you are able to retrieve items
from memory. For example, if you are learning a new language and have a vocabulary
test coming up, you will concentrate on learning the meaning of the words. If you have to
write an essay, you will also need to concentrate on sentence structure and grammar.
Also, the more elaborately and diversely you encode material, the more readily you are
likely to recall it later in a variety of task settings. Just looking over material again and again
in the same way is less likely to be productive for learning the material than is finding more
than one way in which to learn it. If the context for retrieval will require you to have a deep
understanding of the information, you should find ways to encode the material at deep levels
of processing, such as by asking yourself meaningful questions about the material.
Are there any circumstances under which elaboration might be problematic?
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203
An Integrative Model: Working Memory
The working-memory model is probably the most widely used and accepted model today. Psychologists who use it view short-term and long-term memory from a different
perspective (e.g., Baddeley, 2007, 2009; Unsworth, 2009). Table 5.3 shows the contrasts between the Atkinson-Shiffrin model and an alternative perspective. Note the
semantic distinctions in how memory components are labeled, the differences in metaphorical representation, and the differences in emphasis for each view. The key feature
of the alternative view is the role of working memory. Working memory holds only the
most recently activated, or conscious, portion of long-term memory, and it moves these
activated elements into and out of brief, temporary memory storage (Dosher, 2003).
The Components of Working Memory
Alan Baddeley has suggested an integrative model of memory (see Figure 5.5;
Baddeley, 1990a, 1990b, 2007, 2009). It synthesizes the working-memory model
with the LOP framework. Essentially, he views the LOP framework as an extension
of, rather than as a replacement for, the working-memory model.
Baddeley originally suggested that working memory comprises five elements: the
visuospatial sketchpad, the phonological loop, the central executive, subsidiary
Table 5.3
Traditional versus Nontraditional Views of Memory
Since Richard Atkinson and Richard Shiffrin first proposed their three-store model of memory
(which may be considered a traditional view of memory), various other models have been
suggested.
Traditional Three-Store
View
Alternative View of Memory
Terminology: definition of memory stores
Working memory is
another name for shortterm memory, which is
distinct from long-term
memory.
Working memory (active memory) is that
part of long-term memory that comprises
all the knowledge of facts and procedures that recently has been activated in
memory, including the brief, fleeting
short-term memory and its contents.
Metaphor for
envisioning the
relationships
Short-term memory may
be envisioned as being
distinct from long-term
memory, perhaps either
alongside it or hierarchically linked to it.
Short-term memory, working memory,
and long-term memory may be envisioned as nested concentric spheres, in
which working memory contains only
the most recently activated portion of
long-term memory, and short-term memory contains only a very small, fleeting
portion of working memory.
Metaphor for the
movement of
information
Information moves directly from long-term
memory to short-term
memory and then
back—never in both
locations at once.
Information remains within long-term
memory; when activated, information
moves into long-term memory’s specialized working memory, which actively will
move information into and out of the shortterm memory store contained within it.
Emphasis
Distinction between
long- and short-term
memory.
Role of activation in moving information
into working memory and the role of
working memory in memory processes.
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CHAPTER 5 • Memory: Models and Research Methods
Central Executive
Phonological
Storage
Subvocal
Rehearsal
Phonological Loop
Episodic Buffer
Visuospatial Sketchpad
Verbal Information
Long-Term Memory
Visual Information
Figure 5.5 Working Memory.
The components of the working-memory model comprise the central executive, the phonological loop, the visuospatial
sketchpad, and the episodic buffer, as well as several “subsidiary slave systems” (not pictured).
“slave systems,” and the episodic buffer. The first element, the visuospatial sketchpad, briefly holds some visual images.
The phonological loop briefly holds inner speech for verbal comprehension and
for acoustic rehearsal. We use the phonological loop for a number of everyday tasks,
including sounding out new and difficult words and solving word problems. There
are two critical components of this loop. One is phonological storage, which holds
information in memory. The other is subvocal rehearsal, which is used to put the information into memory in the first place. The role of subvocal rehearsal can be seen
in the following example. Try to memorize the following list of words while repeating the number five to yourself continuously:
Tree, pencil, marshmallow, lamp, sunglasses, computer, chocolate, noise, clock,
snow, river, square, store.
Did you notice how hard it is to memorize these words? Try it again without
repeating the number five to yourself—it should be much easier now! So what
happens when you repeat the number five while memorizing words? In this case
subvocal rehearsal is inhibited and you would be unable to rehearse the new words.
When subvocal rehearsal is inhibited, the new information is not stored. This phenomenon is called articulatory suppression. Articulatory suppression is more pronounced when the information is presented visually versus aurally (e.g., by
hearing). The amount of information that can be manipulated within the phonological loop is limited. Thus, we can remember fewer long words compared with
short words (Baddeley, 2000b). Without this loop, acoustic information decays after
about 2 seconds.
The third element is a central executive, which both coordinates attentional
activities and governs responses. The central executive is critical to working memory
because it is the gating mechanism that decides what information to process further
and how to process this information. It decides what resources to allocate to memory
and related tasks, and how to allocate them. It is also involved in higher-order reasoning and comprehension and is central to human intelligence.
Models of Memory
205
The fourth element is a number of other “subsidiary slave systems” that perform
other cognitive or perceptual tasks (Baddeley, 1989, p. 36). The fifth component is
the episodic buffer. The episodic buffer is a limited-capacity system that is capable of
binding information from the visuospatial sketchpad and the phonological loop as well
as from long-term memory into a unitary episodic representation. This component integrates information from different parts of working memory—that is, visual-spatial
and phonological—so that they make sense to us. This incorporation allows us to solve
problems and re-evaluate previous experiences with more recent knowledge.
Whereas the three-store view emphasizes the structural receptacles for stored information (a relatively passive task), the working-memory model underscores the functions
of working memory in governing the processes of memory. These processes include encoding and integrating information. Examples are integrating acoustic and visual information through cross-modality, organizing information into meaningful chunks, and linking
new information to existing forms of knowledge representation in long-term memory.
We can conceptualize the differing emphases with contrasting metaphors. For
example, we can compare the three-store view to a warehouse in which information
is passively stored. The sensory store serves as the loading dock. The short-term store
comprises the area surrounding the loading dock. Here, information is stored temporarily until it is moved to or from the correct location in the warehouse (long-term store).
A metaphor for the working-memory model might be a multimedia production
house. It continuously generates and manipulates images and sounds. It also coordinates the integration of sights and sounds into meaningful arrangements. Once images,
sounds, and other information are stored, they are still available for reformatting and
reintegration in novel ways, as new demands and new information become available.
Neuroscience and Working Memory
Neuropsychological methods, and especially brain imaging, can be very helpful in
understanding the nature of memory. Support for a distinction between working
memory and long-term memory comes from neuropsychological research. Neuropsychological studies have shown abundant evidence of a brief memory buffer. The
buffer is used for remembering information temporarily. It is distinct from longterm memory, which is used for remembering information for long periods (Rudner
et al., 2007; Squire & Knowlton, 2000).
Furthermore, through some promising new research using positron emission tomography (PET) techniques, investigators have found evidence for distinct brain
areas involved in the different aspects of working memory. The phonological loop,
maintaining speech-related information, appears to involve activation in the left
hemisphere of the lateral frontal and inferior parietal lobes as well as the temporal
lobe (Gazzaniga et al., 2009; Baddeley, 2006).
It is interesting that the visuospatial sketchpad appears to activate slightly different areas. Which ones it activates depends on factors like task difficulty and the
length of the retention interval (Logie & Della Sala, 2005). Shorter intervals activate areas of the occipital and right frontal lobes. Longer intervals activate areas of
the parietal and left frontal lobes (Haxby et al., 1995).
Relatively little is known about the central executive. The central executive
functions appear to involve activation mostly in the frontal lobes (Baddeley, 2006;
Roberts, Robbins, & Weiskrantz, 1996).
Finally, the episodic buffer operations seem to involve the bilateral activation of
the frontal lobes and portions of the temporal lobes, including the left hippocampus
(Rudner et al., 2007). Different aspects of working memory are represented in the
brain differently. Figure 5.6 shows some of these differences.
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CHAPTER 5 • Memory: Models and Research Methods
Areas involved in verbal working memory,
phonological storage, and subvocal rehersal
Left Hemisphere
Posterior
Supplementary motor,
parietal area
premotor area
Right Hemisphere
Supplementary motor,
premotor area
Superior
parietal
area
Broca’s area
Areas involved in phonological storage
Right Hemisphere
Left Hemisphere
Supplementary motor,
premotor area
Posterior
parietal area
Superior
parietal
area
Supplementary motor,
premotor area
Area involved in subvocal rehearsal
Left Hemisphere
Broca’s area
Figure 5.6 The Brain and Working Memory.
Different areas of the cerebral cortex are involved in different aspects of working memory.
The figure shows those aspects involved primarily in the articulatory loop, including phonological storage and subvocal rehearsal.
Source: From E. Awh et al. (1996). Dissociation of storage and rehearsal in verbal working memory: Evidence
from positron emission tomography. Psychological Science, 7, 25–31. Copyright © 1996 by Blackwell, Inc.
Reprinted by permission.
Models of Memory
207
Measuring Working Memory
Working memory can be measured through a number of different tasks. The most
commonly used are shown in Figure 5.7.
Task (a) is a retention-delay task. It is the simplest task shown in the figure. An
item is shown—in this case, a geometric shape. (The þ at the beginning is merely a
Item task
Item task
4
Item test
Item test
****
2
Retention delay
(filled or unfilled)
t
7
t
Task:
old or new?
Task:
old or new?
3
5
(b) Temporally ordered working memory load task
(a) Retention delay task
Relational (order) task
Relational (order) task
37
9
6
Test
6
****
2
7
t
1 back
7
9
t
Task: which is
most recent?
3
2 back 4 back
7
5
3
Task:
find and repeat n-back
****
(d) n-back task
(c) Temporal order task
Running span task
Span task
'5 3 7 2'
'5 3 7 2'
****
****
2
t
7
t
3
Task: reproduce
final items in
correct order
Task: reproduce
in correct order
Yes or no
5
(e) Temporally ordered working memory load task
(f) Temporally ordered working memory load task
Figure 5.7 Tasks to Assess Working Memory.
Different kinds of tasks can be used to assess working memory.
Source: From Encyclopedia of Cognitive Science, 4, p. 571. Copyright © 2003. Reproduced with permission of B. Dosher.
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CHAPTER 5 • Memory: Models and Research Methods
focus point to indicate that the series of items is beginning.) There is then a retention interval, which may be filled with other tasks, or unfilled; in which case time
passes without any specifically designed intervening activity. The participant is then
presented with a stimulus and must say whether it is old or new. In the figure, the
stimulus being tested is new. So “new” would be the correct answer.
Task (b) is a temporally ordered working memory load task. A series of items is
presented. After a while, the series of asterisks indicates that a test item will be presented. The test item is presented, and the participant must say whether the item is
old or new. Because “4,” the number in the figure, has not been presented before,
the correct answer is “new.”
Task (c) is a temporal order task. A series of items is presented. Then the asterisks indicate a test item will be given. The test item shows two previously presented items, 3 and 7. The participant must indicate which of the two numbers,
3 or 7, appeared more recently. The correct answer is 7 because 7 occurred after 3
in the list.
Task (d) is an n-back task. Stimuli are presented. At specified points, one is
asked to repeat the stimulus that occurred n presentations back. For example, one might
be asked to repeat the digit that occurred 1 back—or just before (as with the 6). Or one
might be asked to repeat the digit that occurred 2 back (as with the 7).
Task (e) is a temporally ordered working memory load task. It can also be
referred to simply as a digit-span task (when digits are used). One is presented
with a series of stimuli. After they are presented, one repeats them back in the order
they were presented. A variant of this task has the participant repeat them back in
the order opposite to that in which they were presented—from the end to the
beginning.
Finally, Task (f ) is a temporally ordered working memory load task. One is given
a series of simple arithmetic problems. For each problem, one indicates whether the
sum or difference is correct. At the end, one repeats the results of the arithmetic
problems in their correct order.
Each of the tasks described here and in Figure 5.7 allows for the examination of
how much information we can manipulate in memory. Frequently, these tasks are
paired with a second task (called, appropriately, a secondary task) so that researchers
can learn more about the central executive. The central executive is responsible for
allocating attentional and other resources to ongoing tasks. By having participants
do more than one task at once, we can examine how mental resources are assigned
(Baudouin et al., 2006; D’Amico & Guarnera, 2005). A task that often is paired
with those listed in Figure 5.7 is a random-number generation task. In this task,
the participant must try to generate a random series of numbers while completing a
working memory task (Rudkin, Pearson, & Logie, 2007).
Intelligence and Working Memory
Recent work suggests that a critical component of intelligence may be working
memory. Indeed, some investigators have argued that intelligence may be little
more than working memory (Kyllonen & Christal, 1990). In one study, participants
read sets of passages and, after they had read the passages, tried to remember the last
word of each passage (Daneman & Carpenter, 1983). Recall was highly correlated
with verbal ability. In another study, participants performed a variety of working
memory tasks. In one task, for example, the participants saw a set of simple arithmetic problems, each of which was followed by a word or a digit. An example would be
Models of Memory
209
“Is (3 5) 6 ¼ 7? TABLE” (Turner & Engle, 1989; see also Hambrick, Kane, &
Engle, 2005). The participants saw sets of from two to six such problems and solved
each one. After solving the problems in the set, they tried to recall the words that
followed the problems. The number of words recalled was highly correlated with
measured intelligence.
There are indications that a measure of working memory can provide almost
perfect prediction of scores on tests of general ability (Colom et al., 2004; see also
Kane, Hambrick, & Conway, 2005). Other researchers have demonstrated a significant but smaller relationship between working memory and general intelligence
(e.g., Ackerman, Beier, & Boyle, 2005). Thus, it appears that the ability to store
and manipulate information in working memory may be an important aspect of
intelligence. It is probably not all there is to intelligence, however.
Multiple Memory Systems
The working-memory model is consistent with the notion that multiple systems may
be involved in the storage and retrieval of information. Recall that when Wilder
Penfield electrically stimulated the brains of his patients, the patients often asserted
that they vividly recalled particular episodes and events. They did not, however, recall semantic facts that were unrelated to any particular event. These findings suggest that there may be at least two separate explicit memory systems. One would be
for organizing and storing information with a distinctive time referent. It would address questions such as, “What did you eat for lunch yesterday?” or “Who was the
first person you saw this morning?” The second system would be for information
that has no particular time referent. It would address questions such as, “Who were
the two psychologists who first proposed the three-stores model of memory?” and
“What is a mnemonist?”
Based on such findings, Endel Tulving (1972) proposed a distinction between
two kinds of explicit memory. Semantic memory stores general world knowledge.
It is our memory for facts that are not unique to us and that are not recalled
in any particular temporal context. Episodic memory stores personally experienced
events or episodes. According to Tulving, we use episodic memory when we
learn lists of words or when we need to recall something that occurred to us at a
particular time or in a particular context. In either case, we have personally experienced the learning as associated with a given time. The list we learn in the experiment, for example, is associated with the experiment as the context for learning.
For example, suppose I needed to remember that I saw Harrison Hardimanowitz in
the dentist’s office yesterday. I would be drawing on an episodic memory. But if
I needed to remember the name of the person I now see in the waiting room
(“Harrison Hardimanowitz”), I would be drawing on a semantic memory. There is
no particular time tag associated with the name of that individual being Harrison.
But there is a time tag associated with my having seen him at the dentist’s office
yesterday.
Tulving (1983, 1989) and others (e.g., Shoben, 1984) provide support for the
distinction between semantic and episodic memory. It is based on both cognitive
research and neurological investigation. The neurological investigations have involved electrical-stimulation studies, studies of patients with memory disorders,
and cerebral blood flow studies. For example, lesions in the frontal lobe appear to
affect recollection regarding when a stimulus was presented. But they do not affect
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CHAPTER 5 • Memory: Models and Research Methods
recall or recognition memory that a particular stimulus was presented (Schacter,
1989a).
However, it is not clear that semantic and episodic memories are two distinct
systems. They sometimes appear to function in different ways. But many cognitive
psychologists question this distinction (e.g., Eysenck & Keane, 1990; Humphreys,
Bain, & Pike, 1989). They point out that the boundary between these two types of
memory is often fuzzy. They also note methodological problems with some of the
supportive evidence. Perhaps episodic memory is merely a specialized form of semantic memory (Tulving, 1984, 1986).
Some neurological evidence suggests that these two types of memory are separate, however. Through neuropsychological methods, investigators found dissociations, which means that separate and distinct areas seem to be involved in
semantic versus episodic memory retrieval (Prince, Tsukiura, & Cabeza, 2007).
When researchers find neural substrates of particular brain functions, one speaks
about dissociation. There are patients who suffer only from loss of semantic memory,
but their episodic memory is not impaired, as well as vice versa (Temple & Richardson, 2004; Vargha-Khadem et al., 1997). A person with semantic memory loss may
have trouble remembering what date it is or who the current president is; a person
with episodic memory loss cannot remember personal events like where she met her
spouse for the first time. These observations indicate that there is a dissociation between the two kinds of memory. These findings all support the conclusion that there
are separate episodic and semantic memory systems.
A neuroscientific model called HERA (hemispheric encoding/retrieval asymmetry) attempts to account for differences in hemispheric activation for semantic versus
episodic memories. According to this model, there is greater activation in the left
than in the right prefrontal hemisphere for tasks requiring retrieval from semantic
memory (Nyberg, Cabeza, & Tulving, 1996; Tulving et al., 1994). In contrast, there
is more activation in the right than in the left prefrontal hemisphere for episodicretrieval tasks. This model, then, proposes that semantic and episodic memories
must be distinct because they draw on separate areas of the brain. For example, if
one is asked to generate verbs that are associated with nouns (e.g., “drive” with
“car”), this task requires semantic memory. It results in greater left-hemispheric
activation (Nyberg, Cabeza, & Tulving, 1996). In contrast, if people are asked to
freely recall a list of words—an episodic-memory task—they show more righthemispheric activation. Some recent fMRI and ERP studies have not found the predicted frontal asymmetries during encoding and retrieval (Berryhill et al., 2007;
Evans & Federmeier, 2009).
Other findings suggest that the neural processes involved in these memories
overlap (Rajah & McIntosh, 2005). Although there is substantial behavioral and
neurological evidence that there are differences between these two types of memory,
most researchers agree that there is, at the very least, a great deal of interaction between these two types of memory. As a result, the question of whether these forms of
memory are separate is still open.
A taxonomy of the memory system in terms of the dissociations described in the
previous sections is shown in Figure 5.8 (Squire, 1986, 1993). It distinguishes declarative (explicit) memory from various kinds of nondeclarative (implicit) memory.
Nondeclarative memory comprises procedural memory, priming effects, simple
Models of Memory
211
IN THE LAB OF MARCIA K. JOHNSON
Memory and the Brain
Several types of evidence indicate
that the prefrontal cortex (PFC) plays a
A memory is a mental experience that is
key role both in binding features of stimuli
taken to be a veridical (truthful) representogether during encoding and in later identation of an event from one’s past. Attributifying the sources of mental experiences
tions we make about the origin of the
during remembering. Damage to PFC proactive information that constitutes our menduces deficits in source memory. Source
tal experience are the result of cognitive
memory errors are more likely in children
processes that encode, revive, and moni(whose frontal lobes are slow to develop)
MARCIA K. JOHNSON
tor information from various sources or
and in older adults (who are likely to show
experiences. The integration of information across indiincreased neuropathology in PFC with age). PFC dysvidual experiences is necessary for all higher order—
function may also play a role in schizophrenia, which
complex thought. But this very capacity for creative intesometimes includes severe source monitoring deficits in
gration of information from multiple events makes us
the form of delusions or hallucinations. Neuroimaging is
vulnerable to false memories because we somehelping to clarify the specific functions of PFC in source
times misattribute the sources of the information that
memory.
comes to mind. Source monitoring errors include many
For example, in one type of study, participants
types of confusions, for example, attributing something
see a series of items of two types (e.g., pictures and
that was imagined to perception, an intention to an
words). Later they are given a memory test in which
action, something only heard about to something one
they are shown three kinds of words: words that correwitnessed, something read in a tabloid to a television
spond to the pictures seen earlier, words seen earlier as
news program, or an incident that occurred in place A
words, and new words that do not correspond to any
or at time A to place B or time B. Memories can be false
of the items seen earlier (new items). They are asked to
in relatively minor ways (e.g., believing one last saw the
identify the source of some items (e.g., say “yes” to
car keys in the kitchen when they actually were in the
items previously seen as pictures), and for other items
living room) and in major ways that have profound
to simply decide if they are familiar (say “yes” to any
implications for oneself and others (e.g., mistakenly
previously presented [“old”] item). Typically there is
believing one is the source or originator of an idea,
greater brain activity in PFC in the source identification
or believing that one was sexually abused as a child
compared with the old/new test condition. Studies
when one was not).
from our lab and other labs suggest that both right
Investigators from many labs are using neuroimagand left PFC contribute to evaluating the origin of mening (e.g., functional magnetic resonance imaging
tal experiences, possibly in different ways (e.g., engag[fMRI]) to help identify the brain regions that encode
ing different processes or monitoring different types of
different features of events (e.g., scenes [parainformation), and interactions between the right and left
hippocampal gyrus], faces [fusiform gyrus], lateral ochemispheres are likely important. Thus, one goal for fucipital cortex [objects]), and the regions involved in
ture research is to relate specific component processes
binding these features into representations of complex
of cognition to patterns of activity across various reevents (e.g., hippocampus). We have been particularly
gions of the PFC and to specify how PFC regions interinterested in the fact that the same regions are active
act with other brain regions (e.g., the hippocampus
when you perceptually process something (e.g., a viand various feature representational areas) in producsual scene) and when you think of it. This similarity being the subjective experiences we take to be
tween perception and reflection is one of the factors
memories.
that sets the stage for false memories.
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CHAPTER 5 • Memory: Models and Research Methods
Memory
Declarative
Semantic
(facts)
Nondeclarative
Episodic
(events)
Procedural
skills (e.g.,
motor, perceptual,
cognitive)
Priming
(perceptual,
semantic)
Conditioning
Nonassociative
(habituation,
sensitization)
Figure 5.8 A Taxonomy of the Memory System.
Based on extensive neuropsychological research, Larry Squire has posited that memory comprises two fundamental
types: declarative (explicit) memory and various forms of nondeclarative (implicit) memory, each of which may be associated with discrete cerebral structures and processes.
classical conditioning, habituation, sensitization, and perceptual aftereffects. In yet
another view, there are five memory systems in all: episodic, semantic, perceptual
(i.e., recognizing things on the basis of their form and structure), procedural, and
working memory (Schacter, 2000).
A Connectionist Perspective
The network model provides the structural basis for the connectionist parallel distributed processing (PDP) model (see also Chapter 8; Frean, 2003; Sun, 2003). According to the PDP model, the key to knowledge representation lies in the connections
among various nodes, or elements, stored in memory, not in each individual node
(Feldman & Shastri, 2003). Activation of one node may prompt activation of a connected node. This process of spreading activation may prompt the activation of additional nodes (Figure 5.9). The PDP model fits nicely with the notion of working
memory as comprising the activated portion of long-term memory. In this model,
activation spreads through nodes within the network. This spreading continues as
long as the activation does not exceed the limits of working memory.
A prime is a node that activates a connected node. A priming effect is the
resulting activation of the node. The priming effect has been supported by considerable evidence. Examples are the aforementioned studies of priming as an aspect of
implicit memory. In addition, some evidence supports the notion that priming is
due to spreading activation (McClelland & Rumelhart, 1985, 1988). But not everyone agrees about the mechanism for the priming effect (see McKoon & Ratcliff,
1992b).
Connectionist models also have some intuitive appeal in their ability to integrate
several contemporary notions about memory: Working memory comprises the activated
portion of long-term memory and operates through at least some amount of parallel
processing. Spreading activation involves the simultaneous (parallel) activation (priming) of multiple links among nodes within the network. Many cognitive psychologists
who hold this integrated view suggest that part of the reason we humans are as efficient as we are in processing information is that we can handle many operations at
once. Thus, the contemporary cognitive-psychological conceptions of working
Models of Memory
213
Output units
Pattern of
activation
represents
“canary”
Hidden units
Input units
Canary
Figure 5.9 Connectionist Network.
A connectionist network consists of many different nodes. Unlike in semantic networks, it is
not a single node that has a specific meaning, but rather the knowledge is represented in a
combination of differently activated nodes. The size of the dots inside the nodes above indicates the amount of activation (with larger dots indicating more activation). The concept of a
canary is represented by the overall pattern of activation.
Source: From Cognitive Psychology, 2nd ed., by E. Bruce Goldstein, Copyright © 2008.
memory, network models of memory, spreading activation, priming, and parallel processes mutually enhance and support one another.
Some of the research supporting this connectionist model of memory has come
directly from experimental studies of people performing cognitive tasks in laboratory
settings. Connectionist models effectively explain priming effects, skill learning (procedural memory), and several other phenomena of memory. Thus far, however, connectionist models have failed to provide clear predictions and explanations of recall
and recognition memory that occurs following a single episode or a single exposure
to semantic information.
In addition to using laboratory experiments on human participants, cognitive
psychologists have used computer models to simulate various aspects of information
processing. The three-store model is based on serial (sequential) processing of information. Serial processing can be simulated on individual computers that handle only
one operation at a time. In contrast, the parallel-processing model of working memory, which involves simultaneous processing of multiple operations, cannot be simulated on a single computer. Parallel processing requires neural networks. In these
networks, multiple computers are linked and operate in tandem. Alternatively, a single special computer may operate with parallel networks. Many cognitive psychologists now prefer a parallel-processing model to describe many phenomena of
memory. The parallel-processing model was actually inspired by observing how the
human brain seems to process information. Here, multiple processes go on at the
same time. In addition to inspiring theoretical models of memory function, neuropsychological research has offered specific insights into memory processes. It also has provided evidence regarding various hypotheses of how human memory works.
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CHAPTER 5 • Memory: Models and Research Methods
Not all cognitive researchers accept the connectionist model. Some believe that
human thought is more systematic and integrated than connectionist models seem to
allow (Fodor & Pylyshyn, 1988; Matthews, 2003). They believe that complex behavior displays a degree of top-down orderliness and purposefulness that connectionist
models, which are bottom-up, cannot incorporate. Connectionist modelers dispute
this claim. The issue will be resolved as cognitive psychologists explore the extent to
which connectionist models can reproduce and even explain complex behavior.
CONCEPT CHECK
1. What is the difference between the sensory store and the short-term store?
2. What are levels of processing?
3. What are the components of the working-memory model?
4. Why do we need both semantic and episodic memories?
5. Describe a connectionist model of memory.
Exceptional Memory and Neuropsychology
Up to this point, the discussion of memory has focused on tasks and structures involving normally functioning memory. However, there are rare cases of people with
exceptional memory (either enhanced or deficient) that provide some interesting
insights into the nature of memory in general. The study of exceptional memory
leads directly to neuropsychological investigations of the physiological mechanisms
underlying memory.
Outstanding Memory: Mnemonists
Imagine what your life would be like if you were able to remember every word
printed in this book. In this case, you would be considered a mnemonist, someone
who demonstrates extraordinarily keen memory ability, usually based on using special techniques for memory enhancement. Perhaps the most famous of mnemonists
was a man called “S.”
Russian psychologist Alexander Luria (1968) reported that one day S. appeared in
his laboratory and asked to have his memory tested. Luria tested him. He discovered
that the man’s memory appeared to have virtually no limits. S. could reproduce extremely long strings of words, regardless of how much time had passed since the words
had been presented to him. Luria studied S. for over 30 years. He found that even
when S.’s retention was measured 15 or 16 years after a session in which S. had learned
words, S. still could reproduce the words. S. eventually became a professional entertainer. He dazzled audiences with his ability to recall whatever was asked of him.
What was S.’s trick? How did he remember so much? Apparently, he relied
heavily on the mnemonic of visual imagery. He converted material that he needed
to remember into visual images. For example, he reported that when asked to remember the word green, he would visualize a green flowerpot. For the word red, he
visualized a man in a red shirt coming toward him. Numbers called up images. For
example, 1 was a proud, well-built man. The number 3 was a gloomy person. The
number 6 was a man with a swollen foot, and so on.
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215
For S., much of his use of visual imagery in memory recall was not intentional.
Rather, it was the result of a rare psychological phenomenon. This phenomenon,
termed synesthesia, is the experience of sensations in a sensory modality different
from the sense that has been physically stimulated. For example, S. automatically
would convert a sound into a visual impression. He even reported experiencing a
word’s taste and weight. Each word to be remembered evoked a whole range of sensations that automatically would come to S. when he needed to recall that word.
Other mnemonists have used different strategies. “V. P.,” a Russian immigrant,
could memorize long strings of material, such as rows and columns of numbers (Hunt
& Love, 1972). Whereas S. relied primarily on visual imagery, V. P. apparently relied more on verbal translations. He reported memorizing numbers by transforming
them into dates. Then he would think about what he had done on that day.
Another mnemonist, “S. F.,” remembered long strings of numbers by segmenting
them into groups of three or four digits each. He then encoded them into running
times for different races (Ericsson, Chase, & Faloon, 1980). An experienced longdistance runner, S. F. was familiar with the times that would be plausible for different
races. S. F. did not enter the laboratory as a mnemonist. Rather, he had been selected
to represent the average college student in terms of intelligence and memory ability.
S. F.’s original memory for a string of numbers was about seven digits, average
for a college student. After 200 practice sessions distributed over a period of 2 years,
however, S. F. had increased his memory for digits more than tenfold. He could recall up to about 80 digits. His memory was impaired severely, however, when the
experimenters purposely gave him sequences of digits that could not be translated
into running times. The work with S. F. suggests that a person with a fairly typical
level of memory ability can, at least in principle, be converted into one with quite
an extraordinary memory. At least, this is possible in some domains, following a
great deal of concerted practice.
Many of us yearn to have memory abilities like those of S. or V. P. In this way,
we may believe we could ace our exams virtually effortlessly. However, we should
consider that S. was not particularly happy with his life, and part of the reason was
his exceptional memory. He reported that his synesthesia, which was largely involuntary, interfered with his ability to listen to people. Voices gave rise to blurs of sensations. They in turn interfered with his ability to follow a conversation. Moreover, S.’s
heavy reliance on imagery created difficulty for him when he tried to understand abstract concepts. For example, he found it hard to understand concepts such as infinity
or nothing. These concepts do not lend themselves well to visual images. He also
sometimes was overwhelmed when he read. Earlier memories also sometimes intruded
on later ones. Of course, we cannot say how many of S.’s problems in life were caused
by his exceptional memory. But clearly S. believed that his exceptional memory had
a downside as well as an upside. It was often as likely to be a hindrance as a help.
These exceptional mnemonists offer some insight into processes of memory.
Each of the three described here did more or less the same thing—consciously or
almost automatically. Each translated arbitrary, abstract, meaningless information
into more meaningful and often more concrete information, sometimes connected
to the senses. Whether the translated information was racing times, dates and
events, or visual images, the key was their meaning for the mnemonist.
Like the mnemonists, we more easily encode information into our long-term
memory that is similar to the information already stored there. Because we have information in long-term memory that pertains to our interests, it is easier to learn
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CHAPTER 5 • Memory: Models and Research Methods
n BELIEVE IT OR NOT
YOU CAN BE
A
MEMORY CHAMPION, TOO!!!
lively pictures that combine the numbers with the items you
need to buy. For item #1, you can imagine beans growing up high on a flagpole, for example. For item #2, you
can imagine a swan with red plumage because it is swimming in a pond of chopped tomatoes. And for item #3,
you can imagine a nice plate of breakfast cereal shaped
in the form of hearts. You get the idea? Once you are in
the supermarket, you’ll just work down your list from the
first item to the last, imagining your created pictures. There
are no rules except that the representations have to work
for you. With a little bit of practice you’ll soon be able to
memorize long lists of words, even more complicated or
abstract ones. This technique is one of many mnemonic
techniques that belong to the group of association
techniques.
Karin Sternberg
Have you ever heard about people who can effortlessly
remember huge lists of words or numbers? Or would you
already be satisfied if you could memorize your shopping
list? Well, you can do this, too! How? The first thing you
need to do is to come up with a nice system that helps you
remember numbers. Then you connect the words you
want to remember with those numbers. Sounds too complicated? Not really. The example below illustrates how
you can imagine numbers as representations of objects
(remember, you can create your own system!):
Once you are intimately familiar with your representations of numbers, you can start connecting them with
words you would like to remember. Assume you want to
buy beans, chopped tomatoes, and cereal. You’ll create
new information that is in line with these interests that we can relate to the old
information (De Beni et al., 2007). Thus, you may be able to remember the lyrics
of your favorite songs from years ago but not be able to recall the definitions of
new terms that you have just learned. You can improve your memory for new information if you can relate the new information to old information already stored in
long-term memory.
If you are unable to retrieve a memory that you need, does it mean that you have
forgotten it? Not necessarily. Cognitive psychologists have studied a phenomenon
called hypermnesia, which is a process of producing retrieval of memories that would
seem to have been forgotten (Erdelyi & Goldberg, 1979; Holmes, 1991; Turtle &
Yuille, 1994). Hypermnesia is sometimes loosely referred to as “unforgetting,”
although the terminology cannot be correct because, strictly speaking, the memories
Exceptional Memory and Neuropsychology
217
that are retrieved were never unavailable (i.e., forgotten), but rather, inaccessible
(i.e., hard to retrieve). Hypermnesia is usually achieved by trying many and diverse
retrieval cues to unearth a memory. Psychodynamic therapy, for example, is sometimes
used to try to achieve hypermnesia. This therapy also points out the risk of trying
to achieve hypermnesia. The individual may create a new memory, believing it is
an old one, rather than retrieving a genuine old memory. In cases where there are
accusations of abuse against a parent or other individual, newly created memories
posing as old memories could pose a serious problem leading to false accusations.
We usually take for granted the ability to remember, much like the air we breathe.
However, just as we become more aware of the importance of air when we do not have
enough to breathe, we are less likely to take memory for granted when we observe
people with serious memory deficiencies.
Deficient Memory
There are many syndromes associated with memory loss. Just as with the study of
exceptionally good memory, the study of deficient memory provides us with many
valuable insights into how memory works. In this section, we will have a look at
two syndromes. The first and also most well known is amnesia. Afterwards, we will
explore the symptoms and causes of Alzheimer’s disease, which is another prominent
disease that causes memory loss.
© Yang Liu/CORBIS
Amnesia
We begin this section on amnesia by looking at some case studies to gain a better
understanding of what amnesia is and what different kinds of amnesia exist. Afterwards, we will consider what insights can be gained about the differences between
implicit and explicit memory by studying amnesia, and have a look at neuropsychological findings in the context of amnesia.
If the patient uses hypermnesia to dredge up what has seemed to be a forgotten memory, we often cannot
be certain that the memory is genuine, rather than one newly created by suggestion.
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CHAPTER 5 • Memory: Models and Research Methods
What Is Amnesia? Amnesia is severe loss of explicit memory (Robbins, 2009).
One type is retrograde amnesia, in which individuals lose their purposeful memory
for events prior to whatever trauma induces memory loss (Levine et al., 2009;
Squire, 1999). Mild forms of retrograde amnesia can occur fairly commonly when
someone sustains a concussion. Usually, events immediately prior to the concussive
episode are not well remembered.
W. Ritchie Russell and P. W. Nathan (1946) reported a more severe case of
retrograde amnesia. A 22-year-old landscaper was thrown from his motorcycle in
August of 1933. A week after the accident, the young man was able to converse
sensibly. He seemed to have recovered. However, it quickly became apparent that
he had suffered a severe loss of memory for events that had occurred prior to the
trauma. On questioning, he gave the date as February 1922. He believed himself to
be a schoolboy. He had no recollection of the intervening years. Over the next several weeks, his memory for past events gradually returned. The return started with
the least recent event and proceeded toward more recent events. By 10 weeks after
the accident, he had recovered his memory for most of the events of the previous
years. He finally was able to recall everything that had happened up to a few minutes prior to the accident. In retrograde amnesia, the memories that return typically
do so starting from the more distant past. They then progressively return up to the
time of the trauma. Often events right before the trauma are never recalled.
One of the most famous cases of amnesia is the case of H. M. (Scoville & Milner, 1957). H. M. underwent brain surgery to save him from continual disruptions
due to uncontrollable epilepsy. The operation took place on September 1, 1953. It
was largely experimental. The results were highly unpredictable. At the time of the
operation, H. M. was 29 years old. He was above average in intelligence. After the
operation, his recovery was uneventful with one exception. He suffered severe anterograde amnesia, the inability to remember events that occur after a traumatic event.
However, he had good (although not perfect) recollection of events that had
occurred before his operation. H. M.’s memory loss severely affected his life. H. M.
has been extensively studied through behavioral and neurological methods. On one
occasion, he remarked, “Every day is alone in itself, whatever enjoyment I’ve had,
and whatever sorrow I’ve had” (Milner, Corkin, & Teuber, 1968, p. 217). Many
years after the surgery, H. M. still reported that the year was 1953. He also could
not recall the name of any new person he met after the operation, regardless of the
number of times they interacted. Apparently, H. M. lost his ability to recollect any
new memories of the time following his operation. As a result, he lives suspended in
an eternal present.
The examination of H. M.’s memory is ongoing, with recent work examining
changes in H. M.’s memory and brain as he ages. These recent studies have noted
additional memory and cognitive declines. In particular, H. M. exhibited new problems with comprehension and generation of new sentences (MacKay, 2006;
MacKay et al., 2006; Salat et al., 2006; Skotko et al., 2004).
Another kind of “amnesia” that we all experience is infantile amnesia, the inability to recall events that happened when we were very young (Spear, 1979). (We
place “amnesia” in quotation marks because some investigators question whether
infantile amnesia is truly a form of amnesia at all.)
Amnesia and the Explicit-Implicit Memory Distinction Why do researchers study
amnesia patients? What kinds of insight can be gained from amnesia research? One
of the general insights gained by studying amnesia victims highlights the distinction
Exceptional Memory and Neuropsychology
219
between explicit and implicit memories. Explicit memory is typically impaired in
amnesia. Implicit memory, such as priming effects on word-completion tasks and
procedural memory for skill-based tasks, is typically not impaired. This observation
indicates that two kinds of abilities need to be distinguished. The first is the
ability to reflect consciously on prior experience, which is required for tasks involving explicit memory. The second is the ability to demonstrate remembered learning
in an apparently automatic way, without conscious recollection of the learning
(implicit memory; Baddeley, 1989). Priming effects can be seen from about 250
to 500 milliseconds after exposure through positive brain potentials recorded in
the frontal region of the brain. Explicit memory retrieval, however, is indicated
by brain potentials that appear at a later time in the posterior regions (Voss & Paller,
2006).
Amnesia victims perform extremely poorly on most explicit memory tasks, but
they may show normal or almost-normal performance on tasks involving implicit
memory, such as cued-recall tasks (Warrington & Weiskrantz, 1970) and wordcompletion tasks (Baddeley, 1989). What do you think happens after wordcompletion tasks? When amnesics were asked whether they previously had seen the
word they just completed, they were unlikely to remember the specific experience of
having seen the word (Graf, Mandler, & Haden, 1982; Tulving, Schacter, & Stark,
1982). Furthermore, these amnesics do not explicitly recognize words they have seen
at better than chance levels. Although the distinction between implicit memory and
explicit memory has been readily observed in amnesics, both amnesics and normal
participants show the presence of implicit memory.
Likewise, amnesia victims also show paradoxical performance in another regard.
Consider two kinds of tasks. As previously described, procedural-knowledge tasks involve “knowing how.” They involve skills such as how to ride a bicycle, whereas
declarative-knowledge tasks involve “knowing that.” They tap factual information,
such as the terms in a psychology textbook. On the one hand, amnesia victims
may perform extremely poorly on the traditional memory tasks requiring recall
or recognition memory of declarative knowledge. On the other hand, they may
demonstrate improvement in performance resulting from learning—remembered
practice—when engaged in tasks that require procedural knowledge. Such tasks
would include solving puzzles, learning to read mirror writing, or mastering motor
skills (Baddeley, 1989).
Consider an example of procedural knowledge that is retained when a person
suffers from amnesia. Patients with amnesia, when asked to drive in a normal situation, were able to operate and control the car as a normal driver would (Anderson et
al., 2007). However, the investigators also exposed the patients to a simulation in
which a complex accident sequence was experienced. In this situation, the patients
with amnesia showed significant impairment. They could not recall the proper response to this situation. This finding is in line with the fact that in patients with
amnesia, implicit, procedural knowledge is spared, while explicit knowledge is impaired. Most drivers do not have extensive experience with complex accidentavoidance scenarios and therefore would have to rely more on their declarative
memory to make decisions about how to respond.
Amnesia and Neuropsychology Studies of amnesia victims have revealed much
about the way in which memory depends on the effective functioning of particular
structures of the brain. By looking for matches between particular lesions in the
brain and particular deficits of function, researchers come to understand how normal
CHAPTER 5 • Memory: Models and Research Methods
William Haefell/www.Cartoonbank.com
220
“I’m not losing my memory.
I’m living in the now.”
memory functions. Thus, when studying cognitive processes in the brain, neuropsychologists frequently look for dissociations of function. In dissociations, normal individuals show the presence of a particular function (e.g., explicit memory). But
people with specific lesions in the brain show the absence of that particular function. This absence occurs despite the presence of normal functions in other areas
(e.g., implicit memory).
By observing people with disturbed memory function, we know that memory is
volatile. A blow to the head, a disturbance in consciousness, or any number of other
injuries to or diseases of the brain may affect it. We cannot determine, however, the
specific cause-effect relationship between a given structural lesion and a particular
memory deficit. The fact that a particular structure or region is associated with an
interruption of function does not mean that the region is solely responsible for controlling that function. Indeed, functions can be shared by multiple structures or
regions. A broad physiological analogy may help to explain the difficulty of determining localization based on an observed deficit. The normal functioning of a portion of the brain—the reticular activating system (RAS)—is essential to life. But life
depends on more than a functioning brain. If you doubt the importance of other
structures, ask a patient with heart or lung disease. Thus, although the RAS is essential to life, a person’s death may be the result of malfunction in other structures of
the body. Tracing a dysfunction within the brain to a particular structure or region
poses a similar problem.
For the observation of simple dissociations, many alternative hypotheses may explain a link between a particular lesion and a particular deficit of function. Much
more compelling support for hypotheses about cognitive functions comes from observing double dissociations. In double dissociations, people with different kinds of
neuropathological conditions show opposite patterns of deficits. A double dissociation can be observed if a lesion in brain structure 1 leads to impairment in memory
function A but not in memory function B; and a lesion in brain structure 2 leads to
impairment in memory function B but not in memory function A.
For some functions and some areas of the brain, neuropsychologists have managed to observe the presence of a double dissociation. For example, some evidence
Exceptional Memory and Neuropsychology
221
for distinguishing brief memory from long-term memory comes from just such a double dissociation (Schacter, 1989b). People with lesions in the left parietal lobe of the
brain show profound inability to retain information in short-term memory, but they
show no impairment of long-term memory. They continue to encode, store, and retrieve information in long-term memory, apparently with little difficulty (Shallice &
Warrington, 1970; Warrington & Shallice, 1972). In contrast, persons with lesions
in the medial (middle) temporal regions of the brain show relatively normal shortterm memory of verbal materials, such as letters and words, but they show serious
inability to retain new verbal materials in long-term memory (Milner, Corkin, &
Teuber, 1968; Shallice, 1979; Warrington, 1982).
Double dissociations offer strong support for the notion that particular
structures of the brain play particular vital roles in memory (Squire, 1987). Disturbances or lesions in these areas cause severe deficits in memory formation. But we
cannot say that memory—or even part of memory—resides in these structures. Nonetheless, studies of brain-injured patients are informative and at least suggestive of how
memory works. At present, cognitive neuropsychologists have found that double dissociations support several distinctions. These distinctions are those between brief
memory and long-term memory and between declarative (explicit) and nondeclarative
(implicit) memory. There also are some preliminary indications of other distinctions.
Alzheimer’s Disease
Although amnesia is the syndrome most associated with memory loss, it is often less
devastating than a disease that includes memory loss as one of many symptoms.
Alzheimer’s disease is a disease of older adults that causes dementia as well as progressive memory loss (Kensinger & Corkin, 2003). Dementia is a loss of intellectual
function that is severe enough to impair one’s everyday life. The memory loss in
Alzheimer’s disease can be seen in comparative brain scans of individuals with and
without Alzheimer’s disease. Note in Figure 5.10 that as the disease advances, there
is diminishing cognitive activity in the areas of the brain associated with memory
function.
The disease was first identified by Alois Alzheimer in 1907. It is typically recognized on the basis of loss of intellectual function in daily life. Formally, a definitive
diagnosis is possible only after death. Alzheimer’s disease leads to an atrophy (decrease in size) of the brain; especially in the hippocampus and frontal and temporal
brain regions (Jack et al., 2002). The brains of people with the disease show plaques
and tangles that are not found in normal brains. Plaques are dense protein deposits
found outside the nerve cells of the brain (Mirochnic et al., 2009). Tangles are pairs
of filaments that become twisted around each other. They are found in the cell body
and dendrites of neurons and often are shaped like a flame (Kensinger & Corkin,
2003). Alzheimer’s disease is diagnosed when memory is impaired and there is at
least one other area of dysfunction in the domains of language, motor, attention,
executive function, personality, or object recognition. The symptoms are of gradual
onset, and the progression is continuous and irreversible.
Although the progression of disease is irreversible, it can be slowed somewhat.
The main drug currently being used for this purpose is Donepezil (Aricept).
Research evidence is mixed (Fischman, 2004). It suggests that, at best, Aricept may
slightly slow progression of the disease, but that it cannot reverse it. A more recent
drug, memantine (sold as Namenda or Ebixa), can supplement Aricept and slow
© Zepher/Photo Researchers, Inc.
CHAPTER 5 • Memory: Models and Research Methods
© CNRI/Phototake.
(b)
(a)
© CNRI/Phototake
222
(c)
Figure 5.10
The Brain with and without Alzheimer’s.
Brain scans of (a) a normal individual and (b) an individual with early-stage Alzheimer’s. You
can see the atrophy (black space) in the brain of the Alzheimer’s patient (b) compared with
the healthy person (a). Image (c) depicts PET scans of an individual with late-stage Alzheimer’s
and a healthy person. The metabolism in the healthy brain is much more pronounced. As the
disease progresses, cognitive activity in the brain associated with memory function decreases.
progression of the disease somewhat more. The two drugs have different mechanisms. Aricept slows destruction of the neurotransmitter acetylcholine in the brain.
Memantine inhibits a chemical that overexcites brain cells and leads to cell damage
and death (Fischman, 2004).
The incidence of Alzheimer’s increases exponentially with age (Kensinger &
Corkin, 2003). About 1% of people between 70 to 75 years of age experience an
onset of Alzheimers. But between ages 80 and 85, the incidence is more than 6% a
year.
A special kind of Alzheimer’s disease is familial, known as early-onset Alzheimer’s disease. It has been linked to a genetic mutation. People with the genetic mutation always develop the disease. It results in the disease exhibiting itself early,
often before even 50 years of age and sometimes as early as the 20s (Kensinger &
Corkin, 2003). Late-onset Alzheimer’s, in contrast, appears to be complexly determined and related to a variety of possible genetic and environmental influences,
none of which have been conclusively identified.
Exceptional Memory and Neuropsychology
223
The earliest signs of Alzheimer’s disease typically include impairment of episodic
memory. People have trouble remembering things that were learned in a temporal or
spatial context. As the disease progresses, semantic memory also begins to go.
Whereas people without the disease tend to remember emotionally charged information better than they remember non-emotionally charged information, people with
the disease show no difference in the two kinds of memory (Kensinger et al., 2002).
Most forms of nondeclarative memory are spared in Alzheimer’s disease until near the
very end of its course. The end is inevitably death, unless the individual dies first of
other causes.
Memory tests may be given to assess whether an individual has Alzheimer’s disease. However, definitive diagnosis is possible only through analysis of brain tissue,
which, as mentioned earlier, shows plaques and tangles in cases of disease. In one
test, individuals see a sheet of paper containing four words (Buschke et al., 1999).
Each word belongs to a different category. The examiner says the category name
for one of the words. The individual must point to the appropriate word. For example, if the category is animal, the individual might point to a picture of a cow. A few
minutes after the words have been presented, individuals make an attempt to recall
all the words they saw. If they cannot recall a word, they are given the category to
which the word belongs. Some individuals cannot remember the words, even when
prompted with the categories. Alzheimer’s patients score much worse on this test
than do other individuals.
How Are Memories Stored?
Where in the brain are memories stored, and what structures and areas of the brain
are involved in memory processes, such as encoding and retrieval? Many early attempts at localization of memory were unfruitful. For example, after literally hundreds of experiments, renowned neuropsychologist Karl Lashley (1950) reluctantly
stated that he could find no specific locations in the brain for specific memories. In
the decades since Lashley’s admission, psychologists have located many cerebral
structures involved in memory. For example, they know of the importance of the
hippocampus and other nearby structures. However, the physiological structure may
not be such that we will find Lashley’s elusive localizations of specific ideas,
thoughts, or events. Even Penfield’s findings regarding links between electrical stimulation and episodic memory of events have been subject to question.
Some studies show encouraging, although preliminary, findings regarding the
structures that seem to be involved in various aspects of memory. First, specific sensory properties of a given experience appear to be organized across various areas of
the cerebral cortex (Squire, 1986). For example, the visual, spatial, and olfactory
(odor) features of an experience may be stored discretely in each of the areas of the
cortex responsible for processing each type of sensation. Thus, the cerebral cortex
appears to play an important role in memory in terms of the long-term storage of
information (Zola & Squire, 2000; Zola-Morgan & Squire, 1990).
In addition, the hippocampus and some related nearby cerebral structures appear
to be important for explicit memory of experiences and other declarative information. The hippocampus also seems to play a key role in the encoding of declarative
information (Manns & Eichenbaum, 2006; Thompson, 2000). Its main function appears to be in the integration and consolidation of separate sensory information as
well as spatial orientation and memory (Ekstrom et al., 2003; Moscovitch, 2003;
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CHAPTER 5 • Memory: Models and Research Methods
Solstad et al., 2008). Most important, it is involved in the transfer of newly synthesized information into long-term structures supporting declarative knowledge. Perhaps such transfer provides a means of cross-referencing information stored in
different parts of the brain (Reber, Knowlton, & Squire, 1996). Additionally, the
hippocampus seems to play a crucial role in complex learning (Gupta et al., 2009;
McCormick & Thompson, 1984). Finally, the hippocampus also has a significant
role in the recollection of information (Gilboa et al., 2006).
In evolutionary terms, the aforementioned cerebral structures (chiefly the cortex
and the hippocampus) are relatively recent acquisitions. Declarative memory also
may be considered a relatively recent phenomenon. At the same time, other memory structures may be responsible for nondeclarative forms of memory. For example,
the basal ganglia seem to be the primary structures controlling procedural knowledge
(Shohamy et al., 2009). But they are not involved in controlling the priming effect
(Heindel, Butters, & Salmon, 1988), which may be influenced by various other
kinds of memory (Schacter, 1989b). Furthermore, the cerebellum also seems to
play a key role in memory for classically conditioned responses and contributes to
many cognitive tasks in general (Thompson & Steinmetz, 2009). Thus, various
forms of nondeclarative memory seem to rely on differing cerebral structures.
The amygdala is often associated with emotional events, so a natural question to
ask is whether, in memory tasks, there is involvement of the amygdala in memory
for emotionally charged events. In one study, participants saw two video presentations presented on separate days (Cahill et al., 1996). Each presentation involved
12 clippings, half of which had been judged as involving relatively emotional content and the other half as involving relatively unemotional content. As participants
watched the video clippings, brain activity was assessed by means of PET (see Chapter 2). After a gap of 3 weeks, the participants returned to the lab and were asked to
recall the clips. For the relatively emotional clips, amount of activation in the amygdala was associated with recall; for the relatively unemotional clips, there was no
association. This pattern of results suggests that when memories are emotionally
charged, the level of amygdala activation is associated with recall. In other words,
the more emotionally charged the emotional memory, the greater the probability
the memory will later be retrieved. There also may be a gender difference with regard to recall of emotional memories. There is some evidence that women recall emotionally charged pictures better than do men (Canli et al., 2002). The amygdala also
appears to play an important role in memory consolidation, especially where emotional experience is involved (Cahill & McGaugh, 1996; Roozendaal et al., 2008).
In addition to these preliminary insights regarding the macrolevel structures of
memory, we are beginning to understand the microlevel structure of memory. For
example, we know that repeated stimulation of particular neural pathways tends to
strengthen the likelihood of firing. This is called long-term potentiation (where potentiation refers to an increase in activity). In particular, at a particular synapse, there
appear to be physiological changes in the dendrites of the receiving neuron. These
changes make the neuron more likely to reach the threshold for firing again. This
finding is very important because it indicates that neurons in the hippocampus may
be able to change their interactions (i.e., that they are plastic).
We also know that some neurotransmitters disrupt memory storage. Others
enhance memory storage. Both serotonin and acetylcholine seem to enhance neural
transmission associated with memory. Norepinephrine also may do so. High concentrations of acetylcholine have been found in the hippocampus of normal people (Squire,
1987), but low concentrations are found in people with Alzheimer’s disease. In fact,
Alzheimer’s patients show severe loss of the brain tissue that secretes acetylcholine.
Key Themes
225
Serotonin also plays a role in another form of memory dysfunction, Korsakoff
syndrome. Severe or prolonged abuse of alcohol can lead to this devastating form of
anterograde amnesia. Alcohol consumption has been shown to disrupt the activity of
serotonin. It thereby impairs the formation of memories (Weingartner et al., 1983).
This syndrome is often accompanied by at least some retrograde amnesia (Clark et
al., 2007). Korsakoff’s syndrome has been linked to damage in the diencephalon (the
region comprising the thalamus and the hypothalamus) of the brain (Postma et al.,
2008). It also has been linked to dysfunction or damage in other areas, such as in the
frontal and the temporal lobes of the cortex (Jacobson et al., 1990; Kopelman et al.,
2009; Reed et al., 2003).
Other physiological factors also affect memory function. Some of the naturally
occurring hormones stimulate increased availability of glucose in the brain, which
enhances memory function. These hormones are often associated with highly arousing events. Examples of such events are traumas, achievements, first-time experiences (e.g., first passionate kiss), crises, or other peak moments (e.g., reaching a
major decision). Hormones may play a role in remembering these events.
Some of the most fascinating research in cognitive psychology focuses on the
strategies used in regard to memory. Memory strategies and memory processes are
the subject of the following chapter.
CONCEPT CHECK
1. Define amnesia and name three forms of amnesia.
2. What is Alzheimer’s disease?
3. What is the role of the hippocampus in storing information?
Key Themes
This chapter illustrates some of the key themes noted in Chapter 1.
Applied versus basic research. Basic and applied research can interact. An example is research on Alzheimer’s disease. Presently, the disease is not curable, but is
treatable with drugs and with guidance provided in a structured living environment.
Basic research into the biological structures (e.g., tangles and plaques) and cognitive
functions (e.g., impaired memory) associated with Alzheimer’s may one day help us
better understand and treat the disease.
Biology versus behavioral methods. This chapter shows the interaction of biology with behavior. The hippocampus has become one of the most carefully studied
parts of the brain. Current functional magnetic resonance imaging (fMRI) research
is showing how the hippocampus and other parts of the brain, such as the amygdala
(in the case of emotionally based memories) and the cerebellum (in the case of procedural memories) function to enable us to remember what we need to know. Biological processes have an impact on what we experience, how we behave, and what
we remember.
Structures versus processes. Structure and function are both important to understanding human memory. The Atkinson-Shiffrin model proposed control processes that operate on three structures: a very short-term store, a short-term store,
and a long-term store. The more recent working-memory model proposes how executive function controls and activates portions of long-term memory to provide the
information needed to solve tasks at hand.
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CHAPTER 5 • Memory: Models and Research Methods
Summary
1. What are some of the tasks used for studying
memory, and what do various tasks indicate
about the structure of memory? Among the
many tasks used by cognitive psychologists,
some of the main ones have been tasks assessing
explicit recall of information (e.g., free recall,
serial recall, and cued recall) and tasks assessing
explicit recognition of information. By comparing memory performance on these explicit tasks
with performance on implicit tasks (e.g., wordcompletion tasks), cognitive psychologists have
found evidence of differing memory systems or
processes governing each type of task (e.g., as
shown in studies of amnesics).
2. What has been the prevailing traditional
model for the structure of memory? Memory
is the means by which we draw on our knowledge of the past to use this knowledge in the
present. According to one model, memory is
conceived as involving three stores: a sensory
store is capable of holding relatively limited
amounts of information for very brief periods; a
short-term store is capable of holding small
amounts of information for somewhat longer
periods; and a long-term store is capable of storing large amounts of information virtually indefinitely. Within the sensory store, the iconic
store refers to visual sensory memory.
3. What are some of the main alternative models
for the structure of memory? An alternative
model uses the concept of working memory,
usually defined as being part of long-term memory and also comprising short-term memory.
From this perspective, working memory holds
only the most recently activated portion of
long-term memory. It moves these activated
elements into and out of short-term memory.
A second model is the levels-of-processing
framework, which hypothesizes distinctions in
memory ability based on the degree to which
items are elaborated during encoding.
A third model is the multiple memory systems model, which posits not only a distinction
between procedural memory and declarative
(semantic) memory but also a distinction between semantic and episodic memory.
In addition, psychologists have proposed
other models for the structure of memory.
They include a parallel distributed processing
(PDP; connectionist) model. The PDP model
incorporates the notions of working memory,
semantic memory networks, spreading activation, priming, and parallel processing of
information.
Finally, many psychologists call for a complete change in the conceptualization of
memory, focusing on memory functioning in
the real world. This call leads to a shift in
memory metaphors from the traditional storehouse to the more modern correspondence
metaphor.
4. What have psychologists learned about the
structure of memory by studying exceptional
memory and the physiology of the brain?
Among other findings, studies of mnemonists
have shown the value of imagery in memory
for concrete information. They also have demonstrated the importance of finding or forming
meaningful connections among items to be
remembered. The main forms of amnesia are
anterograde amnesia, retrograde amnesia, and
infantile amnesia. The last form of amnesia is
qualitatively different from the other forms and
occurs in everyone.
Through the study of the memory function
of people with each form of amnesia, it has
been possible to differentiate various aspects of
memory. These include long-term versus temporary forms of memory, procedural versus declarative memory processes, and explicit versus
implicit memory.
Although specific memory traces have not
yet been identified, many of the specific structures involved in memory function have been
located. To date, the subcortical structures involved in memory appear to include the hippocampus, the thalamus, the hypothalamus, and
even the basal ganglia, and the cerebellum. The
cortex also governs much of the long-term storage of declarative knowledge.
The neurotransmitters serotonin and acetylcholine appear to be vital to memory function.
Other physiological chemicals, structures, and
processes also play important roles, although
further investigation is required to identify
these roles.
Media Resources
227
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe two characteristics each of sensory
memory, short-term memory, and long-term
memory.
2. What are double dissociations, and why are they
valuable to understanding the relationship between cognitive function and the brain?
3. Compare and contrast the three-store model of
memory with one of the alternative models of
memory.
4. Critique one of the experiments described in
this chapter (e.g., Sperling’s 1960 experiment
on the iconic store, or Craik and Tulving’s 1975
experiment on the levels-of-processing model).
What problem do you see regarding the interpretation given? How could subsequent research
be designed to enhance the interpretation of the
findings?
5. How would you design an experiment to study
some aspect of implicit memory?
6. Imagine what it would be like to recover from
one of the forms of amnesia. Describe your impressions of and reactions to your newly recovered memory abilities.
7. How would your life be different if you could
greatly enhance your own mnemonic skills in
some way?
Key Terms
Alzheimer’s disease, p. 221
amnesia, p. 218
anterograde amnesia, p. 218
central executive, p. 204
culture-relevant tests, p. 192
episodic buffer, p. 205
episodic memory, p. 209
explicit memory, p. 190
hypermnesia, p. 216
hypothetical constructs, p. 193
iconic store, p. 194
implicit memory, p. 190
infantile amnesia, p. 218
levels-of-processing framework,
p. 200
long-term store, p. 193
memory, p. 187
mnemonist, p. 214
phonological loop, p. 204
prime, p. 212
priming effect, p. 212
recall, p. 187
recognition, p. 187
retrograde amnesia, p. 218
semantic memory, p. 209
sensory store, p. 193
short-term store, p. 193
visuospatial sketchpad, p. 204
working memory, p. 203
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Brain Asymmetry
Memory Span
Partial Report
Absolute Identification
Operation Span
Implicit Learning
Modality Effect
Position Error
Irrelevant Speech
Phonological Similarity
Levels of Processing
6
C
H
A
P
T
Memory Processes
CHAPTER OUTLINE
Encoding and Transfer of Information
Forms of Encoding
Short-Term Storage
Long-Term Storage
Transfer of Information from Short-Term Memory
to Long-Term Memory
Rehearsal
Organization of Information
Retrieval
Retrieval from Short-Term Memory
Parallel or Serial Processing?
Exhaustive or Self-Terminating Processing?
The Winner—a Serial Exhaustive Model—with
Some Qualifications
Retrieval from Long-Term Memory
Intelligence and Retrieval
Processes of Forgetting and Memory
Distortion
Interference Theory
Decay Theory
228
The Constructive Nature of Memory
Autobiographical Memory
Memory Distortions
The Eyewitness Testimony Paradigm
Repressed Memories
The Effect of Context on Memory
Key Themes
Summary
Thinking about Thinking: Analytical,
Creative, and Practical Questions
Key Terms
Media Resources
E
R
CHAPTER 6 • Memory Processes
229
Here are some of the questions we will explore in this chapter:
1. What have cognitive psychologists discovered regarding how we encode information for storing it in
memory?
2. What affects our ability to retrieve information from memory?
3. How does what we know or what we learn affect what we remember?
n BELIEVE IT OR NOT
THERE’S A REASON YOU REMEMBER THOSE ANNOYING SONGS
Having a song or part of a song stuck in your head is
incredibly frustrating. We’ve all had the experience of the
song from a commercial repeatedly running through our
minds, even though we wanted to forget it. But sequence
recall—remembering episodes or information in sequential order (like the notes to a song)—has a special and
useful place in memory. We constantly have to remember
sequences, from the movements involved in signing our
name or making coffee in the morning, to the names of
the exits that come before the motorway turn-off we take to
drive home every day.
The ability to recall these sequences makes many aspects of everyday life possible. As you think about a snippet of song or speech, your brain may repeat a sequence
that strengthens the connections associated with that
phrase. In turn, this increases the likelihood that you will
recall it, which leads to more reinforcement.
You could break this unending cycle of repeated recall
and reinforcement—even though this is a necessary and
normal process for the strengthening and cementing of
memories—by introducing other sequences. Thinking of
another song may allow a competing memory to crowd
out the first one: Find another infectious song and hope
that the cure doesn’t become more annoying than the
original problem.
In this chapter, we will learn more about how we store
and recall information, as well as what makes us forget
that information again.
Researchers John Bransford and Marcia Johnson (1972, p. 722) gave their participants the following procedure to follow. Are you able to recall the steps outlined
in this procedure?
The procedure is actually quite simple. First, you arrange items into different groups. Of
course one pile may be sufficient, depending on how much there is to do. If you have to
go somewhere else due to lack of facilities that is the next step; otherwise, you are pretty
well set. It is important not to overdo things. That is, it is better to do too few things at
once than too many. In the short run this may not seem important but complications
can easily arise. A mistake can be expensive as well. At first, the whole procedure will
seem complicated. Soon, however, it will become just another facet of life. It is difficult
to foresee any end to the necessity for this task in the immediate future, but then, one
can never tell. After the procedure is completed one arranges the materials into different
groups again. Then they can be put into their appropriate places. Eventually they will be
used once more and the whole cycle will then have to be repeated. However, that is part
of life.
How easy or difficult is it for you to remember all the details? Bransford and Johnson’s
participants (and probably you, too) had a great deal of difficulty understanding this
passage and recalling the steps involved. What makes this task so difficult? What are
the mental processes involved in this task?
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CHAPTER 6 • Memory Processes
As mentioned in the previous chapter, cognitive psychologists generally refer to the
main processes of memory as comprising three common operations: encoding, storage, and retrieval. Each one represents a stage in memory processing:
• Encoding refers to how you transform a physical, sensory input into a kind of
representation that can be placed into memory.
• Storage refers to how you retain encoded information in memory.
• Retrieval refers to how you gain access to information stored in memory.
Our emphasis in discussing these processes will be on recall of verbal and pictorial
material. Remember, however, that we have memories of other kinds of stimuli as
well, such as odors (Herz & Engen, 1996; Olsson et al., 2009).
Encoding, storage, and retrieval often are viewed as sequential stages. You first
take in information. Then you hold it for a while. Later you pull it out. However,
the processes interact with each other and are interdependent. For example, you
may have found the Bransford and Johnson procedure difficult to encode, thereby
also making it hard to store and to retrieve the information. However, a verbal label
can facilitate encoding and hence storage and retrieval.
Most people do much better with the passage if given its title, “Washing
Clothes.” Now, read the procedure again. Can you recall the steps described in the
passage? The verbal label, “washing clothes” helps us to encode, and therefore to
remember a passage that otherwise seems incomprehensible.
Encoding and Transfer of Information
Before information can be stored in memory, it first needs to be encoded for storage.
Even if the information is held in our short-term memory, it is not always transferred
to our long-term memory. So in order to remember events and facts over a long
period of time, we need to encode and subsequently transfer them from short-term to
long-term storage. These are the processes we will explore in the forthcoming
section.
Forms of Encoding
We encode our memories to store them. However, do short-term and long-term storage use the same kind of code to store information, or do their codes differ? Let us
have a look at some research to answer this question.
Short-Term Storage
When you encode information for temporary storage and use, what kind of code do
you use? This is what Conrad and colleagues (1964) set out to discover with an
experiment. Participants were visually presented with several series of six letters at
the rate of 0.75 seconds per letter. The letters used in the various lists were B, C,
F, M, N, P, S, T, V, and X. There were no vowels included in order to ensure that
letter combinations did not result in any words or pronounceable combinations that
could be memorized more easily. Immediately after the letters were presented, participants were asked to write down each list of six letters in the order given. What
kinds of errors did participants make? Despite the fact that letters were presented
visually, errors tended to be based on acoustic confusability. In other words, instead
of recalling the letters they were supposed to recall, participants substituted letters
Encoding and Transfer of Information
231
that sounded like the correct letters. Thus, they were likely to confuse F for S, B for
V, P for B, and so on.
Another group of participants simply listened to single letters in a setting that
had noise in the background. They then immediately reported each letter as they
heard it. Participants showed the same pattern of confusability in the listening task
as in the visual memory task (Conrad, 1964). Thus, we seem to encode visually presented letters by how they sound, not by how they look.
The Conrad experiment shows the importance in short-term memory of an
acoustic code rather than a visual code. But the results do not rule out the possibility
that there are other codes. One such code would be a semantic code—one based on
word meaning.
Baddeley (1966) argued that short-term memory relies primarily on an acoustic
rather than a semantic code. He compared recall performance for lists of acoustically
confusable words—such as map, cab, mad, man, and cap—with lists of acoustically
distinct words—such as cow, pit, day, rig, and bun. He found that performance was
much worse for the visual presentation of acoustically similar words. He also compared performance for lists of semantically similar words—such as big, long, large,
wide, and broad—with performance for lists of semantically dissimilar words—such
as old, foul, late, hot, and strong. There was little difference in recall between the
two lists. If performance for the semantically similar words had been much worse,
what would such a finding have meant? It would have indicated that participants
were confused by the semantic similarities and hence were processing the words
semantically. However, performance for the semantically similar words was only
slightly worse than that for the semantically dissimilar words, meaning that semantics
did not matter much for processing.
Subsequent work investigating how information is encoded in short-term memory has shown clear evidence, however, of at least some semantic encoding in shortterm memory (Shulman, 1970; Wickens, Dalezman, & Eggemeier, 1976). Thus,
encoding in short-term memory appears to be primarily acoustic, but there may be
some secondary semantic encoding as well. In addition, we sometimes temporarily
encode information visually as well (Posner, 1969; Posner et al., 1969; Posner &
Keele, 1967). But visual encoding appears to be even more fleeting (about 1.5 seconds). We are more prone to forgetting visual information than acoustic information. Thus, initial encoding is primarily acoustic in nature, but other forms of
encoding may be used under some circumstances. For example, when you remember
a telephone number from long ago, you are more likely to remember how it sounds
when you say it to yourself than to remember a visual image of it.
Long-Term Storage
As mentioned, information stored temporarily in working memory is encoded
primarily in acoustic form. So, when we make errors in retrieving words from shortterm memory, the errors tend to reflect confusions in sound. How is information
encoded into a form that can be transferred into storage and available for subsequent
retrieval?
Most information stored in long-term memory is primarily semantically encoded.
In other words, it is encoded by the meanings of words. Consider some relevant
evidence.
Participants in a research study learned a list of 41 words (Grossman & Eagle,
1970). Five minutes after learning took place, participants were given a recognition
test. Included in the recognition test were distracters—items that appear to be
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CHAPTER 6 • Memory Processes
legitimate choices but that are not correct alternatives. Nine of the distracters
(words that were not in the list of 41 words) were semantically related to words on
the list. Nine were not. The researchers were interested in “false alarm” responses in
which the participants indicated that they had seen the distracters, even though
those words weren’t even on the list. Participants falsely recognized an average of
1.83 of the synonyms but only an average of 1.05 of the unrelated words. This result
indicated a greater likelihood of semantic confusion.
Another way to show semantic encoding is to use sets of semantically related
test words, rather than distracters. Participants learned a list of 60 words that included 15 animals, 15 professions, 15 vegetables, and 15 names of people (Bousfield,
1953). The words were presented in random order. Thus, members of the various
categories were intermixed thoroughly. After participants heard the words, they
were asked to use free recall to reproduce the list in any order they wished. The investigator then analyzed the order of output of the recalled words. Did participants
recall successive words from the same category more frequently than would be expected by chance? Indeed, successive recalls from the same category did occur
much more often than would be expected by chance occurrence. Participants were
remembering words by clustering them into categories.
Levels of processing, discussed in Chapter 5, also influences encoding in longterm memory. When learning lists of words, participants move more information
into long-term memory when using a semantic encoding strategy than when using
a nonsemantic strategy. Interestingly, this advantage is not seen in people with
autism. This finding suggests that, in persons with autism, information may not be
encoded semantically, or at least, not to the same extent as in people who do not
have autism (Toichi & Kamio, 2002). When engaged in semantic processing, people
with autism show less activation in Broca’s area than do healthy participants. This
finding indicates that Broca’s area may be related to the semantic deficits autistic
patients often exhibit (Harris et al., 2006).
Encoding of information in long-term memory is not exclusively semantic.
There also is evidence for visual encoding. Participants in a study received 16 drawings of objects, including four items of clothing, four animals, four vehicles, and four
items of furniture (Frost, 1972). The investigator manipulated not only the semantic
category but also the visual category. The drawings differed in visual orientation.
Four were angled to the left, four angled to the right, four horizontal, and four vertical. Items were presented in random order. Participants were asked to recall them
freely. The order of participants’ responses showed effects of both semantic and
visual categories. These results suggested that participants were encoding visual as
well as semantic information. In fact, people are able to store thousands of images
(Brady et al., 2008).
Functional Magnetic Resonance Imaging (fMRI) studies have found that the
brain areas that are involved in encoding can be, but do not necessarily have to
be, involved in retrieval. With respect to faces, the anterior medial prefrontal cortex
and the right fusiform face area play an important role both in encoding and retrieval, whereas the left fusiform face area contributes mostly to encoding processes.
Both encoding and retrieval of places activate the left parahippocampal place area
(PPA); the left PPA is associated with encoding rather than retrieval. In addition,
medial temporal and prefrontal regions are related to memory processes in general,
no matter what kind of stimulus is used (Prince et al., 2009).
In addition to semantic and visual information, acoustic information can be encoded in long-term memory (Nelson & Rothbart, 1972). Thus, there is considerable
233
Encoding and Transfer of Information
flexibility in the way we store information that we retain for long periods. Those
who seek to know the single correct way we encode information are seeking an
answer to the wrong question. There is no one correct way. A more useful question
involves asking, “In what ways do we encode information in long-term memory?”
From a more psychological perspective, however, the most useful question to ask is,
“When do we encode in which ways?” In other words, under what circumstances do
we use one form of encoding, and under what circumstances do we use another?
These questions are the focus of present and future research.
Transfer of Information from Short-Term Memory
to Long-Term Memory
© Ed Fisher/www.Cartoonbank.com
We encounter two key problems when we transfer information from short-term
memory to long-term memory: interference and decay. When competing information interferes with our storing information, we speak of interference. Imagine you
have watched two crime movies with the same actor. You then try to remember the
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CHAPTER 6 • Memory Processes
story line of one of the movies but mix it up with the second movie. You are
experiencing interference. When we forget facts just because time passes, we speak
of decay. These two concepts will be discussed in more detail later in this chapter.
Given the problems of decay and interference, how do we move information from
short-term memory to long-term memory? The means of moving information depends
on whether the information involves declarative or nondeclarative memory.
Some forms of nondeclarative memory are highly volatile and decay quickly. Examples are priming and habituation. Let’s go back to our movie example and assume
that one of the main protagonists in the movie was Tom Cruise. After the movie,
you overhear a conversation in which the word “cruise” is mentioned. Automatically, Tom Cruise pops into your mind. If you hear the word “cruise” a few days
later, however, Tom Cruise may not be so accessible in your mind, and you may
rather think of a cruise you recently took, or would like to take, in the Caribbean.
Other nondeclarative forms are maintained more readily, particularly as a result of
repeated practice (of procedures) or repeated conditioning (of responses).
Entrance into long-term declarative memory may occur through a variety of
processes. One method of accomplishing this goal is by deliberately attending to
information to comprehend it. Another is by making connections or associations
between the new information and what we already know and understand. We make connections by integrating the new data into our existing schemas of stored information. This
process of integrating new information into stored information is called consolidation.
In humans, the process of consolidating declarative information into memory can continue for many years after the initial experience (Squire, 1986). When you learn about
someone or something, for example, you often integrate new information into your
knowledge a long time after you have acquired that knowledge. For example, you may
have met a friend many years ago and started organizing that knowledge at that
time. But you still acquire new information about that friend—sometimes surprising
information—and continue to integrate this new information into your knowledge base.
Stress generally impairs the memory functioning. However, stress also can help
enhance the consolidation of memory through the release of hormones (Park et al.,
2008; Roozendaal, 2002, 2003). The disruption of consolidation has been studied
effectively in amnesics. Studies have particularly examined people who have suffered
brief forms of amnesia as a consequence of electroconvulsive therapy (ECT; Squire,
1986). For these amnesics, the source of the trauma is clear. Confounding variables
can be minimized. A patient history before the trauma can be obtained, and followup testing and supervision after the trauma are more likely to be available. A range
of studies suggests that during the process of consolidation, our memory is susceptible
to disruption and distortion.
We may use various metamemory strategies to preserve or enhance the integrity
of memories during consolidation (Metcalfe, 2000; Waters & Schneider, 2010).
Metamemory strategies involve reflecting on our own memory processes with a
view to improving our memory. Such strategies are especially important when we
are transferring new information to long-term memory by rehearsing it. Metamemory
strategies are just one component of metacognition, our ability to think about and
control our own processes of thought and ways of enhancing our thinking.
Rehearsal
One technique people use for keeping information active is rehearsal, the repeated
recitation of an item. The effects of such rehearsal are termed practice effects.
Rehearsal may be overt, in which case it is usually aloud and obvious to anyone
watching. Or it may be covert, in which case it is silent and hidden.
Encoding and Transfer of Information
235
Elaborative and Maintenance Rehearsal To move information into long-term
memory, an individual must engage in elaborative rehearsal. In elaborative rehearsal,
the individual somehow elaborates the items to be remembered. Such rehearsal makes
the items either more meaningfully integrated into what the person already knows or
more meaningfully connected to one another and therefore more memorable.
In contrast, consider maintenance rehearsal. In maintenance rehearsal, the individual simply repetitiously rehearses the items to be repeated. Such rehearsal temporarily
maintains information in short-term memory without transferring the information to
long-term memory. Without any kind of elaboration, the information cannot be organized and transferred (Tulving, 1962). This finding is of immediate importance when
you study for an exam. If you want to transfer facts to your long-term memory, you will
need somehow to elaborate on the information and link it to what you already know.
For example, if you meet a new acquaintance, you might encode not just the acquaintance’s name but also other connections you have with the person, such as being members of a particular club or taking a particular course together. It will also be helpful to
use mnemonic techniques like the ones discussed in the next section, but repeating
words over and over again is not enough to achieve effective rehearsal.
The Spacing Effect What is the best way to organize your time for rehearsing
new information? More than a century ago, Hermann Ebbinghaus (1885, cited in
Schacter, 1989a; see also Chapter 1) noticed that the distribution of study (memory
rehearsal) sessions over time affects the consolidation of information in long-term
memory. Much more recently, researchers have offered support for Ebbinghaus’s observations as a result of their studies of people’s recall of foreign language vocabulary,
facts, and names of visual objects (Cepeda, 2009).
Much more recently, researchers have offered support for Ebbinghaus’s observation as a result of their studies of people’s long-term recall of Spanish vocabulary
words the subjects had learned 8 years earlier (Bahrick & Phelps, 1987). People’s
memory for information depends on how they acquire it. Their memories tend to
be good when they use distributed practice, learning in which various sessions are
spaced over time. Their memories for information are not as good when the information is acquired through massed practice, learning in which sessions are crammed
together in a very short space of time. The greater the distribution of learning trials
over time, the more the participants remembered over long periods. To maximize
the effect on long-term recall, the spacing should ideally be distributed over months,
rather than days or weeks. This effect is termed the spacing effect. The research in
this area is used by companies producing consumer products and advertising companies, among others. The goal of these companies is to anchor their products in your
long-term memory so that you will remember them when you are in need of a particular product. The spacing in advertisements is varied to maximize the effect on
your memory (Appleton-Knapp, 2005). That means that a company will not place
ads for the same product on several papers of a given magazine, but rather that they
will place one ad every month in that magazine.
The spacing effect is linked to the process by which memories are consolidated
in long-term memory (Glenberg, 1977, 1979; Leicht & Overton, 1987). That is, the
spacing effect may occur because at each learning session, the context for encoding
may vary. The individuals may use alternative strategies and cues for encoding. They
thereby enrich and elaborate their schemas for the information. The principle of the
spacing effect is important to remember in studying. You will recall information longer, on average, if you distribute your learning of subject matter and you vary the context for encoding. Do not try to cram it all into a short period. Imagine studying for an
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CHAPTER 6 • Memory Processes
Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
4–5%
Light sleep.
Muscle
activity slows
down.
Occasional
muscle
twitching.
45–55%
Breathing
pattern and
heart rate
slows. Slight
decrease
in body
temperature.
4–6%
Deep sleep
begins.
Brain begins
to generate
slow delta
waves.
12–15%
Very deep
sleep.
Rhythmic
breathing.
Limited muscle
activity. Brain
produces delta
waves.
20–25%
Rapid eye
movement.
Brainwaves
speed up and
dreaming
occurs.
Muscles relax
and heart rate
increases.
Breathing is
rapid and
shallow.
Sleep Stages
Wake
REM
first
cycle
second
cycle
third
cycle
fourth
cycle
fifth
cycle
Stage 1
Stage 2
Stage 3
Stage 4
Deep Sleep (SWS)
Dreaming (REM)
Figure 6.1 There are five different sleep stages that differ in their EEG patterns. Dreaming
takes place during stage 5, the so-called REM sleep. REM sleep is particularly important for
memory consolidation.
exam in several short sessions over a 2-week period. You will remember much of the
material. However, if you try to study all the material in just one night, you will remember very little and the memory for this material will decay relatively quickly.
Why would distributing learning trials over days make a difference? One possibility is that information is learned in variable contexts. These diverse contexts help
strengthen and begin to consolidate it. Another possible answer comes from studies
of the influences of sleep on memory.
Sleep and Memory Consolidation Of particular importance to memory is the amount
of rapid eye movement (REM) sleep, a particular stage of sleep (see Figure 6.1)
characterized by dreaming and increased brainwave activity (Karni et al., 1994), a person
receives.
Encoding and Transfer of Information
237
Specifically, disruptions in REM sleep patterns the night after learning reduced
the amount of improvement on a visual discrimination task that occurred relative to
normal sleep. Furthermore, this lack of improvement was not observed for disrupted
stage-three or stage-four sleep patterns (Karni et al., 1994). Other research also
shows better learning with increases in the proportion of REM-stage sleep after exposure to learning situations (Ellenbogen, Payne, & Stickgold, 2006; Smith, 1996).
The positive influence of sleep on memory consolidation is seen across age groups
(Hornung et al., 2007). People who suffer from insomnia, a disorder that deprives
the sufferer of much-needed sleep, have trouble with memory consolidation
(Backhaus et al., 2006). Research suggests that memory processes in the hippocampus are influenced by the production and integration of new cells into the neuronal
network. Prolonged sleep deprivation seems to affect such cell development negatively (Meerlo et al., 2009). These findings highlight the importance of biological
factors in the consolidation of memory. Thus, a good night’s sleep, which includes
plenty of REM-stage sleep, aids in memory consolidation.
Neuroscience and Memory Consolidation Is there something special occurring in
the brain that could explain why REM sleep is so important for memory consolidation? Neuropsychological research on animal learning may offer a tentative answer
to this question. Recall that the hippocampus has been found to be an important
structure for memory. In recording studies of rat hippocampal cells, researchers
have found that cells of the hippocampus that were activated during initial learning
are reactivated during subsequent periods of sleep. It is as if they are replaying the
initial learning episode to achieve consolidation into long-term storage (Scaggs &
McNaughton, 1996; Wilson & McNaughton, 1994). This effect has also been observed in humans. After learning routes within a virtual town, participants slept. Increased hippocampal activity was seen during sleep after the person had learned the
spatial information. In the people with the most hippocampal activation, there was
also an improvement in performance when they needed to recall the routes
(Peigneux et al., 2004). During this increased activity, the hippocampus also shows
extremely low levels of the neurotransmitter acetylcholine. When patients were
given acetylcholine during sleep, they showed impaired memory consolidation, but
only for declarative information. Procedural memory consolidation was not affected
by acetylcholine levels (Gais & Born, 2004).
The hippocampus acts as a rapid learning system (McClelland, McNaughton, &
O’ Reilly, 1995). It temporarily maintains new experiences until they can be appropriately assimilated into the more gradual neocortical representation system of the
brain. Such a complementary system is necessary to allow memory to more accurately represent the structure of the environment. McClelland and his colleagues
have used connectionist models of learning to show that integrating new experiences
too rapidly leads to disruptions in long-term memory systems. Thus, the benefits of
distributed practice seem to occur because we have a relatively rapid learning system
in the hippocampus that becomes activated during sleep. Repeated exposure on subsequent days and repeated reactivation during subsequent periods of sleep help learning. These rapidly learned memories become integrated into our more permanent
long-term memory system.
Reconsolidation is a topic related to consolidation. The process of consolidation
makes memories less likely to undergo either interference or decay. However, after a
memory is called back into consciousness, it may return to a more unstable state. In
this state, the memory that was consolidated may again fall victim to interference or
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CHAPTER 6 • Memory Processes
PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
MEMORY STRATEGIES
You can use these memory strategies to help you study for exams:
1. Study throughout the course rather than cram the night before an exam. This distributes the learning sessions, which allows for consolidation into more permanent memory systems.
2. Link new information to what you already know by rehearsing new information in
meaningful ways. Organize new information to relate it to other coursework or areas
of your life.
3. Use the various mnemonic devices shown in Table 6.1.
How could mnemonic devices be helpful in memorizing the state capitals?
decay. To prevent this loss, a process of reconsolidation takes place. Reconsolidation
has the same effect that consolidation does, but it is completed on previously encoded information. Reconsolidation does not necessarily occur with each memory
we recall but does seem to occur with relatively newly consolidated material (Walker
et al., 2003).
Organization of Information
Stored memories are organized. One way to show how memories are organized is by
measuring subjective organization in free recall. This means that researchers measure
the different ways that individuals organize their memories. Researchers do this
by giving participants a list of unrelated words to recall in any order (free recall).
Participants have multiple trials during which to learn to recall a list of unrelated
words in any order they choose. Remember that if sets of test words can be divided
into categories (e.g., names of fruits or of furniture), participants spontaneously will
cluster their recall output by these categories. They do so even if the order of presentation is random (Bousfield, 1953). Similarly, participants will tend to show consistent patterns of word order in their recall protocols, even if there are no apparent
relations among words in the list (Tulving, 1962). In other words, participants create
their own consistent organization and then group their recall by the subjective units
they create. Although most adults spontaneously tend to cluster items into categories, categorical clustering also may be used intentionally as an aid to memorization.
Mnemonic devices are specific techniques to help you memorize lists of words
(Best, 2003). Essentially, such devices add meaning to otherwise meaningless or arbitrary lists of items. Even music can be used as a mnemonic device when a wellknown or easy melody is used and connected with the material that needs to be
learned. Music can even serve as a retrieval cue. For example, if you want to learn
vocabulary words in a foreign language for body parts, sing those words to yourself in
a melody that you like and know well (see, for example, Moore et al., 2008).
As Table 6.1 shows, a variety of methods—categorical clustering, acronyms, acrostics, interactive imagery among items, pegwords, and the method of loci—can
help you to memorize lists of words and vocabulary items. Although the techniques
described in Table 6.1 are not the only available ones, they are among the most frequently used.
Encoding and Transfer of Information
Table 6.1
239
Mnemonic Devices
Of the many mnemonic devices available, the ones described here rely either on organization of information into
meaningful chunks, such as categorical clustering, acronyms, and acrostics, or on visual images, such as interactive
images, a pegword system, and the method of loci.
Technique
Explanation/Description
Example
Categorical
clustering
Organize a list of items into a
set of categories.
If you needed to remember to buy apples, milk, bagels, grapes,
yogurt, rolls, Swiss cheese, grapefruit, and lettuce, you would be
better able to do so if you tried to memorize the items by categories: fruits—apples, grapes, grapefruit; dairy products—milk,
yogurt, Swiss cheese; breads—bagels, rolls; vegetables—lettuce.
Interactive
images
Create interactive images
that link the isolated words in
a list.
Suppose you have to remember to buy socks, apples, and a pair
of scissors. You might imagine using scissors to cut a sock that has
an apple stuffed in it.
Pegword system
Associate each new word
with a word on a previously
memorized list and form an
interactive image between
the two words.
One such list is from a nursery rhyme: One is a bun. Two is a
shoe. Three is a tree, and so on. To remember that you need to
buy socks, apples, and a pair of scissors, you might imagine an
apple between two buns, a sock stuffed inside a shoe, and a pair
of scissors cutting a tree. When you need to remember the words,
you first recall the numbered images and then recall the words as
you visualize them in the interactive images.
Method of loci
Visualize walking around an
area with distinctive landmarks that you know well,
and then link the various
landmarks to specific items to
be remembered
Mentally walk past each of the distinctive landmarks, depositing
each word to be memorized at one of the landmarks. Visualize an
interactive image between the new word and the landmark. Suppose you have three landmarks on your route to school—a
strange-looking house, a tree, and a baseball diamond. You
might imagine a big sock on top of the house in place of the
chimney, the pair of scissors cutting the tree, and apples replacing
bases on the baseball diamond. When ready to remember the
list, you would take your mental walk and pick up the words you
had linked to each of the landmarks along the walk.
Acronym
Devise a word or expression
in which each of its letters
stands for a certain other
word or concept (e.g., USA,
IQ, and laser)
Suppose that you want to remember the names of the mnemonic
devices described in this chapter. The acronym “IAM PACK”
might prompt you to remember Interactive images, Acronyms,
Method of loci, Pegwords, Acrostics, Categories, and Keywords.
Of course, this technique is more useful if the first letters of the
words to be memorized actually can be formed into a word
phrase, or something close to one, even if the word or phrase is
nonsensical, as in this example.
Acrostic
Form a sentence rather than
a single word to help you
remember the new words
Music students trying to memorize the names of the notes found on
lines of the treble clef (the higher notes; specifically E, G, B, D,
and F above middle C) learn that “Every Good Boy Does Fine.”
Keyword system
Form an interactive image
that links the sound and
meaning of a foreign word
with the sound and meaning
of a familiar word.
Suppose that you needed to learn that the French word for butter is
beurre. First, you would note that beurre sounds something like
“bear.” Next, you would associate the keyword bear with butter
in an image or sentence. For instance, you might visualize a bear
eating a stick of butter. Later, bear would provide a retrieval cue
for beurre.
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CHAPTER 6 • Memory Processes
• In categorical clustering, organize a list of items into a set of categories.
• In interactive images, imagine (as vividly as possible) the objects represented by
words you have to remember as if the objects are interacting with each other in
some active way.
• In the pegword system, associate each word with a word on a previously memorized list and form an interactive image between the two words.
• In the method of loci, visualize walking around an area with distinctive, wellknown landmarks and link the various landmarks to specific items to be
remembered.
• In using acronyms, devise a word or expression in which each of its letters stands
for a certain other word or concept.
• In using acrostics, form a sentence, rather than a single word, to help one
remember new words.
• In using the keyword system, create an interactive image that links the
sound and meaning of a foreign word with the sound and meaning of a familiar
word.
What is the comparative effectiveness of the mnemonic strategies listed in Table
6.1? Henry Roediger (1980) conducted a study in which his participants used
different strategies to memorize material. Table 6.2 shows how effective the different
strategies were.
Henry Roediger’s (1980) study of recall memory involved initial recall of a series
of items compared with recall following brief training in each of several memory
Table 6.2
Mnemonic Devices: Comparative Effectiveness
Free Recall Criterion
Serial Recall Criterion
Average number of items recalled
correctly following training
Average number of items recalled
correctly following training
Number of
correct items
immediately
recalled on
practice list,
prior to
Immediate
training
recall
Recall
following
a 24-hour
delay
Number of
participants
Number of
correct items
immediately
recalled on
practice list,
prior to
Immediate
training
recall
Elaborative rehearsal (verbal)
32
13.2
11.4
6.3
7.0
5.8
1.3
Isolated images of
individual items
25
12.4
13.1
6.8
6.8
4.8
1.0
Interactive imagery 31
(with links from one
item to the next)
13.0
15.6
11.2
7.6
9.6
5.0
Condition
(type of
mnemonic
training)
Recall
following
a 24-hour
delay
Method of loci
29
12.6
15.3
10.6
6.8
13.6
5.8
Pegword system
33
13.1
14.2
8.2
7.7
12.5
4.9
Mean performance —
across conditions
12.9
13.9
8.6
7.2
9.4
3.6
Source: H. L. Roediger (1980), “The Effectiveness of Four Mnemonics in Ordering Recall,” Journal of Experimental Psychology: HLM, 6(5):
558–567. Copyright © 1980, by the American Psychological Association. Adapted with permission.
Encoding and Transfer of Information
241
strategies. For both free recall and serial recall, training in interactive imagery, the
method of loci, and the pegword system was more effective than either elaborative
(verbal) rehearsal or imagery for isolated items. However, the beneficial effects of
training were most pronounced for the serial recall condition. In the free recall condition, imagery of isolated items was modestly more effective than elaborative (verbal) rehearsal, but for serial recall, elaborative (verbal) rehearsal was modestly more
effective than imagery for isolated items.
The relative effectiveness of the methods for encoding is influenced by the kind
of task (free recall versus serial recall) required at the time of retrieval (Roediger,
1980). Thus, when choosing a method for encoding information for subsequent
recall, you should consider the purpose for recalling the information. You should
choose not only strategies that allow for effectively encoding the information (moving
it into long-term memory), but strategies that offer appropriate cues for facilitating
subsequent retrieval when needed. For example, using a strategy for retrieving an alphabetical list of prominent cognitive psychologists would probably be relatively ineffective prior to taking an exam in cognitive psychology. Using a strategy for linking
particular theorists with the key ideas of their theories is likely to be more effective.
The use of mnemonic devices and other techniques for aiding memory involves
metamemory (our understanding and reflection upon our memory and how to improve it). Because most adults spontaneously use categorical clustering, its inclusion
in this list of mnemonic devices is actually just a reminder to use this common memory strategy. In fact, each of us often uses various kinds of reminders—external memory aids—to enhance the likelihood that we will remember important information.
For example, by now you have surely learned the benefits of various external memory aids. These include taking notes during lectures, writing shopping lists for items
to purchase, setting timers and alarms, and even asking other people to help you
remember things. In addition, we can design our environment to help us remember
important information through the use of forcing functions (Norman, 1988). These
are physical constraints that prevent us from acting without at least considering the
key information to be remembered. For example, to ensure that you remember to
take your notebook to class, you might lean the notebook against the door through
which you must pass to go to class.
So-called forcing functions are also used in professional settings, such as hospitals, to change behavior. Patients in emergency rooms sometimes have to be physically restrained, but that restraint also significantly increases their risk of dying. The
computer systems physicians use can force the physicians to re-evaluate their decisions concerning the restraint orders by requiring them to renew the order and eventually blocking computer access if the renewal is not executed (Griffey et al., 2009).
In effect, the physicians are forced to deal with the problem at hand.
Most of the time, we try to improve our retrospective memory—our memory for
the past. At times we also try to improve our prospective memory—memory for things
we need to do or remember in the future. For example, we may need to remember to
call someone, to buy cereal at the supermarket, or to finish a homework assignment
due the next day. We use a number of strategies to improve prospective memory.
Examples are keeping a to-do list, asking someone to remind us to do something,
or tying a string around our finger to remind us that we need to do something. Research suggests that having to do something regularly on a certain day does not necessarily improve prospective memory for doing that thing. However, being
monetarily reinforced for doing the thing does tend to improve prospective memory
(Meacham, 1982; Meacham & Singer, 1977).
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CHAPTER 6 • Memory Processes
Prospective memory, like retrospective memory, is subject to decline as we age.
Over the years, we retain more of our prospective memory than of our retrospective
memory. This retention is likely the result of the use of the external cues and strategies that can be used to bolster prospective memory. In the laboratory, older adults
show a decline in prospective memory; however, outside the laboratory they show
better performance than young adults. This difference may be due to greater reliance
on strategies to aid in remembering as we age (Henry et al., 2004).
CONCEPT CHECK
1. How does encoding differ in the short-term storage and the long-term storage?
2. What is rehearsal?
3. Name three mnemonic devices.
Retrieval
Once we have encoded and stored information in short-term memory, how do we
retrieve it? If we have problems retrieving information, was the information even
stored in the first place?
Retrieval from Short-Term Memory
In one study on memory scanning, Saul Sternberg presented participants with a
short list including from one to six digits (Sternberg, 1966). They were expected to
hold the list in short-term memory. After a brief pause, a test digit was flashed on a
screen. Participants had to say whether this digit appeared in the set that they had
been asked to memorize. Thus, if the list comprised the digits 4, 1, 9, 3, and the digit
9 flashed on the screen, the correct response would be “yes.” If, instead, the test digit
was 7, the correct response would be “no.” The digits that were presented are termed
the positive set. Those that were not presented are termed the negative set. Predictions of the possible results are shown in Figure 6.2.
Are items retrieved all at once (parallel processing) or sequentially (serial processing)? If retrieved serially, the question then arises: Are all items retrieved, regardless of the task (exhaustive retrieval), or does retrieval stop as soon as an item seems
to accomplish the task (self-terminating retrieval)? In the next sections, we examine
parallel and serial processing, and then exhaustive and self-terminating retrieval.
INVESTIGATING COGNITIVE PSYCHOLOGY
Test Your Short-Term Memory
Test your ability to retrieve information from your short-term memory. Try this memory
scanning test that is similar to the S. Sternberg experiment described in the chapter.
Use 10 index cards and write one number on each card (1–10). Have a friend quickly
show you five of the index cards (e.g., 6, 3, 8, 2, 7). Then, have your friend hold up
one of the index cards and ask, “Is this one of the numbers?” Have your friend repeat
this procedure five times. How often were you correct? Now, switch roles and test your
friend’s short-term memory. How do people make decisions such as this one?
243
Response time
Response time
Retrieval
Number of symbols in list
(b) Serial processing
Response time
Response time
Number of symbols in list
(a) Parallel processing
Position of symbols in list
(c) Exhaustive serial processing
Position of symbols in list
(d) Self-terminating serial processing
Figure 6.2 This figure shows the four possible predictions for retrieval from short-term memory of Saul Sternberg’s
experiment. Panel (a) illustrates findings suggestive of parallel processing; (b) illustrates serial processing; (c) shows
exhaustive serial processing; and (d) shows self-terminating serial processing.
Source: Based on S. Sternberg (1966), “High Speed in S. Sternberg’s Short-Term Memory-Scanning Task,” Science, Vol. 153, pp. 652–654.
Copyright © 1966 American Association for the Advancement of Science.
Let’s think about these different options for retrieving memories and see what the
research results say.
Parallel or Serial Processing?
Parallel processing refers to the simultaneous handling of multiple operations. As applied to short-term memory, the items stored in short-term memory would be retrieved all at once, not one at a time. The prediction in Figure 6.2(a) shows what
would happen if parallel processing were the case in the Sternberg memory scanning
task: Response times should be the same, regardless of the size of the positive set.
This is because all comparisons would be done at once.
Serial processing refers to operations being done one after another. In other words,
on the digit-recall task, the digits would be retrieved in succession, rather than all at
once (as in the parallel model). According to the serial model, it should take longer to
retrieve four digits than to retrieve two digits [as shown in Figure 6.2(b)].
Exhaustive or Self-Terminating Processing?
If information processing were serial, there would be two ways in which to gain access to the stimuli: exhaustive or self-terminating processing. Exhaustive serial processing implies that the participant always checks the test digit against all digits in the
positive set, even if a match were found partway through the list.
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CHAPTER 6 • Memory Processes
Exhaustive processing would predict the pattern of data shown in Figure 6.2(c).
Note that positive responses all would take the same amount of time, regardless of
the serial position of a positive test probe. In other words, in an exhaustive search,
you would take the same amount of time to find any digit. Where in the list it was
located would not matter.
Self-terminating serial processing implies that the participant would check the test
digit against only those digits needed to make a response. Consider Figure 6.2(d). It
shows that response time now would increase linearly as a function of where a test
digit was located in the positive set. The later the serial position, the longer is the
response time.
The Winner—a Serial Exhaustive Model—with Some Qualifications
The actual pattern of data was crystal clear. The data looked like those in Figures 6.2(b)
and (c). Response times increased linearly with set size, but they were the same, regardless of serial position. Later, this pattern of data was replicated (Sternberg, 1969).
Moreover, the mean response times for positive and negative responses were essentially the same. This fact further supported the serial exhaustive model. Comparisons
took roughly 38 milliseconds (0.038 seconds) apiece (Sternberg, 1966, 1969).
Although many investigators considered the question of parallel versus serial
processing to have been answered decisively, in fact, a parallel model could account
for the data (Corcoran, 1971). Imagine a horse race that involves parallel processing.
The race is not over until the last horse passes the finish line. Now, suppose we add
more horses to the race. The length of the race, from the start until the last of the
horses crosses the finishing line, is likely to increase. For example, if horses are selected randomly, the slowest horse in an eight-horse race is likely to be slower than
the slowest horse in a four-horse race. That is, with more horses, a wider range of
speeds is more likely. So the entire race will take longer because the race is not complete until the slowest horse crosses the finish line.
Similarly, when applying a parallel model to a retrieval task involving more items,
a wider range of retrieval speeds for the various items is also more likely. The entire
retrieval process is not complete until the last item has been retrieved. Mathematically,
it is impossible to distinguish parallel from serial models unequivocally (Townsend,
1971). Some parallel model always exists that will mimic any serial model in its predictions and vice versa. The two models may not be equally plausible, but they still exist.
Moreover, it appears that which processes individuals use depends in part on the stimuli that are processed (e.g., Naus, 1974; Naus, Glucksberg, & Ornstein, 1972).
Some cognitive psychologists have suggested that we should seek not only to
understand the how of memory processes but also the why of memory processes
(e.g., Bruce, 1991). That is, what functions does memory serve for individual persons
and for humans as a species? To understand the functions of memory, we must study
memory for relatively complex information. We also need to understand the relationships between the information presented and other information available to the
individual, both within the informational context and as a result of prior experience.
Retrieval from Long-Term Memory
It is difficult to separate storage from retrieval phenomena. Participants in one study
were tested on their memory for lists of categorized words (Tulving & Pearlstone,
1966). Participants would hear words within a category together in the list. They
Retrieval
245
Copyright © 2005, with permission from Elsevier.
even would be given the name of the category before the items within it were presented. For example, the participants might hear the category “article of clothing”
followed by the words, “shirt, socks, pants, belt.” Participants then were tested for
their recall.
The recall test was done in one of two ways. In the free recall condition, participants merely recalled as many words as they could in any order they chose. In a
cued recall condition, however, participants were tested category by category. They
were given each category label as a cue. They then were asked to recall as many
words as they could from that category. The critical result was that cued recall was
far better, on average, than free recall. Had the researchers tested only free recall,
they might have concluded that participants had not stored quite so many words.
However, the comparison to the cued recall condition demonstrated that apparent
memory failures were largely a result of retrieval, rather than storage failures.
Categorization dramatically can affect retrieval. Investigators had participants
learn lists of categorized words (Bower et al., 1969). Either the words were presented
in random order or they were presented in the form of a hierarchical tree that
showed the organization of the words. For example, the category “minerals” might
be at the top, followed by the categories of “metals and stones,” and so on. Participants given hierarchical presentation recalled 65% of the words. In contrast, recall
was just 19% by participants given the words in random order.
An interesting study by Khader and colleagues (2005) demonstrated that material that is processed in certain cortical areas during perception also activates those
same areas again during long-term memory recall. Participants learned abstract words
that were connected either with one or two faces or with one or two spatial positions (see Figure 6.3). A few days later in a cued recall task, they were presented
with two words and were asked to decide whether those two words were connected
by a common face or position, with their performance recorded by fMRI. Recall of
Figure 6.3 In the experiment of Khader and colleagues (2005), participants were presented with abstract words like “concept,” which were paired with either one or two spatial
positions or faces.
Source: Reprinted from Neuroimage, 27(4), Khader, P., Burke, M., Bien, S., Ranganath, C., & Roesler, F.
(2005). Content-specific activation during associative long-term memory retrieval, 805–816.
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CHAPTER 6 • Memory Processes
spatial positions activated areas such as the parietal and precentral cortex, and faces
activated areas such as the left prefrontal temporal cortex and the posterior cingulated cortex. Blood oxygen levels increased with the number of associations to be
recalled.
Another problem that arises when studying memory is figuring out why we sometimes have trouble retrieving information. Cognitive psychologists often have difficulty
finding a way to distinguish between availability and accessibility of items. Availability
is the presence of information stored in long-term memory. Accessibility is the degree
to which we can gain access to the available information. Memory performance
depends on the accessibility of the information to be remembered. Ideally, memory
researchers would like to assess the availability of information in memory. Unfortunately, they must settle for assessing the accessibility of such information.
Intelligence and Retrieval
Is there a link between age-related slowing of information processing and (1) initial
encoding and recall of information and (2) long-term retention (Nettelbeck et al.,
1996; see also Bors & Forrin, 1995)? It appears that the relation between inspection
time and intelligence may not be related to learning. In particular, there is a difference between initial recall and actual long-term learning (Nettelbeck et al., 1996).
Initial recall performance is mediated by processing speed. Older, slower participants
showed deficits.
Longer-term retention of new information, preserved in older participants, is
mediated by cognitive processes other than speed of processing. These processes include rehearsal strategies. Thus, speed of information processing may influence initial
performance on recall and inspection time tasks, but speed is not related to longterm learning. Perhaps faster information processing aids participants in performance
aspects of intelligence test tasks, rather than contributing to actual learning and intelligence. Clearly, this area requires more research to determine how informationprocessing speed relates to intelligence.
CONCEPT CHECK
1. How do we retrieve data from short-term memory?
2. Why do we need to make a difference between the availability and the accessibility of
information?
3. Does intelligence influence retrieval?
Processes of Forgetting and Memory Distortion
Why do we so easily and so quickly forget phone numbers we have just looked up or
the names of people whom we have just met? Several theories have been proposed
as to why we forget information stored in working memory. The two most wellknown theories are interference theory and decay theory. Interference occurs when
competing information causes us to forget something; decay occurs when simply the
passage of time causes us to forget.
Processes of Forgetting and Memory Distortion
247
Interference Theory
Percent correct recall
Interference theory refers to the view that forgetting occurs because recall of certain
words interferes with recall of other words. Evidence for interference goes back many
years (Brown, 1958; Peterson & Peterson, 1959). In one study, participants were asked
to recall trigrams (strings of three letters) at intervals of 3, 6, 9, 12, 15, or 18 seconds after
the presentation of the last letter (Peterson & Peterson, 1959). The investigators used
only consonants so that the trigrams would not be easily pronounceable—for example,
“K B F.” Figure 6.4 shows percentages of correct recalls after the various intervals of time.
Why does recall decline so rapidly? Because after the oral presentation of each trigram, participants counted backward by threes from a three-digit number spoken immediately after the trigram. The purpose of having the participants count backward was to
prevent them from rehearsing during the retention interval. This is the time between the
presentation of the last letter and the start of the recall phase of the experimental trial.
Clearly, the trigram is almost completely forgotten after just 18 seconds if participants are not allowed to rehearse it. Moreover, such forgetting also occurs when
words rather than letters are used as the stimuli to be recalled (Murdock, 1961).
So, counting backward interfered with recall from short-term memory, supporting
the interference account of forgetting in short-term memory. At that time, it seemed
surprising that counting backward with numbers would interfere with the recall of
letters. The previous view had been that verbal information would interfere only
with verbal (words) memory. Similarly, it was thought that quantitative (numerical)
information would interfere only with quantitative memory.
At least two kinds of interference figure prominently in psychological theory
and research: retroactive interference and proactive interference. Retroactive interference (or retroactive inhibition) occurs when newly acquired knowledge impedes
the recall of older material. This kind of interference is caused by activity occurring
after we learn something but before we are asked to recall that thing. The interference in the Brown-Peterson task appears to be retroactive because counting backward by threes occurs after learning the trigram. It interferes with our ability to
remember information we learned previously.
100
90
80
70
60
50
40
30
20
10
0
3
6
9
12
15
Retention interval (seconds)
18
Figure 6.4 The percentage of recall of three consonants (a trigram) drops off quickly
if participants are not allowed to rehearse the trigrams.
Source: G. Keppel and B. J. Underwood (1962), “Proactive Inhibition in Short-Term Retention of Single
Items,” Journal of Verbal Learning and Verbal Behavior, 1, pp. 153–161. Reprinted by permission of Elsevier.
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CHAPTER 6 • Memory Processes
Proactive interference (or proactive inhibition) occurs when material that was
learned in the past impedes the learning of new material. In this case, the interfering material occurs before, rather than after, learning of the to-be-remembered
material. If you have studied more than one foreign language, you may have experienced this effect quite intensely. The author studied French at school, and then
started learning Spanish when she entered college. Unfortunately, French words
found their way into her Spanish essays unnoticed, and it took her a while to eliminate those French words from her writing in Spanish (proactive interference).
Later, she studied Italian, and because she had not practiced Spanish in a few years,
when she formulated Spanish sentences in a conversation without much time to
think, there was a good chance a mixture of Italian and Spanish would emerge
(retroactive interference).
Proactive as well as retroactive interference may play a role in short-term memory (Keppel & Underwood, 1962; Makovski & Jiang, 2008). Thus, retroactive interference appears to be important (Reitman, 1971; Shiffrin, 1973; Waugh & Norman,
1965), but not the only factor impeding memory performance.
The amount of proactive interference generally climbs with increases in the
length of time between when the information is presented (and encoded) and when
the information is retrieved (Underwood, 1957). Also as you might expect, proactive
interference increases as the amount of prior—and potentially interfering—learning
increases (Greenberg & Underwood, 1950). Proactive interference generally has stronger effects in older adults than in younger people (Ebert & Anderson, 2009).
Proactive interference seems to be associated with activation in the frontal cortex. In particular, it activates Brodmann area 45 in the left hemisphere (Postle,
Brush, & Nick, 2004). In alcoholic patients, proactive interference is seen to a lesser
degree than in non-alcoholic patients. This finding suggests that the alcoholic patients have difficulty integrating past information with new information. Thus, alcoholic patients may have difficulty binding together unrelated items in a list (De Rosa
& Sullivan, 2003). Taken together, these findings suggest that Brodmann area 45 is
likely involved in the binding of items into meaningful groups. When more information is gathered, an attempt to relate them to one another can occupy much of the
available resources, leaving limited processing ability for new items.
All information does not equally contribute to proactive interference. For instance, if you are learning a list of numbers, your performance in learning the list
will gradually decline as the list continues. If, however, the list switches to words,
your performance will rebound. This enhancement in performance is known as release from proactive interference (Bunting, 2006). The effects of proactive interference
appear to dominate under conditions in which recall is delayed. However, proactive
and retroactive interference now are viewed as complementary phenomena.
Some early psychologists recognized the need to study memory retrieval for connected texts and not just for unconnected strings of digits, words, or nonsense syllables. In one study, participants learned a text and then recalled it (Bartlett, 1932).
British participants learned a North American Indian legend called “The War of the
Ghosts,” which to them was a strange and difficult-to-understand text. Read the legend in Investigating Cognitive Psychology: Bartlett’s Legend and test yourself to see how
much of the legend you can recall.
Participants distorted their recall to render the story more comprehensible
to themselves. In other words, their prior knowledge and expectations had a substantial effect on their recall. Apparently, people bring into a memory task their
already existing schemas, which affect the way in which they recall what they
Processes of Forgetting and Memory Distortion
249
INVESTIGATING COGNITIVE PSYCHOLOGY
Can You Recall Bartlett’s Legend?
Read the following legend and then turn the page so you can not see the story. Now, try to recall the legend in its
entirety by writing down what you remember.
(A) ORIGINAL INDIAN MYTH
The War of the Ghosts
One night two young men from Egulac went down to the
river to hunt seals, and while they were there it became foggy
and calm. Then they heard war-cries, and they thought:
“Maybe this is a war-party.” They escaped to the shore, and
hid behind a log.
Now canoes came up, and they heard the noise of
paddles, and saw one canoe coming up to them. There were
five men in the canoe, and
they said:
“What do you think? We wish to take you along. We
are going up the river to make war on the people.”
One of the young men said, “I have no arrows.”
“Arrows are in the canoe,” they said.
“I will not go along. I might be killed. My relatives do not
know where I have gone. But you,” he said, turning to the
other, “may go with them.”
So one of the young men went, but the other returned
home.
And the warriors went on up the river to a town on the
other side of Kalama. The people came down to the water,
and they began to fight, and many were killed. But presently
the young man heard one of the warriors say: “Quick, let us go
home; that Indian has been hit.” Now he thought: “Oh, they
are ghosts.” He did not feel sick, but they said he had been
shot.
So the canoes went back to Egulac, and the young man
went ashore to his house, and made a fire. And he told
everybody and said: “Behold I accompanied the ghosts, and
we went to fight. Many of our fellows were killed, and many of
those who attacked us were killed. They said I was hit, and I
did not feel sick.”
He told it all, and then he became quiet.
When the sun rose he fell down. Something black came
out of his mouth. His face became contorted. The people
jumped up and cried.
He was dead.
(B) TYPICAL RECALL BY A STUDENT
IN ENGLAND
The War of the Ghosts
Two men from Edulac went fishing. While thus
occupied by the river they heard a noise in the distance.
“It sounds like a cry,” said one, and presently there
appeared some in canoes who invited them to join the party
of their adventure. One of the young men refused to go, on
the ground of family ties, but the other offered to go.
“But there are no arrows,” he said.
“The arrows are in the boat,” was the reply.
He thereupon took his place, while his friend returned
home. The party paddled up the river to Kaloma, and began
to land on the banks of the river. The enemy came rushing
upon them, and some sharp fighting ensued. Presently
someone was injured, and the cry was raised that the enemy
were ghosts.
The party returned down the stream, and the young
man arrived home feeling none the worse for his experience.
The next morning at dawn he endeavored to recount his
adventures. While he was talking something black issued
from his mouth. Suddenly he uttered a cry and fell down. His
friends gathered round him.
But he was dead.
Source: “The War of the Ghosts,” from Remembering: A Study in Experimental and Social Psychology by F. C. Bartlett. Copyright
© 1932 by Cambridge University Press. Reprinted with permission of Cambridge University Press.
learn. Schemas are mental frameworks that represent knowledge in a meaningful
way. The later work using the Brown-Peterson paradigm confirms the notion that
prior knowledge has an enormous effect on memory, sometimes leading to interference or distortion.
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INVESTIGATING COGNITIVE PSYCHOLOGY
The Serial-Position Curve
Get at least two or three friends or family members to help you with this experiment. Tell
them that you are going to read a list of words and as soon as you finish, they are to
write down as many words as they can remember in any order they wish. (Make sure
everyone has paper and a pencil.) Read the following words to them about 1 second
apart: book, peace, window, run, box, harmony, hat, voice, tree, begin, anchor, hollow, floor, area, tomato, concept, arm, rule, lion, hope. After giving them enough time
to write down all of the words they can remember, total their number of recollections in
the following groups of four:
(1) book, peace, window, run;
(2) box, harmony, hat, voice;
(3) tree, begin, anchor, hollow;
(4) floor, area, tomato, concept;
(5) arm, rule, lion, hope.
Most likely, your friends and family will remember more words from groups 1 and 5
than from groups 2, 3, and 4, with group 3 the least recalled group. This exercise demonstrates the serial-position curve. Save the results of this experiment for a demonstration in Chapter 7.
Another method often used for determining the causes of forgetting involves the
serial-position curve. The serial-position curve represents the probability of recall of
a given word, given its serial position (order of presentation) in a list. Suppose that
you are presented with a list of words and are asked to recall them.
The recency effect refers to superior recall of words at and near the end of a list.
The primacy effect refers to superior recall of words at and near the beginning of a
list. As Figure 6.5 shows, both the recency effect and the primacy effect seem to
influence recall. The serial-position curve makes sense in terms of interference
theory. Words at the end of the list are subject to proactive but not to retroactive
interference. Words at the beginning of the list are subject to retroactive but not to
proactive interference. And words in the middle of the list are subject to both types
of interference. Therefore, recall would be expected to be poorest in the middle of
the list. Indeed, it is poorest.
Primacy and recency effects can also be encountered in everyday life. Have you
noticed that when you meet someone and then get to know him or her better, it can
sometimes be very hard to get over your first impressions? This difficulty may be a
INVESTIGATING COGNITIVE PSYCHOLOGY
Primacy and Recency Effects
Say the following list of words once to yourself, and then, immediately try to recall all the
words, in any order, without looking back at them: table, cloud, book, tree, shirt, cat,
light, bench, chalk, flower, watch, bat, rug, soap, pillow. If you are like most people,
you will find that your recall of words is best for items at and near the end of the list.
Your recall will be second best for items near the beginning of the list and poorest for
items in the middle of the list. A typical serial-position curve is shown in Figure 6.5.
Processes of Forgetting and Memory Distortion
251
Proportion correct
1.00
Primacy
Recency
0
1
2
3
4
5 6
7 8
Serial position
9
10 11
Figure 6.5 When asked to recall a list of words, we show superior recall of words close
to the end of a list (the recency effect), pretty good recall of words close to the beginning of
the list (primacy effect), and relatively poor recall of words in the middle of the list.
result of a primacy effect, which leads to your remembering your first impression particularly well. And if you are applying for a job and are doing interviews, you may be
well served by being one of the first or last candidates that are interviewed in the
hope that your interviewers will remember you better and more clearly than the candidates whose turns were in the middle.
Decay Theory
In addition to interference theory, there is another theory for explaining how
we forget information—decay theory. Decay theory asserts that information is forgotten because of the gradual disappearance, rather than displacement, of the
memory trace. Thus, decay theory views the original piece of information as gradually disappearing unless something is done to keep it intact. This view contrasts
with interference theory, in which one or more pieces of information block recall
of another.
Decay theory turns out to be exceedingly difficult to test because under normal
circumstances, preventing participants from rehearsing is difficult. Through rehearsal, participants maintain the to-be-remembered information in memory. Usually
participants know that you are testing their memory. They may try to rehearse the
information or they may even inadvertently rehearse it to perform well during testing.
However, if you do prevent them from rehearsing, the possibility of interference arises.
The task you use to prevent rehearsal may interfere retroactively with the original
memory.
For example, try not to think of white elephants as you read the next two pages.
When instructed not to think about them, you actually find it quite difficult not to.
The difficulty persists even if you try to follow the instructions. Unfortunately, as a
test of decay theory, this experiment is itself a white elephant because preventing
people from rehearsing is so difficult.
Despite these difficulties, it is possible to test decay theory. A research paradigm
called the “recent-probes task” has been developed that does not encourage participants to rehearse the items presented (Berman et al., 2009; Monsell, 1978). It is
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based on the item-recognition task of S. Sternberg (1966) presented earlier in this
chapter. Here is the recent-probes task:
• Participants are shown four target words.
• Next, participants are presented with a probe word.
• Participants decide whether or not the probe word is identical to one of the four
target words.
If the probe word is not the same as the target words but is identical to a target
word from a recent prior set of target words (“recent negative”), then it will take
participants longer to decide that probe word and target words do not match than
if the probe word is completely new.
The response delay, which is usually between 50–100 milliseconds, is a result of
the high familiarity of the probe word. That is, the recent-probes task elicits clear
interference effects. Of interest to researchers is the intertrial interval (the time
between the presentation of one set of target words and subsequent probe), which
can easily be varied. After each set of stimuli, participants have no incentive to
rehearse the target words, so the longer the intertrial interval, the more time passes
and the more are the target words subject to decay in memory. Thus, if there is
memory decay just as a result of time passing by, then recent negative probes in trials
with a longer intertrial interval should not be as interfering of memory performance
as recent negative probes in trials with a shorter intertrial time. So even if both decay and interference contribute to forgetting, it can be argued that interference has
the strongest effect (Berman et al., 2009).
And this is exactly what researchers have found:
• Decay only had a relatively small effect on forgetting in short-term memory.
• Interference accounted for most of the forgetting.
• So even if both decay and interference contribute to forgetting, it can be argued
that interference has the strongest effect (Berman et al., 2009).
To conclude, evidence exists for both interference and decay, at least in shortterm memory. There is some evidence for decay, but the evidence for interference is
much stronger. For now, we can assume that interference accounts for most of the
forgetting in short-term memory. However, the extent to which the interference is
retroactive, proactive, or both is unclear. In addition, interference also affects material in long-term memory, leading to memory distortion.
CONCEPT CHECK
1. Name and define two types of interference.
2. What is the recency effect?
3. What is the difference between interference and decay?
The Constructive Nature of Memory
An important lesson about memory is that memory retrieval is not just reconstructive, involving the use of various strategies (e.g., searching for cues, drawing inferences) for retrieving the original memory traces of our experiences and then
The Constructive Nature of Memory
253
rebuilding the original experiences as a basis for retrieval (see Kolodner, 1983, for
an artificial-intelligence model of reconstructive memory). Rather, in real-life situations, memory is also constructive, in that prior experience affects how we recall
things and what we actually recall from memory (Davis & Loftus, 2007; Grant &
Ceci, 2000; Sutton, 2003). Think back to the Bransford and Johnson (1972) study,
cited at the opening of this chapter. In this study, participants could remember a
passage about washing clothes quite well but only if they realized that it was about
washing clothes.
In a further demonstration of the constructive nature of memory, participants
read an ambiguous passage that could be interpreted meaningfully in two ways
(Bransford & Johnson, 1973). It could be viewed as being either about watching a
peace march from the 40th floor of a building or about a space trip to an inhabited
planet. Participants omitted different details, depending on what they thought the
passage was about. Consider, for example, a sentence mentioning that the atmosphere did not require the wearing of special clothing. Participants were more likely
to remember it when they thought the passage was about a trip into outer space than
when they thought it was about a peace march.
Consider a comparable demonstration in a different domain (Bower, Karlin, &
Dueck, 1975). Investigators showed participants 28 different droodles—nonsense pictures that can be given various interpretations (see also Chapter 10). Half of the
participants in their experiment were given an interpretation by which they could
label what they saw. The other half did not receive an interpretation prompting a
label. Participants in the label group correctly reproduced almost 20% more droodles
than did participants in the control group.
Autobiographical Memory
Autobiographical memory refers to memory of an individual’s history. Autobiographical memory is constructive. One does not remember exactly what has happened. Rather, one remembers one’s construction or reconstruction of what
happened. People’s autobiographical memories are generally quite good. Nevertheless, they are subject to distortions (as will be discussed later). They are differentially
good for different periods of life. Middle-aged adults often remember events from
their youthful and early-adult periods better than they remember events from their
more recent past (Read & Connolly, 2007; Rubin, 1982, 1996).
One way of studying autobiographical memory is through diary studies. In such
studies, individuals, often researchers, keep detailed autobiographies (e.g., Linton,
1982; Wagenaar, 1986). One investigator, for example, kept a diary for a 6-year
period (Linton, 1982). She recorded at least two experiences per day on index
cards. Then, each month she chose two cards at random and tried to recall the
events she had written on the cards as well as the dates of the events. She further
rated each memory for its salience and its emotional content. Surprisingly, her rate
of forgetting of events was linear. It was not curvilinear, as is usually the case. In
other words, a typical memory curve shows substantial forgetting over short time
intervals and then a slowing in the rate of forgetting over longer time intervals.
Linton’s forgetting curve, however, did not show any such pattern. Her rate of forgetting was about the same over the entire 6-year interval. She also found little
relationship between her ratings of the salience and emotionality of memories, on
the one hand, and their memorability, on the other. Thus, she surprised herself in
what she did and did not remember.
CHAPTER 6 • Memory Processes
In another study of autobiographical memory, a researcher attempted to recall
information regarding performances attended at the Metropolitan Opera over a period of 25 years (Sehulster, 1989). A total of 284 performances comprised the data
for the study. The results were more in line with traditional expectations. Operas
seen near the beginning and end of the 25-year period were remembered better
(serial-position effect). Important performances also were better recalled than less
important ones.
Recent work has illustrated the importance of self-esteem in the formation and
recall of autobiographical memory. People with positive self-esteem remember more
positive events, whereas people with negative self-esteem remember more negative
events (Christensen, Wood, & Barrett, 2003). Likewise, depressed people recall
more negative memories than people who are not depressed (Wisco & NolenHoeksema, 2009). When people misremember, they usually tend to be wrong with
regard to minor and marginal aspects, but remember the central characteristics
© Spencer Platt/Getty Images.
254
Events like the attacks of September 11, 2001, are often remembered in flashbulb memories that are
experienced almost as vividly as a movie.
The Constructive Nature of Memory
255
correctly. But if you think about it, this is not so surprising. If we would remember a
large number of small details, those details would likely at some point start to interfere with our memories for important things. So it may be better to concentrate on
what is really important (Bjork et al., 2005; Goldsmith et al., 2005).
An often-studied form of vivid memory is the flashbulb memory—a memory
of an event so powerful that the person remembers the event as vividly as if it
were indelibly preserved on film (Brown & Kulik, 1977). People old enough to
recall the assassination of President John Kennedy may have flashbulb memories
of this event. Some people also have flashbulb memories for the destruction of
the World Trade Center, or momentous events in their personal lives. The emotional intensity of an experience may enhance the likelihood that we will recall
the particular experience (over other experiences) ardently and perhaps accurately
(Bohannon, 1988). A related view is that a memory is most likely to become a
flashbulb memory under three circumstances: The memory trace is important
to the individual, is surprising, and has an emotional effect on the individual
(Conway, 1995).
Some investigators suggest that flashbulb memories may be more vividly recalled
because of their emotional intensity. Other investigators, however, suggest that the
vividness of recall may be the result of the effects of rehearsal. The idea here is that
we frequently retell, or at least silently contemplate, our experiences of these momentous events. Perhaps our retelling also enhances the perceptual intensity of our
recall (Bohannon, 1988). Other findings suggest that flashbulb memories may be
perceptually rich (Neisser & Harsch, 1993). In this view, they may be recalled
with relatively greater confidence in the accuracy of the memories (Weaver, 1993)
but not actually be any more reliable or accurate than any other recollected memory
(Neisser & Harsch, 1993; Weaver, 1993). Suppose flashbulb memories are indeed
more likely to be the subject of conversation or even silent reflection. Then perhaps,
at each retelling of the experience, we reorganize and construct our memories such
that the accuracy of our recall actually diminishes, while the perceived vividness of
recall increases over time.
A study examining the memories of more than 3,000 people of the September 11
attacks on the World Trade Center towers in New York City found that the rate of
forgetting is faster in the first year and then slows down. This change in rate allows
the content to become more stable later on. Furthermore, it seems that emotional
reactions elicited by the flashbulb memories are not as well remembered as nonemotional features, such as where a person was at the time of the attack (Hirst et al.,
2009).
Some interesting effects of flashbulb memory involve the role of emotion. The
more a person is emotionally involved in an event, the better the person’s memory is
for that event. Also, over time, memory for the event degrades (Smith, Bibi, &
Sheard, 2004). In one study, more than 70% of people who were questioned about
the World Trade Center attacks on September 11, 2001, reported seeing the first
plane hit the first tower. However, this footage was not available until the next
day (Pezdek, 2003, 2006). These distortions illustrate the constructive nature of
flashbulb memories. These findings further indicate that flashbulb memories are not
immune to distortion, as once was thought.
Are different memory processes at work for flashbulb memories than for other
kinds of memories? It appears not. Just as for other memories, the factors that influence encoding and retrieval are ones such as elaboration and the frequency of rehearsal (Neisser, 2003; Read & Connolly, 2007).
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n BELIEVE IT OR NOT
CAUGHT
IN THE
PAST!?
Have you ever been haunted by memories from your
past? In a unique case of extraordinary autobiographical
memory, a young woman named A. J. is able to recall the
date and weekday of every day since she was 14 years
old, as well as what she did that day. Conversations with
other people, things she sees, and just about everything
provides a cue for her to retrieve another memory from her
past. She cannot let go of her memories and is caught
thinking about it time and again while trying to live her
life in the present. However, A. J. does not know how she
retrieves her memories; she just “knows” what happened
on any particular day in her life.
Researchers have examined her extraordinary ability
and found that her superior memory is constrained to autobiographical events—she never was a particularly great
student and does not fare well on memory tasks that ask
her to recall word lists, for example. It is hypothesized she
may have a rare neurodevelopmental, frontostriatal disorder that is related to other disorders like autism, schizophrenia, and attention deficit hyperactivity disorder. But
whatever it is that distinguishes A. J. from the rest of us, it
seems like for the foreseeable future she’ll just have to keep
remembering (Parker et al., 2006).
Which parts of the brain are involved in autobiographic memories? It seems that
the medial temporal lobe is crucially involved in the recall of autobiographic memories. People with lesions in this area have trouble recalling memories from their recent past (but not from their more remote past; Kirwan et al., 2008).
Memory Distortions
People have tendencies to distort their memories (Aminoff et al., 2008; Roediger &
McDermott, 2000; Schacter & Curran, 2000; Schnider, 2008). For example, just
saying something has happened to you makes you more likely to think it really happened. This is true whether the event happened or not (Ackil & Zaragoza, 1998).
These distortions tend to occur in seven specific ways, which Schacter (2001) refers
to as the “seven sins of memory.” Here are Schacter’s “seven sins”:
1. Transience. Memory fades quickly. For example, although most people know
that O. J. Simpson was acquitted of criminal charges in the murder of his wife,
they do not remember how they found out about his acquittal. At one time they
could have said, but they no longer can.
2. Absent-mindedness. People sometimes brush their teeth after already having
brushed them or enter a room looking for something only to discover that they
have forgotten what they were seeking.
3. Blocking. People sometimes have something that they know they should remember, but they can’t. It’s as though the information is on the tip of their tongue,
but they cannot retrieve it (see also the explanation of the tip-of-the-tongue
phenomenon in Chapter 4). For example, people may see someone they know,
but the person’s name escapes them; or they may try to think of a synonym for a
word, knowing that there is an obvious synonym, but are unable to recall it.
4. Misattribution. People often cannot remember where they heard what they heard
or read what they read. Sometimes people think they saw things they did not
see or heard things they did not hear. For example, eyewitness testimony is
sometimes clouded by what we think we should have seen, rather than what
we actually saw.
5. Suggestibility. People are susceptible to suggestion, so if it is suggested to them
that they saw something, they may think they remember seeing it. For example,
The Constructive Nature of Memory
257
in one study, when asked whether they had seen a television film of a plane
crashing into an apartment building, many people said they had seen it. There
was no such film.
6. Bias. People often are biased in their recall. For example, people who currently
are experiencing chronic pain in their lives are more likely to remember pain in
the past, whether or not they actually experienced it. People who are not
experiencing such pain are less likely to recall pain in the past, again with little
regard to their actual past experience.
7. Persistence. People sometimes remember things as consequential that, in a broad
context, are inconsequential. For example, someone with many successes but one
notable failure may remember the single failure better than the many successes.
What are some of the specific ways in which memory distortions are studied?
We will consider two research areas next that investigate eyewitness testimony and
repressed memories.
The Eyewitness Testimony Paradigm
A survey of U.S. prosecutors estimated that about 77,000 suspects are arrested each year
after being identified by eyewitnesses (Dolan, 1995). Of the first 180 cases in the
United States in which convicts were exonerated through the use of DNA evidence,
more than three quarters involved eyewitness errors (Wells et al., 2006).
Eyewitness testimony may be the most common source of wrongful convictions
in the United States (Modafferi et al., 2009). Generally, what proportion of eyewitness identifications are mistaken? The answer to that question varies widely (“from
as low as a few percent to greater than 90%”; Wells, 1993, p. 554), but even the
most conservative estimates of this proportion suggest frightening possibilities.
Consider the story of a man named Timothy. In 1986, Timothy was convicted of
brutally murdering a mother and her two young daughters (Dolan, 1995). He was then
sentenced to die, and for 2 years and 4 months, Timothy lived on death row. Although
the physical evidence did not point to Timothy, eyewitness testimony placed him near
the scene of the crime at the time of the murder. Subsequently, it was discovered that a
man who looked like Timothy was a frequent visitor to the neighborhood of the murder victims. Timothy received a second trial and was acquitted.
What Influences the Accuracy of Eyewitness Testimonies? There are serious
potential problems of wrongful conviction when using eyewitness testimony as the
sole, or even the primary, basis for convicting accused people of crimes (Loftus & Ketcham, 1991; Loftus, Miller, & Burns, 1987; Wells & Loftus, 1984). Moreover, eyewitness testimony is often a powerful determinant of whether a jury will convict an
accused person. The effect is particularly pronounced if eyewitnesses appear highly confident of their testimony. This is true even if the eyewitnesses can provide few perceptual details or offer apparently conflicting responses. People sometimes even think they
remember things simply because they have imagined or thought about them (Garry &
Loftus, 1994). It has been estimated that as many as 10,000 people per year may be
convicted wrongfully on the basis of mistaken eyewitness testimony (Cutler & Penrod,
1995; Loftus & Ketcham, 1991). In general, people are remarkably susceptible to mistakes in eyewitness testimony. They are generally prone to imagine that they have seen
things they have not seen (Loftus, 1998).
Some of the strongest evidence for the constructive nature of memory has
been obtained by those who have studied the validity of eyewitness testimony. In a
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These are two slides that were shown to participants in the experiment of Loftus and colleagues (1978).
Although the slides depicting the initial incident had featured a stop sign, participants who had been questioned about a yield sign often remembered having seen that yield sign in the original scene.
Source: From Loftus, E. F., Miller, D. G., & Burns, H. J. (1978). Semantic integration of verbal information
into a visual memory. Journal of Experimental Psychology: Human Learning and Memory, 4, 19–31.
now-classic study, participants saw a series of 30 slides in which a red Datsun drove
down a street, stopped at a stop sign, turned right, and then appeared to knock down
a pedestrian crossing at a crosswalk (Loftus, Miller, & Burns, 1978). Afterwards, participants were asked a series of 20 questions, one of which referred either to correct
information (the stop sign) or incorrect information (a yield sign instead of the stop
sign). In other words, the information in the question given this second group was
inconsistent with what the participants had seen. Later, after engaging in an unrelated activity, all participants were shown two slides and asked which they had
seen. One had a stop sign, the other had a yield sign. Accuracy on this task was
34% better for participants who had received the consistent question (stop sign question) than for participants who had received the inconsistent question (yield sign
question).
Loftus’ eyewitness testimony experiment and other experiments (e.g., Loftus,
1975, 1977) have shown people’s great susceptibility to distortion in eyewitness accounts. This distortion may be due, in part, to phenomena other than just constructive memory. But it does show that we easily can be led to construct a memory that
is different from what really happened. As an example, you might have had a disagreement with a roommate or a friend regarding an experience in which both of
you were in the same place at the same time. But what each of you remembers about
the experience may differ sharply. And both of you may feel that you are truthfully
and accurately recalling what happened.
Questions do not have to be suggestive to influence the accuracy of eyewitness
testimony. Line-ups also can lead to faulty conclusions (Wells, 1993). Eyewitnesses
assume that the perpetrator is in the line-up. This is not always the case, however.
When the perpetrator of a staged crime was not in a line-up, participants were susceptible to naming someone other than the true perpetrator as the perpetrator. In
this way, they believed they were able to recognize someone in the line-up as having
committed the crime. The identities of the nonperpetrators in the line-up also can
affect judgments (Wells, Luus, & Windschitl, 1994). In other words, whether a
given person is identified as a perpetrator can be influenced simply by who the
others are in the line-up. So the choice of the “distracter” individuals is important.
Police may inadvertently affect the likelihood of whether or not an identification
occurs and also whether a false identification is likely to occur.
Confessions also influence the testimony of eyewitnesses. A study by Hasel and
Kassin (2009) had participants view a staged robbery. Afterwards, the participants
were presented with a line-up of suspects and were given the opportunity to identify
The Constructive Nature of Memory
259
the robber (although the actual perpetrator was not among them). Sometime later,
the participants were informed that one of the suspects in the lineup had made a
confession. In all, 61% of those who had made a selection previously changed their
identifications, and 50% of those who had not made an identification went on to
positively identify the confessor. This finding shows what a grave impact a confession has on the identification of a perpetrator.
Likewise, feedback to eyewitnesses affected participants’ testimony. Telling them
that they had identified the perpetrator made them feel more secure in their choice,
whereas the feedback that they had identified a filler person made them back away
from their judgment immediately. This phenomenon is called the post-identification
feedback effect (Wells, 2008; Wright & Skagerberg, 2007).
Eyewitness identification is particularly weak when identifying people of a racial
or ethnic group other than that of the witness (e.g., Bothwell, Brigham, & Malpass,
1989; Brigham & Malpass, 1985; Pezdek, Blandon-Gitlin, & Moore, 2003; Shapiro
& Penrod, 1986). Evidence suggests that this weakness is not a problem remembering stored faces of people from other racial or ethnic groups, but rather, a problem of
accurately encoding their faces (Walker & Tanaka, 2003).
Eyewitness identification and recall are also affected by the witness’s level of
stress. As stress increases, the accuracy of both recall and identification declines
(Deffenbacher et al., 2004; Payne et al., 2002). These findings further call into question the accuracy of eyewitness testimony because most crimes occur in highly stressful situations.
Not everyone views eyewitness testimony with such skepticism, however (e.g.,
see Zaragoza, McCloskey, & Jamis, 1987). It is still not clear whether the information about the original event actually is displaced by, or is simply competing with,
the subsequent misleading information. Some investigators have argued that psychologists need to know a great deal more about the circumstances that impair eyewitness testimony before impugning such testimony before a jury (McKenna,
Treadway, & McCloskey, 1992). At present, the verdict on eyewitness testimony is
still not in.
Although there has been no ultimate verdict yet on eyewitness testimony, it is
certainly important for all involved parties to know the limits of eyewitness statements. Research has shown, however, that although defense attorneys are moderately knowledgeable about the limitations of eyewitness testimony, prosecutors are
less so. Indeed, prosecutors tend to overestimate the reliability of eyewitnesses’ statements and to underestimate the role of eyewitness statements in wrongful convictions (Wise et al., 2009). These results show the importance of educating the
public as well as the parties involved in court proceedings about the fallibility of
eyewitness accounts.
Children as Eyewitnesses Whatever may be the validity of eyewitness testimony
for adults, it clearly is suspect for children (Ceci & Bruck, 1993, 1995). Children’s
recollections are particularly susceptible to distortion. Such distortion is especially
likely when the children are asked leading questions, as in a courtroom setting.
Consider some relevant facts (Ceci & Bruck, 1995). First, the younger the child
is, the less reliable the testimony of that child can be expected to be. In particular,
children of preschool age are much more susceptible to suggestive questioning that
tries to steer them to a certain response than are school-age children or adults.
Second, when a questioner is coercive or even just seems to want a particular
answer, children can be quite susceptible to providing the adult with what he or
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CHAPTER 6 • Memory Processes
IN THE LAB OF ELIZABETH LOFTUS
that this had actually happened to them.
Many participants will freely supply details
Remember the time when you were a kid
about this impossible experience such as
and your family went to Disneyland? The
remembering that they touched the ear or
highlight of your trip was meeting Mickey
tail of Bugs or heard him say, “What’s up
Mouse, who shook your hand?
Doc?”
Remember that? Marketers use autoIt’s one thing to plant a false memory
biographical advertising like this to creof meeting Bugs Bunny, but quite another
ate nostalgia for their products. Several
to plant a false memory of an unpleasant
ELIZABETH LOFTUS
years ago, we wondered whether such
experience with another character. So with
referencing could cause people to beShari Berkowitz and other colleagues, we
lieve that they had experiences as children that are
tried to plant a false belief that people had had an
mentioned in the ads (Braun, Ellis, & Loftus, 2002). In
unpleasant experience with the Pluto character while
one study, participants viewed an ad for Disney that
on a childhood trip to Disney (Berkowitz et al.,
suggested that as a child they shook hands with
2008). We succeeded with about 30% of the subjects.
Mickey Mouse. Later on they answered questions
Moreover, those who were seduced by the suggestion
about their childhood experiences at Disney. Relative
did not want to pay as much for a Pluto souvenir. This
to controls, the ad increased their confidence that as a
finding shows that false beliefs can have consequences
child they personally had shaken hands with Mickey at
that can affect later thoughts and behaviors.
Disney.
A host of other studies show that false memories
A question came up as to whether the ad caused
have repercussions. For example, we have shown that
(1) a revival of a true memory, or (2) the creation of a
by planting false memories for food-related experiences
new, false one. Because some people could have ac(e.g., becoming ill after eating egg salad), we can aftually met Mickey at Disney, both are possibilities. So,
fect how much people like particular foods and how
we conducted another study in which people viewed a
much they actually eat (Bernstein & Loftus 2009).
fake ad for Disney that suggested that they shook hands
These studies are part of a larger program of rewith an impossible character: Bugs Bunny. Of course,
search on the malleability of human memory (Loftus,
Bugs, a Warner Brothers character, would not be
2005). More specifically, they suggest that advertisefound at a Disney resort. Again, relative to controls,
ments or other suggestive influences can tamper with
the ad increased confidence that they personally had
our personal childhood memories. After decades of
shaken hands with the impossible character as a child
watching how easy it is to tamper with memory, I
at Disney. Although this could not possibly have hapcan’t help but wonder how much of our vast store of
pened because Bugs Bunny is a Warner Brothers charmemories reflects genuine experience, and how much
acter and would not be hanging around a
is a product of suggestion, imagination, or some other
Disney property, about 16% of the subjects later said
mental process?
Research on False Memories
she wants to hear. Given the pressures involved in court cases, such forms of questioning may be unfortunately prevalent. For instance, when asked a yes-or-no question, even if they don’t know the answer, most children will give an answer. If the
question has an explicit “I don’t know” option, most children, when they do not
know an answer, will admit they do not know, rather than speculate (Waterman,
Blades, & Spencer, 2001).
Third, children may believe that they recall observing things that others have
said they observed. In other words, they hear a story about something that took
place and then believe that they have observed what allegedly took place. If the
The Constructive Nature of Memory
261
child has some intellectual disability, memory for the event is even more likely to be
distorted, at least when a significant delay has occurred between the time of the
event and the time of recall (Henry & Gudjonsson, 2003).
A study in the United Kingdom has found that, when giving eyewitness testimony, children are also easily impressed by the presence of uniformed officers. When
having to identify an individual in a line-up after having witnessed a staged incident,
children made significantly more mistakes when a uniformed official was present
(Lowenstein et al., 2010). Therefore, perhaps even more so than the eyewitness testimony of adults, the testimony of children must be interpreted with great caution.
Can Eyewitness Testimonies Be Improved? Steps can be taken to enhance eyewitness identification (e.g., using methods to reduce potential biases, to reduce the
pressure to choose a suspect from a limited set of options, and to ensure that each
member of an array of suspects fits the description given by the eyewitness, yet offers
diversity in other ways; described in Wells, 1993). Moreover, suggestive interviews
can cause biases in memory (Melnyk & Bruck, 2004). This problem is especially
likely to occur when these interviews take place close in time to the actual event.
After a crime, witnesses are generally interviewed as soon as possible. Therefore,
steps must be taken to ensure that the questions asked of witnesses are not leading
questions, especially when the witness is a child. This caution can decrease the likelihood of distortion of memory.
Gary Wells (2006) made several suggestions to improve identification accuracy in
line-ups. These suggestions include presenting only one suspect per line-up so that witnesses do not feel like they have to decide between several people they saw; making
sure that all people in the line-up are reasonably similar to each other to decrease the
chance that somebody is identified mistakenly, just because he or she happens to share
one characteristic with the suspected perpetrator that no one else in the line-up
shares; and cautioning witnesses that the suspect may not be in the line-up at all.
In addition, some psychologists (e.g., Loftus, 1993a, 1993b) and many defense
attorneys believe that jurors should be advised that the degree to which the eyewitness feels confident of her or his identification does not necessarily correspond to the
degree to which the eyewitness is actually accurate in her or his identification of the
defendant as being the culprit. At the same time, some psychologists (e.g., Egeth,
1993; Yuille, 1993) and many prosecutors believe that the existing evidence, based
largely on simulated eyewitness studies rather than on actual eyewitness accounts, is
not strong enough to risk attacking the credibility of eyewitness testimony when
such testimony might send a true criminal to prison, preventing the person from
committing further crimes.
Repressed Memories
Might you have been exposed to a traumatic event as a child but have been so traumatized by this event that you now cannot remember it? Some psychotherapists
have begun using hypnosis and related techniques to elicit from people what are alleged to be repressed memories. Repressed memories are memories that are alleged to
have been pushed down into unconsciousness because of the distress they cause.
Such memories, according to the view of psychologists who believe in their existence, are very inaccessible, but they can be dredged out (Briere & Conte, 1993).
However, although people may be able to forget terrible events that happened to
them, there is only dubious support for the notion that clients in psychotherapy often are unaware of their having been abused as a child (Loftus, 1996).
CHAPTER 6 • Memory Processes
Published in The New Yorker 12/1/1997 by Frank Cotham/www.Cartoonbank.com
262
Do repressed memories actually exist? Many psychologists strongly doubt their existence (Ceci & Loftus, 1994; Pennebaker & Memon, 1996; Roediger & McDermott,
1995, 2000; Rofe, 2008). Others are at least highly skeptical (Bowers & Farvolden,
1996; Brenneis, 2000). There are many reasons for this skepticism, which are provided in the following section. First, some therapists may inadvertently plant ideas in
their clients’ heads. In this way, they may inadvertently create false memories of
events that never took place. Indeed, creating false memories is relatively easy, even
in people with no particular psychological problems. Such memories can be implanted
by using ordinary, nonemotional stimuli (see below; Roediger & McDermott, 1995).
Second, showing that implanted memories are false is often extremely hard to
do. Reported incidents often end up, as in the case of childhood sexual abuse,
merely pitting one person’s word against another (Schooler, 1994). At the present
time, no compelling evidence points to the existence of such memories. But psychologists also have not reached the point where their existence can be ruled out definitively. Therefore, no clear conclusion can be reached at this time.
The Roediger-McDermott (1995) paradigm, which is adapted from the work of
Deese (1959), is able to show the effects of memory distortion in the laboratory.
Participants receive a list of 15 words strongly associated with a critical but
The Constructive Nature of Memory
263
nonpresented word. For example, the participants might receive 15 words strongly related to the word sleep but never receive the word sleep. The recognition rate for the
nonpresented word (in this case, sleep) was comparable to that for presented words.
This result has been replicated multiple times (McDermott, 1996; Schacter, Verfaellie,
& Pradere, 1996; Sugrue & Hayne, 2006). Even when shorter lists were used, there was
an increased level of false recognition for nonpresented items. In one experiment, lists
as short as three items revealed this effect, although to a lesser degree (Coane et al.,
2007). Embedding the list in a story can increase this effect in young children. This
strategy strengthens the shared context and increases the probability of a participant’s
falsely recognizing the nonpresented word (Dewhurst, Pursglove, & Lewis, 2007).
Why are people so weak in distinguishing what they have heard from what they
have not heard? One possibility is a source-monitoring error, which occurs when a person attributes a memory derived from one source to another source. People frequently
have difficulties in source monitoring, or figuring out the origins of a memory. They
may believe they read an article in a prestigious newspaper, such as The New York
Times, when in fact they saw it in a tabloid on a supermarket shelf while waiting to
check out. When people hear a list of words not containing a word that is highly associated with the other words, they may believe that their recall of that central word is
from the list rather than from their minds (Foley et al., 2006; Johnson, 1996, 2002).
Another possible explanation of this increased false recognition is spreading activation. In spreading activation, every time an item is studied, you think of the items
related to that item. Imagine a metaphorical spider web with a word in the middle.
Branching out from that word are all the words relating to that word. Of course there
will be individual differences in the construction of these webs, but there will also be a
lot of overlap. For instance, when you read the word nap, words like sleep, bed, and cat
may be activated in your mind. In this way, activation branches out from the original
word nap. If you see 15 words, all of which activate the word sleep, it is likely that, via
a source-monitoring error, you may think you had been presented the word sleep.
Some recent work supports the spreading-activation theory of errors in this paradigm
(Dodd & MacLeod, 2004; Hancock et al., 2003; Roediger, Balota, & Watson, 2001).
This theory is not, however, universally accepted (Meade et al., 2007.
The Effect of Context on Memory
A number of factors, such as emotions, moods, states of consciousness, schemas, and
other features of our internal context, clearly affect memory retrieval. As studies of
constructive memory show, our cognitive contexts for memory clearly influence our
memory processes of encoding, storing, and retrieving information. Studies of expertise also show how existing schemas (frameworks for representing knowledge, see
also Chapter 8) may provide a cognitive context for encoding, storing, and retrieving new information. Specifically, experts generally have more elaborated schemas
than do novices in regard to their areas of expertise (e.g., Chase & Simon, 1973;
Frensch & Sternberg, 1989). These schemas provide a cognitive context in which
the experts can operate. The use of schemas makes integration and organization relatively easy. They fill in gaps when provided with partial or even distorted information and visualize concrete aspects of verbal information. They also can implement
appropriate metacognitive strategies for organizing and rehearsing new information.
Clearly, expertise enhances our confidence in our recollected memories.
Our moods and states of consciousness also may provide a context for encoding
that affects later retrieval of semantic memories. Thus, when we encode semantic
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CHAPTER 6 • Memory Processes
information during a particular mood or state of consciousness, we may more readily
retrieve that information when in the same state again (Baddeley, 1989; Bower,
1983). Interestingly, an Australian study has found that weather-induced negative
mood improves people’s memory for everyday scenes (like a scene in a shopping
mall; Forgas et al., 2009).
How does state of consciousness affect memory? Something that is encoded when
we are influenced by alcohol or other drugs may be retrieved more readily while under
those same influences again (Eich, 1980, 1995). On the whole, however, the “main
effect” of alcohol and many drugs is stronger than the interaction. In other words, the
depressing effect of alcohol and many drugs on memory is greater than the facilitating
effect of recalling something in the same drugged state as when one encoded it.
Some investigators have suggested that persons in a depressed mood can more
readily retrieve memories of previous sad experiences, which may further the continuation of the depression (Baddeley, 1989; see also Wisco & Nolen-Hoeksema, 2009). If
psychologists or others can intervene to prevent the continuation of this vicious cycle,
the person may begin to feel happier. As a result, other happy memories may be more
easily retrieved, thus further relieving the depression, and so on. Perhaps the folkwisdom advice to “think happy thoughts” is not entirely unfounded. In fact, under laboratory conditions, participants seem more accurately to recall items that have pleasant
associations than they recall items that have unpleasant or neutral associations (Matlin
& Underhill, 1979; Monnier & Syssau, 2008). Interestingly, people suffering from depression tend to have deficits in forming and recalling memories (Bearden et al., 2006).
Even our external contexts may affect our ability to recall information. We appear to be better able to recall information when we are in the same physical context as the one in which we learned the material (Godden & Baddeley, 1975). In
one experiment, 16 underwater divers were asked to learn a list of 40 unrelated
words. Learning occurred either while the divers were on shore or while they were
20 feet beneath the sea. Later, they were asked to recall the words when either in
the same environment as where they had learned them or in the other environment.
Recall was better when it occurred in the same place as did the learning.
Even infants demonstrate context effects on memory. Consider an operantconditioning experiment in which the infants could make a crib mobile move in interesting ways by kicking it. Three-month-old infants (Butler & Rovee-Collier, 1989) and
6-month-old infants (Borovsky & Rovee-Collier, 1990) were given an opportunity to
kick a distinctive crib mobile in the same context (i.e., surrounded by a distinctive bumper lining the periphery of the crib) in which they first learned to kick it or in a different
context. They kicked more strongly in the same context. The infants showed much less
kicking when in a different context or when presented with a different mobile.
From these results, such learning seems highly context dependent. However, in
one set of studies, 3-month-old infants (Rovee-Collier & DuFault, 1991) and
6-month-old infants (Amabile & Rovee-Collier, 1991) were offered operant conditioning experiences in multiple contexts for kicking a distinctive mobile. They were
soon thereafter placed in a novel context. It was unlike any of the contexts for conditioning. The infants retained the memory. They kicked the mobile at high rates
in the novel context. Thus, when information is encoded in various contexts, the
information also seems to be retrieved more readily in various contexts. This effect
occurs at least when there is minimal delay between the conditioning contexts and
the novel context. However, consider what happened when the novel context
occurred after a long delay. The infants did not show increased kicking.
The Constructive Nature of Memory
265
Nevertheless, they still showed context-dependent memory for kicking in the familiar contexts (Amabile & Rovee-Collier, 1991).
All of the preceding context effects may be viewed as an interaction between
the context for encoding and the context for retrieval of encoded information. The
results of various experiments on retrieval suggest that how items are encoded has a
strong effect both on how, and on how well, items are retrieved. This relationship is
called encoding specificity—what is recalled depends on what is encoded (Tulving &
Thomson, 1973). Consider a rather dramatic example of encoding specificity. We
know that recognition memory is virtually always better than recall. For example,
recognizing a word that you have learned is easier than recalling it. After all, in recognition you have only to say whether you have seen the word. In recall, you have to
generate the word and then mentally confirm whether it appeared on the list.
In one experiment, Watkins and Tulving (1975) had participants learn a list of
24 paired associates, such as ground-cold and crust-cake.
• Participants were instructed to learn to associate each response (such as cold)
with its stimulus word (such as ground).
• After participants had studied the word pairs, they were given an irrelevant task.
• Then they were given a recognition test with distracters.
• Participants were asked simply to circle the words they had seen previously.
Participants recognized an average of 60% of the words from the list. Then, participants were provided with the 24 stimulus words. They were asked to recall the
responses. Their cued recall was 73%. Thus, recall was better than recognition.
Why? According to the encoding-specificity hypothesis, the stimulus was a better
cue for the word than the word itself. The reason was that the words had been
learned as paired associates.
As mentioned in Chapter 5, the link between encoding and retrieval also may
explain the self-reference effect (Greenwald & Banaji, 1989). Specifically, the main
cause of the self-reference effect is not due to unique properties of self-referent
cues. Rather, it is due to a more general principle of encoding and retrieval: When
individuals generate their own cues for retrieval, they are much more potent than
when other individuals do so.
Other researchers have confirmed the importance of making cues meaningful to
the individual to enhance memory. For example, consider what happened when participants made up their own retrieval cues. They were able to remember, almost without
errors, lists of 500 and 600 words (Mantyla, 1986). For each word on a list, participants
were asked to generate another word (the cue) that to them was an appropriate description or property of the target word. Later, they were given a list of their cue words.
They were asked to recall the target word. Cues were most helpful when they were
both compatible with the target word and distinctive, in that they would not tend to
generate a large number of related words. For example, if you are given the word coat,
then jacket might be both compatible and distinctive as a cue. However, suppose you
came up with the word wool as a cue. That cue might make you think of a number of
words, such as fabric and sheep, which are not the target word.
To summarize, retrieval interacts strongly with encoding. Suppose you are studying
for a test and want to recall well at the time of testing. Organize the information you
are studying in a way that appropriately matches the way in which you will be expected
to recall it. Similarly, you will recall information better if the level of processing for
encoding matches the level of processing for retrieval (Moscovitch & Craik, 1976).
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CHAPTER 6 • Memory Processes
CONCEPT CHECK
1. What is autobiographical memory?
2. In what specific ways do memory distortions occur?
3. Do you think eyewitness accounts should be allowed in court?
4. What are repressed memories?
5. How does the context influence encoding and retrieval of information?
Key Themes
This chapter illustrates several of the key themes first presented in Chapter 1.
Rationalism versus empiricism. To what extent should courts rely on empirical
evidence from psychological research to guide what they do? To what extent should
the credibility of witnesses be determined by rational considerations (e.g., were they
at the scene of a crime, or are they known to be trustworthy) and to what extent by
empirical considerations revealed by psychological research (e.g., being at the scene
of a crime does not guarantee credible testimony, and people’s judgments of trustworthiness are often incorrect)? Court systems often work on the basis of rational
considerations—of what should be. Psychological research reveals what is.
Domain generality versus domain specificity. Mnemonics discussed in this
chapter work better in certain domains than they do in others. For example, you
may be able to devise mnemonics better if you are highly familiar with a domain,
such as was the case for the long-distance runner studied by Chase, Ericsson, and
Faloon (discussed in Chapter 5). In general, the more knowledge you have about a
domain, the easier it will be to chunk information in that domain.
Validity of causal inferences versus ecological validity. Some researchers, such
as Mahzarin Banaji and Robert Crowder, have argued that laboratory research yields
findings that maximize not only experimental control but also ecological validity.
Ulric Neisser has disagreed, suggesting that if one wishes to study everyday memory,
one must study it in everyday settings. Ultimately, the two kinds of research together
are likely to maximize our understanding of memory phenomena. Typically, there is
no one right way to do research. Rather, we learn the most when we use a variety of
methods that converge on a set of common findings.
Summary
1. What have cognitive psychologists discovered
regarding how we encode information for storing it in memory? Encoding of information in
short-term memory appears to be largely, although not exclusively, acoustic in form. Information in short-term memory is susceptible to
acoustic confusability—that is, errors based on
sounds of words. But there is some visual and
semantic encoding of information in short-term
memory. Information in long-term memory appears to be encoded primarily in a semantic
form. Thus, confusions tend to be in terms of
meanings rather than in terms of the sounds of
words. In addition, some evidence points to the
existence of visual encoding, as well as of acoustic encoding, in long-term storage.
Thinking about Thinking
Transfer of information into long-term
storage may be facilitated by several factors:
1. rehearsal of the information, particularly if
the information is elaborated meaningfully;
2. organization, such as categorization of the
information;
3. the use of mnemonic devices;
4. the use of external memory aids, such as
writing lists or taking notes;
5. knowledge acquisition through distributed
practice across various study sessions, rather
than through massed practice.
However, the distribution of time during any
given study session does not seem to affect
transfer into long-term memory. The effects of
distributed practice may be due to a
hippocampal-based mechanism that results in
rapid encoding of new information to be integrated with existing memory systems over time,
perhaps during sleep.
2. What affects our ability to retrieve information from memory? Studying retrieval from
long-term memory is difficult due to problems
of differentiating retrieval from other memory
processes.
It also is difficult to differentiate accessibility from availability. Retrieval of information
from short-term memory appears to be in the
form of serial exhaustive processing. This implies that a person always sequentially checks
all information on a list. Nevertheless, some
data may be interpreted as allowing for the possibility of self-terminating serial processing and
even of parallel processing.
267
3. How does what we know or what we learn
affect what we remember? Two of the main
theories of forgetting in short-term memory are
decay theory and interference theory. Interference theory distinguishes between retroactive
interference and proactive interference. Assessing the effects of decay, while ruling out both
interference and rehearsal effects, is much
harder. However, some evidence of distinctive
decay effects has been found.
Interference also seems to influence longterm memory, at least during the period of consolidation. This period may continue for several
years after the initial memorable experience.
Memory appears to be not only reconstructive—a reproduction of what was learned, based
on recalled data and on inferences from only
those data. It is also constructive—influenced
by attitudes, subsequently acquired information,
and schemas based on past knowledge. As shown
by the effects of existing schemas on the construction of memory, schemas affect memory processes. However, so do other internal contextual
factors, such as emotional intensity of a memorable experience, mood, and even state of consciousness. In addition, environmental context
cues during encoding seem to affect later retrieval. Encoding specificity refers to the fact
that what is recalled depends largely on what is
encoded. How information is encoded at the
time of learning will greatly affect how it is later
recalled.
One of the most effective means of enhancing recall is for the individual to generate
meaningful cues for subsequent retrieval.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. In what forms do we encode information for
brief memory storage versus long-term memory
storage?
2. What is the evidence for encoding specificity?
Cite at least three sources of supporting
evidence.
3. What is the main difference between two of the
proposed mechanisms by which we forget
information?
4. Compare and contrast some of the views regarding flashbulb memory.
5. Suppose that you are an attorney defending a
client who is being prosecuted solely on the
basis of eyewitness testimony. How could you
demonstrate to members of the jury the frailty of
eyewitness testimony?
6. Use the chapter-opening example from Bransford and Johnson as an illustration to make up a
description of a common procedure without
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CHAPTER 6 • Memory Processes
labeling the procedure (e.g., baking chocolate
chip cookies or changing a tire). Try having
someone read your description and then recall
the procedure.
7. Make a list of 10 or more unrelated items you
need to memorize. Choose one of the mnemonic devices mentioned in this chapter, and
describe how you would apply the device to
memorizing the list of items. Be specific.
8. What are three things you learned about memory that can help you to learn new information
and effectively recall the information over the
long term?
Key Terms
accessibility, p. 246
autobiographical memory, p. 253
availability, p. 246
consolidation, p. 234
constructive, p. 253
decay, p. 234
decay theory, p. 251
distributed practice, p. 235
encoding, p. 230
encoding specificity, p. 265
flashbulb memory, p. 255
interference, p. 233
interference theory, p. 247
massed practice, p. 235
metacognition, p. 234
metamemory, p. 234
mnemonic devices, p. 238
primacy effect, p. 250
proactive interference, p. 248
recency effect, p. 250
reconstructive, p. 252
rehearsal, p. 234
retrieval (memory), p. 230
retroactive interference, p. 247
schemas, p. 249
serial-position curve, p. 250
spacing effect, p. 235
storage (memory), p. 230
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Brown-Peterson
False Memory
Serial Position
Sternberg Research
Von Restorff Effect
Encoding Specificity
Forgot It All Along
Remember/Know
7
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The Landscape of Memory: Mental
Images, Maps, and Propositions
CHAPTER OUTLINE
Mental Representation of Knowledge
Communicating Knowledge: Pictures
versus Words
Pictures in Your Mind: Mental Imagery
Dual-Code Theory: Images and Symbols
Storing Knowledge as Abstract Concepts:
Propositional Theory
What Is a Proposition?
Using Propositions
Do Propositional Theory and Imagery Hold
Up to Their Promises?
Limitations of Mental Images
Limitations of Propositional Theory
Mental Manipulations of Images
Principles of Visual Imagery
Neuroscience and Functional Equivalence
Mental Rotations
How Does Mental Rotation Work?
Intelligence and Mental Rotation
Neuroscience and Mental Rotation
Gender and Mental Rotation
Zooming in on Mental Images: Image Scaling
Examining Objects: Image Scanning
Representational Neglect
Synthesizing Images and Propositions
Do Experimenters’ Expectations Influence
Experiment Outcomes?
Johnson-Laird’s Mental Models
Neuroscience: Evidence for Multiple Codes
Left Brain or Right Brain: Where Is
Information Manipulated?
Two Kinds of Images: Visual
versus Spatial
Spatial Cognition and Cognitive Maps
Of Rats, Bees, Pigeons, and Humans
Rules of Thumb for Using Our Mental Maps:
Heuristics
Creating Maps from What You Hear:
Text Maps
Key Themes
Summary
Thinking about Thinking: Analytical,
Creative, and Practical Questions
Key Terms
Media Resources
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Here are some of the questions we will explore in this chapter:
1.
2.
3.
4.
What are some of the major hypotheses regarding how knowledge is represented in the mind?
What are some of the characteristics of mental imagery?
How does knowledge representation benefit from both images and propositions?
How may conceptual knowledge and expectancies influence the way we use images?
n BELIEVE IT OR NOT
CITY MAPS
OF
MUSIC
FOR THE
BLIND
How can a person who is blind find his or her way around
in a new city? Well, not too far in the future they may be
able to hear their way around by means of a translation of
the landscape into music. Researchers are developing a
handheld device that helps blind persons navigate their
environment with their ears (Cronly-Dillon et al., 2000).
Just like a musical score is made up of black dots in a
particular spatial distribution and are then transformed into
music by a musician, the pixels in a digital image can be
transformed into music as well. Listeners explore the musical landscape and create a mental image of what they
see. The picture is read from the left to the right; a horizontal line is played as one continuous note, a vertical line is
played as a fast chord of many notes, and a diagonal
line from the top left to the bottom right can be heard as a
descending scale. Listeners can scan an entire scene or
zoom in to see the details of an object. The resulting music
sounds a little like modern music. However, this only
works for people who were once able to see because
they once developed the ability to create threedimensional mental images.
For example, in one study, blind subjects were able
to distinguish trees, different buildings (like Victorian or
modern houses and churches), or various types of
cars. The blind subjects communicated their mental
images to the researchers by drawing. In Figure 7.1,
you can see the original images of two cars, processed
images that were analyzed by the blind subjects, and the
pictures of the mental images they drew.
In this chapter, we will explore the representation of
knowledge in our minds—in words as well as in images.
Figure 7.1 How People Who Are Blind Form Mental Images.
Source: Cronly-Dillon, J., Persaud, K. C., & Blore, R. (2000). Blind subjects construct conscious
mental images of visual scenes encoded in musical form. Proceedings of the Royal Society B:
Biological Sciences, 267, 2231–2238.
Mental Representation of Knowledge
271
Look carefully at the photos depicted in Figure 7.2. Now cover the photos and
describe to yourself what two of these people look like and sound like. Clearly,
none of these people can truly exist in a physical form inside your mind. How are
you able to imagine and describe them? You must have stored in your mind some
form of mental representation, something that stands for these people-of what you
know about them.
What you use to recall these celebrities is more generally called knowledge representation, the form for what you know in your mind about things, ideas, events,
and so on, in the outside world.
This chapter explores how knowledge is stored and represented in our minds:
• First, we consider what representations are and in what form they can be stored.
• Second, we will look at theories that describe knowledge representation and
suggest that we store our knowledge in images, symbols, or propositions.
• Third, we look more closely at images in our mind. How can we rotate or scan
them; in short, how can we manipulate mental images?
• Fourth, we examine whether separate theories regarding images and propositions
can be combined as one approach.
• Last, we look at mental maps.
Mental Representation of Knowledge
Ideally, cognitive psychologists would love to observe directly how each of us represents knowledge. It would be as if we could take a videotape or a series of snapshots
of ongoing representations of knowledge in the human mind. Unfortunately, direct
empirical methods for observing knowledge representations are not available at present. Also, such methods are unlikely to be available in the immediate future. When
direct empirical methods are unavailable, several alternative methods remain. We
can ask people to describe their own knowledge representations and knowledgerepresentation processes: What do they see in their minds when they think of the
Statue of Liberty, for example? Unfortunately, none of us has conscious access to
our own knowledge-representation processes and self-reported information about
these processes is highly unreliable (Pinker, 1985). Therefore, an introspectionist
approach goes only so far.
Another possibility for observing how we represent knowledge in our minds is
the rationalist approach. In this approach, we try to deduce logically how people
represent knowledge. For centuries, philosophers have done exactly that. In classic
epistemology—the study of the nature, origins, and limits of human knowledge—
philosophers distinguished between two kinds of knowledge structures. The first
type of knowledge structure is declarative knowledge. Declarative knowledge refers
to facts that can be stated, such as the date of your birth, the name of your best
friend, or the way a rabbit looks. Procedural knowledge refers to knowledge of procedures that can be implemented. Examples are the steps involved in tying your
shoelaces, adding a column of numbers, or driving a car. The distinction is between
knowing that and knowing how (Ryle, 1949). These concepts will be used later in the
chapter.
There are two main sources of empirical data on knowledge representation: standard laboratory experiments and neuropsychological studies. In experimental work,
researchers indirectly study knowledge representation because they cannot look
AP Photo/Matt Rourke
© Pictorial Press Ltd/ Alamy
© AP Images
CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Steve Granitz/WireImage
272
Figure 7.2 Mental Representations.
Look at each of these photos carefully. Next, close your eyes, and picture two of the people represented—people
whom you recognize from reports in the media. Without looking again at the photos, mentally compare the appearances of the two people you have chosen. To compare the people, you need to have a mental representation of them
in your mind.
Mental Representation of Knowledge
273
into people’s minds directly. They observe how people handle various cognitive
tasks that require the manipulation of mentally represented knowledge.
In neuropsychological studies, researchers typically use one of two methods:
(1) they observe how the normal brain responds to various cognitive tasks involving
knowledge representation, or (2) they observe the links between various deficits in
knowledge representation and associated pathologies in the brain.
In the following sections, we explore some of the theories researchers have proposed to explain how we represent and store knowledge in our minds:
• First, we consider what the difference is between images and words when they
are used to represent ideas in the outside world, such as in a book.
• Then we learn about mental images and the idea that we store some of our
knowledge in the form of images.
• Next, we explore the idea that knowledge is stored in the form of both words
and images (dual-code theory).
• Finally, we consider an alternative—propositional theory—which suggests that
we actually use an abstract form of knowledge encoding that makes use of neither words nor mental images.
Communicating Knowledge: Pictures versus Words
Knowledge can be represented in different ways in your mind: It can be stored as a
mental picture, or in words, or abstract propositions. In this chapter, we focus on the
difference between those kinds of knowledge representation. Of course, cognitive
psychologists chiefly are interested in our internal, mental representations of what
we know. However, before we turn to our internal representations, let’s look at external representations, like books. A book communicates ideas through words and
pictures. How do external representations in words differ from such representations
in pictures?
Some ideas are better and more easily represented in pictures, whereas others are
better represented in words. For example, suppose someone asks you, “What is the
shape of a chicken egg?” You may find drawing an egg easier than describing it.
Many geometric shapes and concrete objects seem easier to represent in pictures
rather than in words. However, what if someone asks you, “What is justice?” Describing such an abstract concept in words would already be very difficult, but doing
so pictorially would be even harder.
As Figure 7.3(a) and Figure 7.3(b) show, both pictures and words may be used
to represent things and ideas, but neither form of representation actually retains all
the characteristics of what is being represented. For example, neither the word cat
nor the picture of the cat actually eats fish, meows, or purrs when petted. Both the
word cat and the picture of this cat are distinctive representations of “catness.” Each
type of representation has distinctive characteristics.
As you just observed, the picture is relatively analogous (i.e., similar) to the realworld object it represents. The picture shows concrete attributes, such as shape and
relative size. These attributes are similar to the features and properties of the realworld object the picture represents. Even if you cover up a portion of the figure of
the cat, what remains still looks like a part of a cat. Under typical circumstances,
most aspects of the picture are grasped simultaneously; but you may scan the picture,
zoom in for a closer look, or zoom out to see the big picture. Even when scanning or
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
(a)
(b) The cat is under the table.
(c) UNDER (CAT, TABLE)
Figure 7.3 Different Kinds of Mental Representations.
We may represent things and ideas in pictures or in words. Neither pictures nor words
capture all the characteristics of what they represent, and each more readily captures some
kinds of information than other kinds. Some cognitive psychologists have suggested that we
have (a) some mental representations that resemble pictorial, analogous images; (b) other
mental representations that are highly symbolic, like words; and perhaps even (c) more fundamental propositional representations that are in a pure abstract “mentalese” that is neither
verbal nor pictorial, which cognitive psychologists often represent in this highly simplified
shorthand.
zooming, however, there are no arbitrary rules for looking at the picture—you may
scan the picture from the left to the right, from the bottom to the top, or however it
pleases you.
In contrast, the word cat is a symbolic representation, meaning that the relationship between the word and what it represents is simply arbitrary. There is nothing inherently catlike about the word. If you had grown up in another country like
Germany or France, the word “Katze” or the word “chat,” respectively, would instead
symbolize the concept of a cat to you. Suppose you cover up part of the word “cat.”
The remaining visible part no longer bears even a symbolic relationship to any part
of a cat.
Because symbols are arbitrary, their use requires the application of rules. For
example, in forming words, the sounds or letters also must be sequenced according
to rules (e.g., “c-a-t,” not “a-c-t” or “t-c-a”). In forming sentences, the words also
must be sequenced according to rules. For example, one can say “the cat is under
the table,” but not “table under cat the is.”
Symbolic representations, such as the word cat, capture some kinds of information but not other kinds of information. The dictionary defines cat as “a carnivorous mammal (Felis catus) long domesticated as a pet and for catching rats and
mice” (Merriam-Webster’s Online Dictionary, 2010). Suppose our own mental representations for the meanings of words resemble those of the dictionary. Then the
Mental Representation of Knowledge
275
INVESTIGATING COGNITIVE PSYCHOLOGY
Representations in Pictures and Words
Find a book or magazine with a photo of an animal, plant, or other object (house, car,
airplane) and write down the word for that thing. What is the shape of the word? What
is the shape of the picture? Cover part of the word and explain how what is left relates
to the characteristics of that thing. Now cover part of the picture and explain how what
is left relates to the characteristics of that thing.
word cat connotes an animal that eats meat (“carnivorous”), nurses its young
(“mammal”), and so on. This information is abstract and general. It may be
applied to any number of specific cats having any fur color or pattern. To represent
additional characteristics, we must use additional words, such as black, Persian, or
calico.
The picture of the cat does not convey any of the abstract information conveyed
by the word regarding what the cat eats, whether it nurses its young, and so on.
However, the picture conveys a great deal of concrete information about this specific
cat. For example, it communicates the exact position of the cat’s legs, the angle at
which we are viewing the cat, the length of the cat’s tail, whether both of its eyes
are open, and so on.
Pictures and words also represent relationships in different ways. The picture in
Figure 7.3(a) shows the spatial relationship between the cat and the table. For any
given picture showing a cat and a table, the spatial (positional) relationship (e.g.,
beside, above, below, behind) will be represented concretely in the picture. In contrast, when using words, we must state spatial relationships between things explicitly
by a discrete symbol, such as a preposition (“The cat is under the table.”). More
abstract relationships, however, such as class membership, often are implied by the
meanings of the words. Cats are mammals or tables are items of furniture. But
abstract relationships rarely are implied through pictures.
To summarize, pictures aptly capture concrete and spatial information in a manner analogous to whatever they represent. They convey all features simultaneously.
In general, any rules for creating or understanding pictures pertain to the analogous
relationship between the picture and what it represents. They help ensure as much
similarity as possible between the picture and the object it represents. Words, on the
contrary, handily capture abstract and categorical information in a manner that is
symbolic of whatever the words represent. Representations in words usually convey
information sequentially. They do so according to arbitrary rules that have little to
do with what the words represent. Pictures and words are both well suited to some
purposes but not to others. For example, blueprints and identification photos serve
different purposes than essays and memos.
Now that we have some preliminary ideas about external representations of
knowledge, let’s consider internal representations of knowledge. Specifically, how
do we represent what we know in our minds? Do we have mental scenarios
(pictures) and mental narratives (words)? In subsequent chapters on information
processing and language, we discuss symbolic mental representations. In this chapter,
we focus on mental imagery.
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Pictures in Your Mind: Mental Imagery
Imagery is the mental representation of things that are not currently seen or sensed
by the sense organs (Moulton & Kosslyn, 2009; Thomas, 2003). In our minds we
often have images for objects, events, and settings. For example, recall one of your
first experiences on a college campus. What were some of the sights, sounds, and
smells you sensed at that time—cut grass, tall buildings, or tree-lined paths? You do
not actually smell the grass and see the buildings, but you still can imagine them.
Mental imagery even can represent things that you have never experienced. For
example, imagine what it would be like to travel down the Amazon River. Mental
images even may represent things that do not exist at all outside the mind of the
person creating the image. Imagine how you would look if you had a third eye in
the center of your forehead!
Imagery may involve mental representations in any of the sensory modalities,
such as hearing, smell, or taste. Imagine the sound of a fire alarm, your favorite
song, or your nation’s anthem. Now imagine the smell of a rose, of fried bacon,
or of an onion. Finally, imagine the taste of a lemon, pickle, or your favorite
candy. At least hypothetically, each form of mental representation is subject to
investigation (e.g., Kurby et al., 2009; Palmieri et al., 2009; Pecenka & Keller,
2009).
Nonetheless, most research on imagery in cognitive psychology has focused on
visual imagery, such as representations of objects or settings that are not presently
visible to the eyes. When students kept a diary of their mental images, the students
reported many more visual images than auditory, smell, touch, or taste images
(Kosslyn et al., 1990). Most of us are more aware of visual imagery than of other
forms of imagery.
We use visual images to solve problems and to answer questions involving
objects (Kosslyn & Rabin, 1999; Kosslyn, Thompson & Ganis, 2006). Which is darker red—a cherry or an apple? How many windows are there in your house or apartment? How do you get from your home, apartment, or dormitory room to your first
class of the day? How do you fit together the pieces of a puzzle or the component
parts of an engine, a building, or a model? According to Kosslyn, to solve problems
and answer questions such as these, we visualize the objects in question. In doing so,
we mentally represent the images.
Many psychologists outside of cognitive psychology are interested in applications
of mental imagery to other fields in psychology. Such applications include using
guided-imagery techniques for controlling pain and for strengthening immune responses and otherwise promoting health. With such techniques, you could imagine
being at a beautiful beach and feeling very comfortable, letting your pain fade into
the background. Or you could imagine the cells of your immune system successfully
destroying all the bad bacteria in your body. Such techniques are also helpful in
overcoming psychological problems, such as phobias and other anxiety disorders.
Design engineers, biochemists, physicists, and many other scientists and technologists use imagery to think about various structures and processes and to solve problems in their chosen fields.
Not everyone is equally skilled in creating and manipulating mental images,
however. Research in applied settings and in the laboratory indicates that some of
us are better able to create mental images than are others (Reisberg et al., 1986;
Schienle et al., 2008). These differences are even measurable with functional
Mental Representation of Knowledge
277
magnetic resonance imaging (f MRI) (Cui et al., 2007). Research also indicates that
the use of mental images can help to improve memory. In the case of persons with
Down syndrome, the use of mental images in conjunction with hearing a story improved memory for the material as compared with just hearing the story (de la Iglesia,
Buceta, & Campos, 2005; Kihara & Yoshikawa, 2001). Mental imagery also is used in
other fields such as occupational therapy. Using this technique, patients with brain
damage train themselves to complete complex tasks. For instance, by means of imagining the details of the tasks in the correct order so as to remember all the details
involved, brain-damaged patients can wash dishes or take medication (Liu &
Chan, 2009).
In what form do we represent images in our minds? According to an extreme
view of imagery, all images of everything we ever sense may be stored as exact copies
of physical images. But realistically, to store every observed physical image in the
brain seems impossible. The capacity of the brain would be inadequate to such a
task (Kosslyn, 2006; Kosslyn & Pomerantz, 1977). Note the simple example in
Investigating Cognitive Psychology: Can Your Brain Store Images of Your Face?
Amazingly, learning can indeed take place just by using mental images. A study
by Tartaglia and colleagues (2009) presented participants with a vertical parallel arrangement of three lines. The middle one was closer either to the right or left outer
line. Practice using mental images resulted in participants becoming more sensitive
to the asymmetry toward either the left or right side. A study with architects also
showed the importance of mental images. Whether or not they were permitted to
draw sketches in the early design phase of a project did not impact the design outcome and cognitive activity—if they were not allowed to draw sketches, they just
used mental imaging (Bilda, 2006).
Dual-Code Theory: Images and Symbols
According to dual-code theory, we use both pictorial and verbal codes for representing information (Paivio, 1969, 1971) in our minds. These two codes organize information into knowledge that can be acted on, stored somehow, and later retrieved for
subsequent use. According to Paivio, mental images are analog codes. Analog codes
resemble the objects they are representing. For example, trees and rivers might be
represented by analog codes. Just as the movements of the hands on an analog clock
are analogous to the passage of time, the mental images we form in our minds are
analogous to the physical stimuli we observe.
INVESTIGATING COGNITIVE PSYCHOLOGY
Can Your Brain Store Images of Your Face?
Look at your face in a mirror. Gradually turn your head from far right (to see yourself out
of your left peripheral vision) to far left. Now tilt your head as far forward as you can
then tilt it as far back as you can. All the while, make sure you still are seeing your reflection. Now make a few different expressions, perhaps even talking to yourself to exaggerate your facial movements. Could your brain store this series of separate images of your
face? Storing each of these images and every image you see every day for years likely
is impossible for your brain. So how do we store images in our brains?
CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
In contrast, our mental representations for words chiefly are represented in a
symbolic code. A symbolic code is a form of knowledge representation that has been
chosen arbitrarily to stand for something that does not perceptually resemble what is
being represented. Just as a digital watch uses arbitrary symbols (typically, numerals)
to represent the passage of time, our minds use arbitrary symbols (words and combinations of words) to represent many ideas. Sand can be used as well to represent the
flow of time, as shown in the hourglass in Figure 7.4.
A symbol may be anything that is arbitrarily designated to stand for something
other than itself. For example, we recognize that the numeral “9” is a symbol for the
concept of “nineness.” It represents a quantity of nine of something. But nothing
about the symbol in any way would suggest its meaning. We arbitrarily have designated this symbol to represent the concept. But “9” has meaning only because we
use it to represent a deeper concept. Concepts like justice and peace are best represented symbolically.
Paivio, consistent with his dual-code theory, noted that verbal information
seems to be processed differently than pictorial information. For example, in one
study, participants were shown both a rapid sequence of pictures and a sequence of
words (Paivio, 1969). They then were asked to recall the words or the pictures in
one of two ways. One way was at random, so that they recalled as many items as
possible, regardless of the order in which the items were presented. The other way
was in the correct sequence.
Participants more easily recalled the pictures when they were allowed to do so in
any order. But they more readily recalled the sequence in which the words were
presented than the sequence for the pictures, which suggests the possibility of two
different systems for recall of words versus pictures.
Other researchers have found supporting evidence for dual-code theory as well.
For example, it has been hypothesized that actual visual perception could interfere
Neustockimages/iStockphoto.com
278
Figure 7.4 Symbols Can Represent Ideas in Our Minds.
This hourglass illustrates that we can depict the passage of time in various ways. We do not
necessarily need numbers.
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INVESTIGATING COGNITIVE PSYCHOLOGY
Analogical and Symbolic Representations of Cats
To get an intuitive sense of how you may use each of the two kinds of representations,
think about how you mentally represent all the facts you know about cats. Use your mental definition of the word cat and all the inferences you may draw from your mental
image of a cat. Which kind of representation is more helpful for answering the following
questions:
• Is a cat’s tail long enough to reach the tip of the cat’s nose if the cat is stretching to
full length?
• Do cats like to eat fish?
• Are the back legs and the front legs of a cat exactly the same size and shape?
• Are cats mammals?
• Which is wider—a cat’s nose or a cat’s eye?
Which kinds of mental representations were the most valuable for answering each of
these questions?
with simultaneous visual imagery. Similarly, the need to produce a verbal response
could interfere with the simultaneous mental manipulation of words. If, however,
an experiment found that visual and verbal tasks do not interfere with each other,
this result would indicate that the two kinds of tasks draw on two different systems.
A classic investigation tested this notion (Brooks, 1968). Participants performed
either a visual task or a verbal task. The visual task involved answering questions
requiring judgments about a picture that was presented briefly. The verbal task involved answering questions requiring judgments about a sentence that was stated
briefly. Participants expressed their responses verbally (saying “yes” or “no” aloud),
visually (pointing to an answer), or manually (tapping with one hand to agree and
the other to disagree). There were two conditions in which Brooks expected interference: a visual task requiring a visual (pointing) response and a verbal task requiring a verbal response. This prediction assumed that both task and response required
the same system for completion. Interference was measured by slow-downs in
INVESTIGATING COGNITIVE PSYCHOLOGY
Dual Coding
Look at the list of words that your friends and family members recalled in the demonstration in Chapter 6. Add up the total number of recollections for every other word (i.e.,
book, window, box, hat, etc.—the words in odd-numbered positions in the list). Now
add up the total number of recollections for the other words (i.e., peace, run, harmony,
voice, etc.—the words in even-numbered positions in the list). Most people will recall
more words from the first set than from the second set. This is because the first set is
made up of words that are concrete, or those words that are easily visualized. The second set of words is made up of words that are abstract, or not easily visualized. This is a
demonstration of the dual-coding hypothesis (or its more contemporary version, the
functional-equivalence hypothesis).
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
IN THE LAB OF STEPHEN KOSSLYN
Seeing with the Mind’s Eye
of the brain are activated during visual imagery, but some do not. In an analysis of
If asked to decide what shape Mickey
the results from more than 50 such studies,
Mouse’s ears are, most people report
we found that the variations in results rethat they visualize the cartoon figure’s
flected three factors: (1) if the task required
ears and “see” that the ears are circular.
“seeing” parts with relatively high resoluVisual mental imagery hinges on such
tion (e.g., as is necessary to use imagery
“seeing with the mind’s eye” and is used
to classify the shape of an animal’s ears
not only to recall information (often that
from memory), then these parts of visual
STEPHEN KOSSLYN
one has not thought about previously,
cortex are activated; (2) if the task is spatial
such as the shape of that rodent’s ears), but also in vari(e.g., as required to decide in which arm the Statue of
ous forms of reasoning. For example, when considering
Liberty holds the torch), these parts of the brain are not
how best to fit a bunch of backpacks, suitcases, and
activated; and (3) if a more powerful scanning technique
duffle bags into a trunk of a car, you might visualize
is used (e.g., using a more powerful magnet in a magnetic
each of them, and “see” how best to move them around
resonance imaging machine), then it is more likely that
and pack them efficiently—all before lifting a finger to
activation in these areas will be detected.
heft a single bag into the trunk.
In addition, in order to use imagery in reasoning—
My lab has studied the nature of visual mental imsuch as in packing the trunk of a car—one must be able
agery for more than three decades now and a considto transform the image (rotating objects in it, sliding
erable amount has been learned. First and foremost,
them around, bending them, etc.). We have found
visual mental imagery is a lot like visual perception,
that there are several distinct ways in which such prowhich occurs when one registers input from the eyes.
cesses occur. For example, you can imagine physically
That is, whereas imagery is a bit like playing a DVD
moving the objects in the image (e.g., twisting them by
and seeing the results on the screen, perception is
hand) or can imagine some external force moving them
more like seeing the input from a camera displayed
(e.g., watching a motor spin them around). In the foron a screen (but this is just a metaphor; there’s no little
mer case, parts of the brain used to control actual
man in your head watching a screen—it’s just signals
movements are activated during mental imagery, but
being processed). In fact, when we asked participants
not when the same movement is imagined as a result
to classify parts of visible (but degraded) objects and, in
of an external force’s being at work.
another part of the test, to close their eyes and classify
This research has shown that much of the brain is
parts of visualized objects, more than 90% of the same
activated in comparable ways during visual imagery
brain areas were activated in common.
and perception. But imagery has turned out to be “not
However, there has been a controversy about which
one thing”; rather, it is a collection of distinct abilities
parts of the brain give rise to visual mental imagery. Spe(such as those used to classify shapes versus those used
cifically, are the first parts of the cortex to register input from
to rotate objects). Each new discovery about mental
the eyes during perception also used during visual mental
imagery brings us a little closer toward understanding
imagery? (Just how similar is mental imagery to percephow we can “see” things that aren’t there!
tion?) Some neuroimaging studies find that these portions
response times. Brooks confirmed his hypothesis. Participants did show slower response times in performing the pictorial task when asked to respond using a competing visual display, as compared with when they were using a noninterfering response
medium (i.e., either verbal or manual).
Mental Representation of Knowledge
281
Similarly, his participants showed more interference in performing the verbal task
when asked to respond using a competing verbal form of expression, as compared
with how they performed when responding manually or by using a visual display.
Thus, a response involving visual perception can interfere with a task involving
manipulations of a visual image. Similarly, a response involving verbal expression
can interfere with a task involving mental manipulations of a verbal statement. These
findings suggest the use of two distinct codes for mental representation of knowledge.
The two codes are an imaginal (analogical) code and a verbal (symbolic) code.
Storing Knowledge as Abstract Concepts:
Propositional Theory
Not everyone subscribes to the dual-code theory. Researchers have developed an
alternative theory termed a conceptual-propositional theory, or propositional theory
(Anderson & Bower, 1973; Pylyshyn, 1973, 1984; 2006). Propositional theory
suggests that we do not store mental representations in the form of images or mere
words. We may experience our mental representations as images, but these images are
epiphenomena—secondary and derivative phenomena that occur as a result of other
more basic cognitive processes. According to propositional theory, our mental representations (sometimes called “mentalese”) more closely resemble the abstract form of
a proposition. A proposition is the meaning underlying a particular relationship
among concepts. Anderson and Bower have moved beyond their original conceptualization to a more complex model that encompasses multiple forms of mental representation. Others, such as Pylyshyn (2006), however, still hold to this position.
What Is a Proposition?
How would a propositional representation work? Consider an example. To describe
Figure 7.3(a), you could say, “The table is above the cat.” You also could say, “The
cat is beneath the table.” Both these statements indicate the same relationship as
“Above the cat is the table.” With a little extra work, you probably could come up
with a dozen or more ways of verbally representing this relationship.
Logicians have devised a shorthand means, called “predicate calculus,” of expressing the underlying meaning of a relationship. It attempts to strip away the various
superficial differences in the ways we describe the deeper meaning of a proposition:
[Relationship between elements]([Subject element], [Object element])
The logical expression for the proposition underlying the relationship between
the cat and the table is shown in Figure 7.3(c). This logical expression, of course,
would need to be translated by the brain into a format suitable for its internal mental representation.
Using Propositions
It is easy to see why the hypothetical construct of propositions is so widely accepted
among cognitive psychologists. Propositions may be used to describe any kind of
relationship. Examples of relationships include actions of one thing on another,
attributes of a thing, positions of a thing, class membership of a thing, and so on,
as shown in Table 7.1. In addition, any number of propositions may be combined
to represent more complex relationships, images, or series of words. An example
would be “The furry mouse bit the cat, which is now hiding under the table.” The
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Table 7.1
Propositional Representations of Underlying Meanings
We may use propositions to represent any kind of relationship, including actions, attributes, spatial positions, class
membership, or almost any other conceivable relationship. The possibility for combining propositions into complex
propositional representational relationships makes the use of such representations highly flexible and widely
applicable.
Type of
Relationship
Representation in Words
Propositional
Representation*
Actions
A mouse bit a cat.
Bite [action] (mouse
[agent of action], cat
[object])
Attributes
Mice are furry.
[external surface characteristic] (furry [attribute],
mouse [object])
Spatial positions
A cat is under the table.
[vertically higher position]
(table, cat)
Class or
Category
membership
A cat is an animal.
[categorical membership]
(animal [category], cat
[member])
Imaginal Representation
*In this table, propositions are expressed in a shorthand form (known as “predicate calculus”) commonly used to express underlying
meaning. This shorthand is intended only to give some idea of how the underlying meaning of knowledge might be represented. It is not
believed that this form is literally the form in which meaning is represented in the mind. In general, the shorthand form for representing
propositions is this: [Relationship between elements] ([subject element], [object element]).
key idea is that the propositional form of mental representation is neither in words
nor in images. Rather, it is in an abstract form representing the underlying meanings
of knowledge. Thus, a proposition for a sentence would not retain the acoustic or
visual properties of the words. Similarly, a proposition for a picture would not retain
the exact perceptual form of the picture (Clark & Chase, 1972).
According to the propositional view (Clark & Chase, 1972), both images [e.g., of
the cat and the table in Figure 7.3(a)] and verbal statements [e.g., in Figure 7.3(b)]
are mentally represented in terms of their deep meanings, and not as specific images
or words. That is, they are represented as propositions. According to propositional
theory, pictorial and verbal information are encoded and stored as propositions.
Then, when we wish to retrieve the information from storage, the propositional representation is retrieved. From it, our minds re-create the verbal or the imaginal code
relatively accurately.
Some evidence suggests that these representations need not be exclusive. People
seem to be able to employ both types of representations to increase their performance on cognitive tests (Talasli, 1990).
Mental Representation of Knowledge
283
Do Propositional Theory and Imagery
Hold Up to Their Promises?
The controversy over whether we represent information in our memory by means
of propositions or mental images continues today (see for example Kosslyn, 2006;
Pylyshyn, 2006). Both theories have their limits. We explore these limits in the next
section.
Limitations of Mental Images
What are the limits to analogical representation of images? For example, look quickly
at Figure 7.5, then look away. Does Figure 7.5 contain a parallelogram (a four-sided
figure that has two pairs of parallel lines of equal length)? Participants in one study
looked at figures such as this one. They had to determine whether particular shapes
(e.g., a parallelogram) were or were not part of a given whole figure (Reed, 1974).
Overall performance was little better than chance. The participants appeared unable
to call up a precise analogical mental image. They could not use a mental image to
trace the lines to determine which component shapes were or were not part of a
whole figure. To Reed, these findings suggested the use of a propositional code rather
than an analogical one. Examples of a propositional code would be “a Star of David”
or “two overlapping triangles, one of which is inverted.” Another possible explanation
is that people have analogical mental images that are imprecise in some ways.
There are additional limits to knowledge representation in mental images
(Chambers & Reisberg, 1985, 1992).
• Look at Figure 7.6(a).
• Now cover the image and imagine the rabbit shown in the figure.
Actually, the figure shown here is an ambiguous figure, meaning that it can be
interpreted in more than one way. Ambiguous figures often are used in studies of
perception. But these researchers decided to use such figures to determine whether
Figure 7.5 Mental Images.
Quickly glance at this figure and then cover it with your hand. Imagine the figure you just
saw. Does it contain a parallelogram?
Source: From Cognition, Third Edition, by Margaret W. Matlin. Copyright © 1994 by Holt, Rinehart and
Winston. Reproduced by permission of the publisher.
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
(a)
(b)
(c)
Figure 7.6 Can Mental Images Be Ambiguous?
(a) Look closely at the rabbit, then cover it with your hand and recreate it in your mind. Can you see a different animal
in this image just by mentally shifting your perspective? (b) What animal do you observe in this figure? Create a mental
image of this figure, and try to imagine the front end of this animal as the back end of another animal and the tail end
of this animal as the front end of another animal. (c) Observe the animal in this figure, and create a mental image of
the animal; cover the figure, and try to reinterpret your mental image as a different kind of animal (both animals
probably are facing in the same direction).
Sources: From D. Chambers and D. Reisberg (1985), “Can Mental Images be Ambiguous?” Journal of Experimental Psychology: Human
Perception and Performance, 11, 317–328. Copyright © 1985 by the American Psychological Association. Reprinted with permission.
(b, c) Peterson, M. A., Kihlstrom, J. F., Rose, P. M., & Glisky, M. L. (1992). Mental images can be ambiguous: Reconstruals and
reference-frame reversals. Memory & Cognition, 20, 107–123. Reprinted by permission of Psychonomic Society, Inc.
mental representations of images are truly analogical to perceptions of physical objects (i.e., if mental images are indeed representations similar to what our eyes see).
• Without looking back at the figure, can you determine the alternative interpretation of Figure 7.6(a)?
When the participants in Chambers and Reisberg’s study had difficulty, the researchers offered cues. But even participants with high visualization skills often were
unable to conjure the alternative interpretation.
Finally, the investigators suggested to participants that they should draw the figures out of their memory.
• Without looking again at the figure, briefly sketch Figure 7.6(a), based on your
own mental representation of it.
• Once you have completed your sketch, try once more to see whether you can
find an alternative interpretation of the figure.
If you are like most of Chambers and Reisberg’s participants, you need to have
an actual percept (object of perception) of the figure in front of you so you can guess
Mental Representation of Knowledge
285
at an alternative interpretation of the figure. These results indicate that mental
representations of figures are not the same as percepts of these figures. In case you
have not yet guessed it, the alternative interpretation of the rabbit is a duck. In
this interpretation, the rabbit’s ears are the duck’s bill. One interpretation of Chambers and Reisberg’s findings—an implausible one—is that people plainly do not use
images to represent what they see. An alternative and more plausible explanation is
that a propositional code may override the imaginal code in some circumstances.
Early studies have also suggested that visual images can be distorted through verbal information. Participants were asked to view figures that were labeled. When they
recalled the images, they were distorted in the direction of the meaning of the images.
Much earlier work suggested that semantic (verbal) information (e.g., labels for
figures) tends to distort recall of visual images in the direction of the meaning of the
images (Carmichael, Hogan, & Walter, 1932). For example, for each of the figures
in the center column of Figure 7.7, observe the alternative interpretations for the
figures recalled. Recall differs based on the differing labels given for the figures.
Reproduced
figure
Verbal
labels
Curtains
in a
window
Stimulus
figures
Verbal
labels
Diamond
in a
rectangle
Seven
Four
Ship's
wheel
Sun
Hourglass
Reproduced
figure
Table
Kidney
bean
Canoe
Pine
tree
Trowel
Broom
Gun
Two
Eight
Figure 7.7 The Influence of Semantic Labels.
Semantic labels clearly influence mental images, as shown here in the differing drawings based on mental images of
objects given differing semantic (verbal) labels. (After Carmichael, Hogan, & Walter, 1932.)
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Limitations of Propositional Theory
In contrast to the work just discussed, there is some evidence that we do not necessarily need a propositional code to manipulate information, but can manipulate
mental imagery directly.
Participants in a study by Finke and colleagues (Finke, Pinker, & Farah, 1989)
manipulated mental images by combining two distinct images to form a different
mental image altogether. This manipulation of mental images may be thought of as
an imaginal Gestalt experience. In the combined image, the whole of the two combined images differed from the sum of its two distinct parts. The study showed that
in some situations, mental images can be combined effectively (e.g., the letter H
and the letter X) to create mental images. The images may be of geometric shapes
(e.g., right triangles), of letters (e.g., M), or of objects (e.g., a bow tie).
It appears that propositional codes are less likely to influence imaginal ones
when participants create their own mental images, rather than when participants
are presented with a picture to be represented. However, propositional codes may
influence imaginal ones. This influence is especially likely to occur when the picture
used for creating an image is ambiguous [as in Figure 7.6(a)–(c)] or rather abstract
(as in Figure 7.5).
Other investigators have built on Finke’s work regarding the construction of
mental images (Finke, Pinker, & Farah, 1989). They presented an alternative view
of Chambers and Reisberg’s findings regarding the manipulation of ambiguous figures
(Peterson et al., 1992). They believe that the mental reinterpretation of ambiguous
figures involves two manipulations.
1. The first is a mental realignment of the reference frame. This realignment would
involve a shift in the positional orientations of the figures on the mental “page”
or “screen” on which the image is displayed. In Figure 7.6(a), the shift would be
of the duck’s back to the rabbit’s front, and the duck’s front to the rabbit’s
back.
2. The second manipulation is a mental reconstrual (reinterpretation) of parts of
the figure. This reconstrual would be of the duck’s bill as the rabbit’s ears.
Participants may be unlikely to manipulate mental images spontaneously to
reinterpret ambiguous figures, but such manipulations occur when participants are
given the right context.
Under what conditions do participants mentally reinterpret their image of the
duck-rabbit figure [see Figure 7.6(a)] and of some other ambiguous figures (Peterson
et al., 1992)? What are the supporting hints? Across experiments, 20% to 83%
of participants were able to reinterpret ambiguous figures, using one or more of the
following hints:
1. Implicit reference-frame hint. Participants first were shown another ambiguous
figure involving realignment of the reference frame [e.g., see Figure 7.6(b); a
hawk’s head/a goose’s tail, and a hawk’s tail/a goose’s head].
2. Explicit reference-frame hint. Participants were asked to modify the reference
frame by considering either “the back of the head of the animal they had already
seen as the front of the head of some other animal” (Peterson et al., 1992,
p. 111; considered a conceptual hint) or “the front of the thing you were seeing
as the back of something else” (p. 115; considered an abstract hint).
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287
3. Attentional hint. Participants were directed to attend to regions of the figure
where realignments or reconstruals were to occur.
4. Construals from “good” parts. Participants were asked to construe an image from
parts determined to be “good” (according to both objective [geometrical] and
empirical [inter-rater agreement] criteria), rather than from parts determined to
be “bad” (according to similar criteria).
Additionally, some spontaneous reinterpretation of mental images for ambiguous
figures may occur. This is particularly likely for images of figures that may be reinterpreted without realigning the reference frame. For example, see Figure 7.6(c), which
may be a whole snail or an elephant’s head, or possibly even a bird, a helmet, a leaf,
or a seashell.
The investigators went on to suggest that the processes involved in constructing
and manipulating mental images are similar to the processes involved in perceptual
processes (Peterson et al., 1992). An example would be the recognition of forms
(discussed in Chapter 3). Not everyone agrees with this view. Some support for their
views has been found by cognitive psychologists who hold that mental imagery and
visual perception are functionally equivalent. Here, functional equivalence refers to
individuals using about the same operations to serve about the same purposes for
their respective domains.
Overall, the weight of the evidence seems to indicate there are multiple codes
rather than just a single code. But the controversy continues (Kosslyn, 2006;
Pylyshyn, 2006).
CONCEPT CHECK
1. In what forms can knowledge be represented in our mind?
2. What kinds of codes does dual-code theory comprise?
3. What is a proposition?
Mental Manipulations of Images
According to the functional-equivalence hypothesis, although visual imagery is not
identical to visual perception, it is functionally equivalent to it. Functionally equivalent things are strongly analogous to each other—they can accomplish the same
goals. The functionally-equivalent images are thus analogous to the physical percepts
they represent. This view essentially suggests that we use images rather than propositions in knowledge representation for concrete objects that can be pictured in the
mind. This view has many advocates (e.g., Farah, 1988b; Finke, 1989; Jolicoeur &
Kosslyn, 1985a, 1985b; Rumelhart & Norman, 1988; Shepard & Metzler, 1971).
Principles of Visual Imagery
One investigator has suggested some principles of how visual imagery may be functionally equivalent to visual perception (Finke, 1989). These principles may be used
as a guide for designing and evaluating research on imagery. Table 7.2 offers an idea
of some of the research questions that may be generated, based on Finke’s principles.
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Table 7.2
Principles of Visual Imagery: Questions
According to the functional-equivalence hypothesis, we represent and use visual imagery in a way that is functionally
equivalent (strongly analogous) to that for physical percepts. Ronald Finke has suggested several principles of visual
imagery that may be used to guide research and theory development.
Principle
Possible Questions Generated from Principles
1. Our mental transformations of images and our mental
movements across images correspond to those of
physical objects and percepts.
Do our mental images follow the same laws of motion and
space that are observed in physical percepts? For example,
does it take longer to manipulate a mental image at a greater
angle of rotation than at a smaller one? Does it take longer to
scan across a large distance in a mental image than across a
smaller distance?
2. The spatial relations among elements of a visual image are analogous to those relations in actual physical space.
Are the characteristics of mental images analogous to the
characteristics of percepts? For example, is it easier to see the
details of larger mental images than of smaller ones? Are
objects that are closer together in physical space also closer
together in mental images of space?
3. Mental images can be used to generate information
that was not explicitly stored during encoding.
After participants have been asked to form a mental image,
can they answer questions that require them to infer information
based on the image that was not specifically encoded at the
time they created the image? For example, suppose that participants are asked to picture a tennis shoe. Can they later answer questions such as “How many lace-holes are there in the
tennis shoe?”
4. The construction of mental images is analogous to
the construction of visually perceptible figures.
Does it take more time mentally to construct a more complex
mental image than a simpler one? Does it take longer to construct a mental image of a larger image than of a smaller one?
5. Visual imagery is functionally equivalent to visual
perception in terms of the processes of the visual
system used for each.
Are the same regions of the brain involved in manipulating
mental imagery as are involved in manipulating visual percepts? For example, are similar areas of the brain activated
when mentally manipulating an image, as compared with
those involved when physically manipulating an object?
Neuroscience and Functional Equivalence
Evidence for functional equivalence can be found in neuroimaging studies. In one
study, participants either viewed or imagined an image. Activation of similar brain
areas was noted, in particular, in the frontal and parietal regions. However, there was
no overlap in the areas associated with sensory processes, such as vision (Ganis,
Thomspon, & Kosslyn, 2004).
Schizophrenia provides an interesting example of the similarities between perception and imagery. Many people who suffer from schizophrenia experience auditory hallucinations. Auditory hallucinations are experiences of “hearing” that occur
in the absence of actual auditory stimuli. This “hearing” is the result of internally
generated material. These patients have difficulty discriminating between many different types of self-produced and externally provided stimuli (Blakemore et al.,
2000). Evidence from other researchers reveals that during auditory hallucinations
there is abnormal activation of the auditory cortex (Lennox et al., 2000). Additionally, activation of brain areas involved with receptive language (i.e., hearing or
Mental Manipulations of Images
289
reading as opposed to speaking or writing) is observed during auditory hallucinations
(Ishii et al., 2000). In sum, it is believed that auditory hallucinations occur at least
in part because of malfunctions of the auditory imaging system and problematic
perception processes (Seal, Aleman, & McGuire, 2004). These challenges make it
difficult for afflicted individuals to differentiate between internal images and the perception of external stimuli.
These results suggest that there is indeed functional equivalence between what
our senses perceive and what we create in our minds. In the following section, we
will explore the mental manipulation of images in more detail.
Mental Rotations
Mental images can be manipulated in many ways. They can be rotated just like
physical objects. We can also zoom into mental images to see more details of a specific area, or we can scan across an image from one point to another. Keep in mind
that studies about mental image manipulations also give us some indication of
whether the functional-equivalence hypothesis is indeed correct; that is, of whether
mental images and the images we see with our eyes work in the same way and
adhere to the same principles.
How Does Mental Rotation Work?
Mental rotation involves rotationally transforming an object’s visual mental image
(Takano & Okubo, 2003; Zacks, 2008). Just like you can physically rotate a water
bottle you hold in your hands, you can also imagine a water bottle in your mind and
rotate it in the mind.
In a classic experiment, participants were asked to observe pairs of pictures
showing three-dimensional (3-D) geometric forms (Shepard & Metzler, 1971). The
forms were rotated from 0 to 180 degrees (Figure 7.8). The rotation was either in the
picture plane [i.e., in 2-D space clockwise or counterclockwise; Figure 7.8(a)] or in
depth [i.e., in 3-D space; Figure 7.8(b)].
In addition, participants were shown distracter forms. These forms were not rotations of the original stimuli [Figure 7.8(c)]. Participants then were asked to tell
whether a given image was or was not a rotation of the original stimulus.
The response times for answering the questions about the rotation of the figures
formed a linear function of the degree to which the figures were rotated (Figure 7.9).
For each increase in the degree of rotation of the figures, there was a corresponding
increase in the response times. Furthermore, there was no significant difference between rotations in the picture plane and rotations in depth. These findings are functionally equivalent to what we might expect if the participants had been rotating
physical objects in space. To rotate objects at larger angles of rotation takes longer.
Whether the objects are rotated clockwise, counterclockwise, or in the third dimension of depth, makes little difference. The finding of a relation between degree of
angular rotation and reaction time has been replicated a number of times with a variety
of stimuli (e.g., Gogos et al., 2010; Van Selst & Jolicoeur, 1994; see also Tarr, 1999).
To try your own hand at mental rotations, do the demonstration in the Investigating Cognitive Psychology: Try Your Skills at Mental Rotations box for yourself (based
on Hinton, 1979).
Other researchers have supported these original findings in other studies of mental rotations. For example, they have found similar results in rotations of 2-D figures,
such as letters of the alphabet (Gogos et al., 2010; Jordan & Huntsman, 1990),
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
(a)
(b)
(c)
Figure 7.8 Mental Rotations.
For which of these pairs of figures does the figure on the right show an accurate rotation
of the figure on the left?
Source: Reprinted with permission from “Mental Rotation,” by R. Shepard and J. Metzler. Science, 171(3972),
701–703. Copyright © 1971, American Association for the Advancement of Science.
cubes (Just & Carpenter, 1985; Peters & Battista, 2008), and body parts, in particular hands (Fiorio, Tinazzi & Aglioti, 2006; Fiorio et al., 2007; Takeda et al., 2009).
In addition, response times are longer for degraded stimuli—stimuli that are blurry,
incomplete, or otherwise less informative (Duncan & Bourg, 1983)—than for intact
stimuli. Response times are also longer for complex items compared with simple
items (Bethell-Fox & Shepard, 1988) and for unfamiliar figures compared with familiar ones (Jolicoeur, Snow, & Murray, 1987). Older adults have more difficulty
with this task than do younger adults (Band & Kok, 2000).
The benefits of increased familiarity also may lead to practice effects—improvements in performance associated with increased practice. When participants have
practice in mentally rotating particular figures (increasing their familiarity), their
performance improves (Bethell-Fox & Shepard, 1988). This improvement, however,
appears not to carry over to rotation tasks for novel figures (Jolicoeur, 1985;
Wiedenbauer, Schmid, & Jansen-Osmann, 2007).
Mental Manipulations of Images
5
291
(a) Picture-plane pairs
Reaction time
(in seconds)
4
3
2
1
0
5
20 40 60 80 100 120 140 160
Angle of rotation (degrees)
(b) Depth pairs
Reaction time
(in seconds)
4
3
2
1
0
20 40 60 80 100 120 140 160
Angle of rotation (degrees)
Figure 7.9 Response Times for Mental Rotation.
Response times to questions about mental rotations of figures show a linear relationship to the
angle of rotation, and this relationship is preserved, whether the rotations are in the picture
plane or are in depth.
Source: Reprinted with permission from “Mental Rotation,” by R. Shepard and J. Metzler. Science, 171(3972),
701–703. Copyright © 1971, American Association for the Advancement of Science.
Moreover, children and young adults showed speedier response times in mentalrotation tasks when given opportunities for practice (Kail & Park, 1990). The performance of both school-aged children and young adults on mental-rotation tasks is
not impaired as a function of their engaging in simultaneous tasks involving memory
recall (Kail, 1991). These findings suggest that mental rotation may be an automatic
process for school-aged children and adults. Given that familiarity with the items
and practice with mental rotation appear to enhance response times, Robert Kail’s
work suggests that mental rotation may be an automatic process. Thus, enhanced
response times may be the result of increasing automatization of the task across the
years of childhood and adolescence. Furthermore, such automatic processes may be a
sign of more effective visuospatial skills because increased speed is associated with
increased accuracy in spatial memory (Kail, 1997).
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INVESTIGATING COGNITIVE PSYCHOLOGY
Try Your Skills at Mental Rotation
Imagine a cube floating in the space in front of you. Now, mentally grasp the left front
bottom corner of the cube with your left hand. Also grasp the right back top corner of the
cube with your right hand. While mentally holding those corners, rotate the cube so that
the corner in your left hand is directly below the corner in your right hand (as if to form a
vertical axis around which the cube would spin). How many corners of the imaginary
cube are in the middle (i.e., not being grasped by your hands)? Describe the positions
of the corners.
How well did you do with this mental rotation? Very few people have experience
with mental rotation of geometric shapes. Most people imagine that there are four remaining corners of the cube being held by the two corners in their hands. They further
imagine that all four corners are aligned on a horizontal plane, parallel to the ground. In
fact, six corners remain. Only two corners are aligned in a given horizontal plane (parallel to the ground) at any one time.
At the other end of the life span, two investigators studied whether processing
speed or other factors may influence age-related changes in mental rotation by adults
(Dror & Kosslyn, 1994). They found that older participants (55–71 years; mean
65 years) responded more slowly and less accurately than did younger participants
(18–23 years; mean 20 years) on mental-rotation tasks, a finding that has been replicated (Band & Kok, 2000; Inagaki et al., 2002). However, they also found that
older and younger participants showed comparable response times and error rates
on tasks involving image scanning. Based on these and other findings, the authors
concluded that aging affects some aspects of visual imagery more than others.
Intelligence and Mental Rotation
The work of Shepard and others on mental rotation provides a direct link between
research in cognitive psychology and research on intelligence. The kinds of problems
studied by Shepard and his colleagues are very similar to problems that can be found
on conventional psychometric tests of spatial ability. For example, the Primary
Mental Abilities test of Louis and Thelma Thurstone (1962) requires mental rotation of two-dimensionally pictured objects in the picture plane. Similar problems
appear on other tests. Shepard’s work points out a major contribution of cognitive
research toward our understanding of intelligence: It has identified the mental representations and cognitive processes that underlie adaptations to the environment and
thus, ultimately, that constitute human intelligence.
Neuroscience and Mental Rotation
Is there any physiological evidence for mental rotation? One type of study involves
the brains of primates, animals whose cerebral processes seem most closely analogous
to our own. Using single-cell recordings in the motor cortex of monkeys, investigators found some physiological evidence that monkeys can do mental rotations
(Georgopoulos et al., 1989). Each monkey had been trained physically to move a
handle in a specific direction toward a target light used as a reference point. Wherever the target light appeared, the monkeys were to use that point as a reference for
the physical rotation of the handle. During these physical rotations, the monkey’s
Mental Manipulations of Images
293
cortical activity was recorded. Later, in the absence of the handle, the target light
again was presented at various locations. The cortical activity again was recorded.
During these presentations, activity in the motor cortex showed an interesting pattern. The same individual cortical cells tended to respond as if the monkeys were
anticipating the particular rotations associated with particular locations of the target
light. Another study examining mental rotation also indicates that the motor cortex
(areas in the posterior frontal cortex) is activated during this task. The areas associated with hand movement were particularly active during the mental rotation task
(Eisenegger, Herwig, & Jancke, 2007; Zacks, 2008).
Preliminary findings based on primate research suggest that areas of the cerebral
cortex have representations that resemble the 2-D spatial arrangements of visual receptors in the retina of the eye (see Kosslyn, 1994b). These mappings may be construed as relatively depictive of the visual arrays in the real world (Cohen et al.,
1996; Kosslyn et al., 1995). Perhaps if these same regions of the cortex are active
in humans during tasks involving mental imagery, mental imagery may be similarly
illustrative of the real world in mental representation.
Current brain-imaging techniques have allowed researchers to create images of
human brain activity noninvasively to address such speculations. For example, in a
study using functional magnetic resonance imaging, investigators found that the
same brain areas involved in perception also are involved in mental rotation tasks
(Cohen et al., 1996; see also Kosslyn & Sussman, 1995). Thus, not only are imagery
and perception functionally equivalent in psychological studies, neuropsychological
techniques also verify this equivalence by demonstrating overlapping brain activity.
Does mental imagery also involve the same mechanisms as memory processes
because we have to recall those images from memory? If so, the functionalequivalence hypothesis for perception would lose some ground. If imagery is “functionally equivalent” to everything, then, in effect, it really is equivalent to nothing.
A careful review cites many psychological studies that find differences between
human-imagery and memory tasks so we can assume that these two kinds of tasks
are not functionally equivalent (Georgopoulos & Pellizzer, 1995).
In sum, there is converging evidence, both from traditional and neuropsychological studies, to lend support to the hypothesis of functional equivalence between perception and mental imagery. Further neuropsychological work on images and
propositions will be discussed later in the chapter.
Gender and Mental Rotation
Mental rotation has been extensively studied in addition to its application to the
theories of imagery. A number of studies have highlighted an advantage for males
over females in mental rotation tasks (Collins & Kimura, 1997; Roberts & Bell,
2000a, 2000b, 2003), but others have not (Beste et al., 2010; Jaencke & Jordan,
2007; Jansen-Osmann & Heil, 2007). A number of studies that have not found gender differences have used characters (like letters or numbers) for mental rotation;
therefore, it is possible that the rotation of characters engages different processes
than the mental rotation of other objects. Some researchers have speculated that
this advantage has decreased since it was first observed. A number of other interesting features of this effect have been identified.
First, in young children, there is no gender difference either in performance or
in neurological activation (Roberts & Bell, 2000a, 2000b). Second, there seem to be
differences in the activation of the parietal regions between men and women. There
is less parietal activation for women than for men completing the same mental
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rotation task. However, women exhibit additional inferior frontal activation
(Hugdahl et al., 2006; Thomsen et al., 2000; Zack, 2008). Thus, in women, spatial
tasks involve both sides of the brain, whereas in men, the right side dominates this
function. The differences in brain activation may mean that men and women use
different strategies to solve mental rotation problems (Blake, McKenzie, & Hamm,
2002; Hugdahl et al., 2006; Jordan et al., 2002). Additionally, women have a proportionally greater amount of gray matter in the parietal lobe than do men, which is
associated with a performance disadvantage for mental rotation tasks for the women
(as they need increased effort to complete the tasks) (Koscik et al., 2009). Training
causes the gender difference to decrease or even to disappear (Bosco, Longoni, &
Vecchi, 2004; Kass, Ahlers, & Dugger, 1998).
Zooming in on Mental Images: Image Scaling
The key idea underlying research on image size and scaling is that we represent and
use mental images in ways that are functionally equivalent to our representations
and uses of percepts. In other words, we use mental images the same way we use
our actual perceptions.
For example, when you look at a building from afar, you won’t be able to see as
many details as when you are close by, and you may not be able to see things as
clearly. Our resolution is limited. In general, seeing details of large objects is easier
than seeing such details of small ones. We respond more quickly to questions about
large objects we observe than to questions about small ones we observe. Now, if we
assume that perception and mental representations are functionally equivalent, then
participants should respond more quickly to questions about features of large imagined objects than to questions about features of small ones.
What happens when we zoom in closer to objects to perceive details? Sooner or
later, we reach a point at which we can no longer see the entire object. To see the
whole object once more, we must zoom out. See Investigating Cognitive Psychology:
Imaging Scaling to observe perceptual zooming for yourself.
In research on visual perception, it is easy for researchers to control the sizes of
the objects you see. However, for research on image size, controlling the sizes of people’s mental images is more difficult. How do you know that the image of the elephant in your head is the same size as the image of the elephant in someone else’s
head? Fortunately, there are some ways to get around this problem (Kosslyn, 1975).
INVESTIGATING COGNITIVE PSYCHOLOGY
Image Scaling
Find a large bookcase (floor to ceiling, if possible; if not, observe the contents of a large
refrigerator with an open door). Stand as close to the bookcase as you can while still
keeping all of it in view. Now, read the smallest writing on the smallest book in the
bookcase. Without changing your gaze, can you still see all of the bookcase? Can
you read the title of the book farthest from the book on which you are focusing your perception? Depending on what you want to see (a detail like a book title or the whole
shelf), you may have to zoom in and out of what you see. When you look at a small
detail, it will be hard to perceive the whole shelf, and vice versa. The same is true for
mental images.
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295
One of the ways is to use relative size as a means of manipulating image size
(Kosslyn, 1975). Participants imagine four pairs of animals—an elephant and a rabbit, a rabbit and a fly, a rabbit and an elephant-sized fly, and a rabbit and a fly-sized
elephant (Figure 7.10 and Investigating Cognitive Psychology: Image Scanning). Then
the participants answer specific questions about the features of the rabbit and are
timed in their responses. It takes them longer to describe the details of smaller
objects than to describe the details of the larger objects. That is, it takes longer to
respond to rabbits paired with elephants or with elephant-sized flies than to respond
to rabbits paired with flies or with fly-sized elephants. This result makes sense intuitively: Imagine we each have a mental screen for visual images and look at an elephant’s eye. The larger the eye on the screen, the more details we can see (Kosslyn,
1983; Kosslyn & Koenig, 1992).
In another study, children in the first and fourth grades and adult college undergraduates were asked whether particular animals can be characterized as having various physical attributes (Kosslyn, 1976). Examples would be “Does a cat have claws?”
and “Does a cat have a head?” In one condition, participants were asked to visualize
each animal and to use their mental image in answering the questions. In the other
condition, the participants were not asked to use mental images. It was presumed
that they used verbal-propositional knowledge to respond to the verbal questions.
In the imagery condition, all participants responded more quickly to questions
about physical attributes that were larger than to questions about attributes that were
smaller. For example, they might have been asked about a cat’s head (larger) and a
cat’s claws (smaller). Different results were found in the nonimagery condition. In
the nonimagery condition, fourth graders and adults responded more quickly to questions about physical attributes based on the distinctiveness of the characteristic for the
animal. For example, they responded more quickly to questions about whether cats
have claws (which are distinctive) than to questions about whether cats have heads
(which are not particularly distinctive to cats alone). The physical size of the features
did not have any effect on performance in the nonimagery condition for either fourth
graders or adults.
INVESTIGATING COGNITIVE PSYCHOLOGY
Image Scanning
Look at the rabbit and the fly in Figure 7.10. Close your eyes and picture them both in
your mind. Now, in your imagination, look only at the fly and determine the exact shape
of the fly’s head. Do you notice yourself having to take time to zoom in to “see” the detailed features of the fly? If you are like most people, you are able to zoom in on your
mental images to give the features or objects a larger portion of your mental screen,
much as you might physically move toward an object you wanted to observe more
closely.
Now, look at the rabbit and the elephant and picture them both in your mind. Next,
close your eyes and look at the elephant. Imagine walking toward the elephant, watching it as it gets closer to you. Do you find that there comes a point when you can no
longer see the rabbit or even all of the elephant? If you are like most people, you will
find that the image of the elephant will appear to overflow the size of your image space.
To “see” the whole elephant, you probably have to mentally zoom out again.
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Figure 7.10
Zooming in on Details.
Stephen Kosslyn (1983) asked participants to imagine either a rabbit and a fly (to observe
zooming in to “see” details) or a rabbit and an elephant (to observe whether zooming in may
lead to apparent overflow of the image space).
Interestingly, first-graders constantly responded more quickly regarding larger attributes, not only in the imagery condition but also in the nonimagery condition.
Many of these younger children indicated that they used imagery even when not instructed to do so. Furthermore, in both conditions, adults responded more quickly
than did children. But the difference was much greater for the nonimagery condition
than for the imagery condition. These findings support the functional-equivalence hypothesis: When we see something in front of our “mental eye,” it takes children and
adults about the same amount of time to perceive it, just as it would if we saw something in real life.
The findings also support the dual-code view in two ways. First, for adults and
older children, responses based on the use of imagery (an imaginal code) differed
from responses based on propositions (a symbolic code). Second, the development
of propositional knowledge and ability does not occur at the same rate as the development of imaginal knowledge and ability. Children just did not have the propositional knowledge yet and therefore were slower than were adults in the
nonimaginary condition. The distinction in the rate of development of each form
of representation also seems to support Paivio’s notion of two distinct codes.
Examining Objects: Image Scanning
Stephen Kosslyn has found additional support for his hypothesis that we use mental
images in image scanning. The key idea underlying image scanning research is that
images can be scanned in much the same way as physical percepts can be scanned.
Furthermore, our strategies and responses for imaginal scanning should be the same
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297
as for perceptual scanning. A means of testing the functional equivalence of imaginal scanning is to observe some aspects of performance during perceptual scanning,
and then compare that performance with performance during imaginal scanning.
For example, in perception, to scan across longer distances takes longer than
to scan across shorter ones (Denis & Kosslyn, 1999). In one of Kosslyn’s experiments, participants were shown a map of an imaginary island, which you can see in
Figure 7.11 (Kosslyn, Ball, & Reiser, 1978). The map shows various objects on the
island, such as a hut, a tree, and a lake. Participants studied the map until they could
reproduce it accurately from memory. Once the memorization phase of the experiment was completed, the critical phase began:
• Participants were instructed that, on hearing the name of an object read to them,
they should imagine the map and mentally scan to the mentioned object.
• As soon as they arrived at the location of that object, they should press a key.
• An experimenter then read to the participants the names of objects.
• The participants had to scan to the proper location and press the button once
they had found it.
This procedure was repeated a number of times. In each case, the participants
mentally moved between various pairs of objects on successive trials. For each trial,
the experimenter kept track of the participants’ response times, indicating the
amount of time it took them to scan from one object to another.
Figure 7.11
Mental Scanning: An Imaginary Island.
Stephen Kosslyn and his colleagues used a map of an imaginary island with various landmarks to determine whether mental scanning across the image of a map was functionally
equivalent to perceptual scanning of a perceived map.
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What did Kosslyn find? There was an almost perfect linear relation between
the distances separating pairs of objects in the mental map and the amount of
time it took participants to press the button. The further away from each other
the objects were, the longer it took participants to scan from one object to the
other. Participants seem to have encoded the map in the form of an image. They
actually scanned that image as needed for a response, just as they would have
scanned a real map.
These findings have been replicated using other objects as well. In one study,
Borst and Kosslyn (2008) presented participants with dots on a screen for a short
time. In the mental image scanning task, participants had to memorize the location
of the dots before the trial. Once the dots had been presented, participants in the
mental-image group were presented with an empty frame that contained only an arrow. They had to decide whether the arrow pointed at one of the dots they had seen
previously. In another condition, the participants were presented with a frame that
contained not only the arrow but also the dots. In all conditions, the time to make a
judgment increased linearly, depending on the distance between the dot and the
arrow.
This finding indicates that the same mechanisms were used, no matter whether
participants looked at the actual dots presented with the arrow, or looked only at
the arrow, needing to imagine the dots. If participants did not use a spatial representation but rather a code based on Pylyshyn’s propositional theory (1973), then
the distance between the points and the arrow should not have influenced reaction
time, but it did. Recall that the experiment by Shepard and Metzler (1971) found
linearly increasing reaction times for mental rotations as the angle of rotation
increased.
Findings supporting an imaginal code have been shown in several other domains.
For example, the same pattern of results has been obtained for scanning objects in
three dimensions (Pinker, 1980). Specifically, participants observed and then mentally represented a 3-D array of objects—toys suspended in an open box—and then
mentally scanned from one object to another.
Representational Neglect
Additional evidence for the similarity between perception and mental imagery
can be seen in cases of representational neglect. Many patients suffering from
spatial neglect (see Chapter 4) also suffer from a related impairment called representational neglect. As noted earlier, in spatial neglect a person ignores half of his
or her visual field. In representational neglect, a person asked to imagine a scene
and then describe it ignores half of the imagined scene. Although these two types
of neglect often occur together, they can also occur independently. Peru and
Zapparoli (1999) described a case of a woman who showed no evidence of
spatial neglect while struggling with tasks that required the production of a mental
image.
In another set of studies, an array was described to patients suffering from representational neglect. When the patients had to recall the array, they could not
describe the left portion (Logie et al., 2005). Similarly, when subjects with representational neglect were presented with an image, they described the entire image.
However, when the image was removed and they were asked to describe the image
from memory, they failed to describe the left portion (Denis et al., 2002).
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299
In scenes, representational neglect is present only when a vantage point is given
(Rode et al., 2004). For example, if a person with representational neglect were
asked to describe his or her kitchen, he or she would do so accurately. However, if
the same person were asked to describe the kitchen from the refrigerator, then he or
she would demonstrate neglect. It is likely that there exists complete knowledge of
the scene, but that knowledge sometimes is not accessible when the patient generates a mental image.
CONCEPT CHECK
1. What is mental rotation?
2. What is some of the neuropsychological evidence for mental rotation?
3. What is image scaling?
4. How do we mentally scan images?
5. What is representational neglect?
Synthesizing Images and Propositions
In this chapter, we have discussed two opposing views of knowledge representation.
One is a dual-code theory, suggesting that knowledge is represented both in images
and in symbols. The second is a propositional theory, suggesting that knowledge is
represented only in underlying propositions, not in the form of images, words, or
other symbols. Before we consider some proposed syntheses of the two hypotheses,
let’s review the findings described thus far. We do so in light of Finke’s principles
of visual imagery (see Table 7.3).
In our discussion, we addressed the first three of Finke’s criteria for imaginal representations. Mental imagery appears functionally equivalent to perception in many
ways. This conclusion is based on studies of mental rotations, image scaling (sizing),
and image scanning. However, the studies involving ambiguous figures and unfamiliar mental manipulations suggest that there are limits to the analogy between perception and imagery.
Do Experimenters’ Expectations Influence
Experiment Outcomes?
Although there seems to be good evidence for the existence of both propositions
and mental images (Borst, 2008; Kosslyn, 2006; Pylyshyn, 2006), the debate is not
over. Perhaps some of the confirmatory results found in image research could be the
result of demand characteristics (i.e., subjects’ perceptions of what is expected of
them when they participate in an experiment) (Intons-Peterson, 1983). Do experimenters’ expectancies regarding the performance of participants on a particular task
create an implicit demand for the participants to perform as expected?
Intons-Peterson (1983) set out to investigate just that question. She manipulated experimenter expectancies by suggesting to one group of experimenters that
task performance would be expected to be better for perceptual tasks than for imaginal ones. She suggested the opposite outcome to a second group of experimenters.
Would the different expectations of the experimenters lead to different performances
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Table 7.3
Principles of Visual Imagery: Findings
How well did the studies reported in this chapter satisfy the criteria suggested by Ronald Finke’s principles of visual
imagery?
Principle
Study Findings
1. Our mental transformations of
images and our mental
movements across images
correspond to similar transformations of and movements
across physical objects and
percepts.
Mental rotations generally conform to the same laws of motion and space that are
observed in physical percepts (e.g., Shepard & Metzler, 1971), even showing performance decrements associated with degraded stimuli (Duncan & Bourg, 1983.) (See
Chapter 3 for comparisons with perceptual stimuli). However, it appears that for some
mental images, mental rotations of imaginal objects do not fully and accurately represent the physical rotation of perceived objects (e.g., Gogos et al., 2010; Hinton,
1979; Zacks, 2008). Therefore, some nonimaginal knowledge representations or
cognitive strategies appear influential in some situations. In image scanning, it takes
longer to scan across a large distance in a mental image than across a smaller distance (Borst & Kosslyn, 2008; Kosslyn, Ball, & Reiser, 1978).
2. The spatial relations among
elements of a visual image
are analogous to those relations in actual physical
space.
It appears that cognitive manipulations of mental images are analogous to manipulations of percepts in studies involving image size. As in visual perception, there are
limits to the resolution of the featural details of an image, as well as limits to the size of
the image space (analogous to the visual field) that can be “observed” at any one
time. To observe greater detail of individual objects or parts of objects, a smaller size
or number of objects or parts of objects may be observed, and vice versa (Kosslyn,
1975). In related work (Kosslyn, 1976), it appears easier to see the details of larger
mental images (e.g., a cat’s head) than of smaller ones (e.g., a cat’s claws). It
appears also that, just as we perceive the physical proximity (closeness) of objects that
are closer together in physical space, we also imagine the closeness of mental images
in our mental image space (Kosslyn, Ball, & Reiser, 1978).
3. Mental images can be used
to generate information that
was not explicitly stored during encoding.
After participants have been asked to form a mental image, they can answer some
questions that require them to infer information, based on the image, which was not
specifically encoded at the time they created the image. The studies by Reed (1974)
and by Chambers and Reisberg (1985) suggest that propositional representations may
play a role. Studies by Finke (1989) and by Peterson and colleagues (1992) suggest
that imaginal representations are sometimes sufficient for drawing inferences.
4. The construction of mental
images is analogous to the
construction of visually perceptible figures.
Studies of lifelong blind people suggest that mental imagery in the form of spatial
arrangements may be constructed from haptic (touch-based), rather than visual,
information. Based on the findings regarding cognitive maps (e.g., Friedmann &
Montello, 2004; Louwerse & Zwaan, 2009; Saarinen, 1987b; Tversky, 1981;
Wagner, 2006), it appears that both propositional and imaginal knowledge
representations influence the construction of spatial arrangements.
5. Visual imagery is functionally
equivalent to visual perception
in terms of the processes of the
visual system used for each.
It appears that some of the same regions of the brain that are involved in manipulating
visual percepts may be involved in manipulating mental imagery (e.g., see Farah
et al., 1988a, 1988b; see also Zacks, 2008). But it also appears that spatial and
visual imagery may be represented differently in the brain.
of the participants? She found that experimenter expectancies did influence participants’ responses in three tasks: image scanning, mental rotations, and another task
comparing perceptual performance with imaginal performance.
When experimenters expected imaginal performance to be better than perceptual performance, participants responded accordingly, and vice versa. This result
occurred even when the experimenters were not present while participants were
responding and when the cues were presented via computer. Thus, experimental
Synthesizing Images and Propositions
301
participants performing visualization tasks may be responding in part to the demand
characteristics of the task. These demand characteristics result from the experimenters’ expectations regarding the outcomes.
Other investigators responded to these findings (Jolicoeur & Kosslyn, 1985a,
1985b). In one experiment, participants were not asked to scan their mental images
at all. However, they were asked two kinds of questions intermixed with each other:
questions that involved responses requiring image scanning and questions that did
not. Even when image scanning was not an implicit task demand, participants’ responses to questions that required image scanning still showed a linear increase in
response time if the subjects had to scan across a longer distance. When questions
did not require image scanning, reaction time was always about the same, no matter
what the focus of the question was.
In another set of experiments, Jolicoeur and Kosslyn used a map of an island,
similar to the one presented in Figure 7.11, and again had participants imagine
the map and scan from one location to another. They led their experimenters
to expect a pattern of responses that would show a U-shaped curve, rather than
a linear function. In this study, too, responses still showed a linear relation between distance and time. They did not show the U-shaped response pattern expected by the experimenters. Thus, the expectations of the experimenters did not
influence the responses of the participants. The hypothesis regarding the functional equivalence of imagery and perception thus appears to have strong empirical support.
The debate between the propositional hypothesis and the functionalequivalence (analogical) hypothesis has been suggested to be intractable, based on
existing knowledge (Keane, 1994). For each empirical finding that supports the
view that imagery is analogous to perception, a rationalist reinterpretation of the
finding may be offered. The reinterpretation offers an alternative explanation of
the finding. Although the rationalist alternative may be a less parsimonious explanation than the empiricist explanation, the alternative cannot be refuted outright.
Therefore, the debate between the functional-equivalence view and the propositional view may boil down to a debate between empiricism and rationalism.
Johnson-Laird’s Mental Models
An alternative synthesis of the literature suggests that mental representations may
take any of three forms: propositions, images, or mental models (Johnson-Laird,
1983, 1999; Johnson-Laird & Goldvarg, 1997). Here, propositions are fully abstracted representations of meaning that are verbally expressible. The criterion of
the possibility of verbal expression distinguishes Johnson-Laird’s view from that of
other cognitive psychologists.
Mental models are knowledge structures that individuals construct to understand
and explain their experiences (Brewer, 2003; Goodwin & Johnson-Laird, 2010;
Johnson-Laird, 2001; Schaeken et al., 1996; Tversky, 2000). The models are constrained by the individuals’ implicit theories about these experiences, which can be
more or less accurate. For example, you may have a mental model to account for how
planes fly into the air. But the model depends—not on physical or other laws but
rather—on your beliefs about them. The same would apply to the creation of mental
models from text or symbolic reasoning problems as from accounts of planes flying in
the air (Byrne, 1996; Ehrlich, 1996; Garnham & Oakhill, 1996).
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“The cat is under the table” may be represented in several ways: as a proposition
(because it is verbally expressible); as an image (of a particular cat in a particular
position under a particular table); or as a mental model (of any cat and table).
Is there any proof for the use of mental models? In an experiment by Mani and
Johnson-Laird (1982), some participants received precise location information for
each object in a spatial array (determinate descriptions). Other participants received
ambiguous location information for objects in the array (indeterminate descriptions). As an analogy, consider a relatively determinate description of the location
of Washington, D. C.: It lies between Alexandria, Virginia, and Baltimore, Maryland; an indeterminate description of the location is that it lies between the Pacific
Ocean and the Atlantic Ocean. When participants were given detailed (determinate) descriptions for the spatial layout of objects, they inferred additional spatial
information not included in the descriptions, but they did not recall the verbatim
details well. For example, they could infer additional geographic information
about Washington, D. C.’s location, but they could not remember the description
word for word. Their having inferred additional spatial information suggests that
the participants formed a mental model of the information. That they then did
not recall the verbatim descriptions very well suggests that they relied on the
mental models. They did not rely on the verbal descriptions for their mental
representations.
What do you think happened when participants were given ambiguous (indeterminate) descriptions for the spatial layout of objects? They seldom inferred spatial
information not given in the descriptions, but they remembered the verbatim
descriptions better than did the other participants. The authors suggested that participants did not infer a mental model for the indeterminate descriptions because of
the multitude of possibilities for mental models of the given information. Instead,
the participants appear to have mentally represented the descriptions as verbally expressible propositions. The notion of mental models as a form of knowledge representation has been applied to a broad range of cognitive phenomena. These
phenomena include visual perception, memory, comprehension of text passages,
and reasoning (Johnson-Laird, 1983, 1989). Consider, for example, the statement:
“Some dogs are poodles.” How might you construct a mental model to represent this
statement?
Perhaps the use of mental models may offer a possible explanation of some findings that cannot be fully explained in terms of visual imagery. A series of experiments
studied people who were born blind (Kerr, 1983). Because these participants have
never experienced visual perception, we may assume that they never have formed
visual images (at least, they have not done so in the ordinary sense of the term).
Some of Kosslyn’s tasks were adapted to work comparably for sighted and for blind
participants (Kerr, 1983). For example, for a map-scanning task, the experimenter
used a board with topographical features and landmarks that could be detected by
using touch. She then asked participants to form a mental image of the board.
Kerr asked participants to imagine various common objects of various sizes.
The blind participants responded more slowly to all tasks than did the sighted
participants. But Kerr’s blind participants still showed similar response patterns to
those of sighted participants. They showed faster response times when scanning
shorter distances than when scanning longer distances. They also were faster when
answering questions about images of larger objects than about images of smaller
objects. At least in some respects, spatial imagery appears not to involve representations that are actual analogs to visual percepts.
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303
The use of haptic (touch-based) “imagery” suggests alternative modalities for
mental imagery. Haptic imagery has been explored further by a number of researchers. These researchers have found that haptic imagery shares a number of features
with visual imagery. For instance, similar brain areas are active during both types of
imagery (James et al., 2002; Zhang et al., 2004). Perhaps haptic imagery involves the
formation of a mental model that is analogous, in some respects, to visual imagery.
Imaginal representation also may occur in an auditory modality (based on hearing). As an example, investigators found that participants seem to have auditory
mental images, just as they have visual mental images (Intons-Peterson, Russell, &
Dressel, 1992). Specifically, participants took longer mentally to shift a sound upward in pitch than downward. In particular, they were slower in going from the
low-pitched purring of a cat to the high-pitched ringing of a telephone than in going
from the cat’s purring to a clock’s ticking. The relative response times were analogous to the time needed physically to change sounds up or down in pitch. Consider
what happened, in contrast, when individuals were asked to make psychophysical
judgments involving discriminations between stimuli. Participants took longer to determine whether purring was lower-pitched than was ticking (two relatively close
stimuli) than to determine whether purring was lower-pitched than was ringing
(two relatively distant stimuli). As with haptic imagery, it is easier to conceptualize
auditory imagery in terms of mental models than strictly in terms of the kinds of
pictorial mental representations of which people speak when they think of visual
imagery.
Psychophysical tests of auditory sensation and perception reveal findings analogous to the studies on auditory and haptic imagery. In another study, participants
listened to either familiar or unfamiliar songs with pieces of the song replaced with
silence. Examining the brains of these participants revealed that there was more activation of the auditory cortex during silence when the song was familiar than when
the song was unfamiliar (Kraemer et al., 2005). These findings suggest that when
one generates an auditory image, the same brain areas as those involved in hearing
are engaged.
Faulty mental models are responsible for many errors in thinking. Consider several examples (Brewer, 2003). School children tend to think of heat and cold as
moving through objects, much as fluids do. These children also believe that plants
obtain their food from the ground, and that boats made of iron should sink. Even
adults have trouble understanding the trajectory of an object dropped from a moving
airplane.
Experience is a useful tool for the repair of faulty mental models (Greene &
Azevedo, 2007). In one study, faulty mental models concerning the process of respiration were explored. A group of college students who made false predictions concerning the process of respiration participated in this study. These predictions were
based on imprecise mental models. The experimenters set up a laboratory experience
for the students to demonstrate and explore the process of respiration. One group
stated their predictions before the experiment and another did not. Overall, participating in the activity improved the accuracy of the answers of participants to questions concerning respiration, compared with performance before the activity.
However, when the students were required to state their predictions before the experiment, the improvement was even greater (Modell et al., 2000). This research
can be applied to classroom teaching. For example, if a teacher asks students to explain how they think the respiratory system works and then offers an experiment or
demonstration showing how respiration works, students who did not understand the
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process correctly are now more able, because of the activity, to correct their understanding and learn. Thus, experience can help correct faulty mental models. However, it is most helpful when the faulty models are made explicit.
In sum, mental models provide an additional means of representation in addition to propositions and visual images. They are not mutually exclusive with these
other two forms of representation, but they are complementary to them. Mental
models provide a way of explaining empirical findings, such as haptic and auditory
forms of imagery, which seem quite different from visual images.
Neuroscience: Evidence for Multiple Codes
Participants involved in a research project involving cognitive tasks can be influenced by the expectations of the researcher. But it seems implausible that such factors would equally influence the results of neuropsychological research. For
example, suppose you remembered every word in Chapter 2 regarding which particular parts of your brain govern which kinds of perceptual and cognitive functions. (This is, of course, an unlikely assumption for you or for most participants
in neuropsychological research.) How would you go about conforming to experimenters’ expectations? You would have to control directly your brain’s activities
and functions so that you would simulate what experimenters expected in association with particular perceptual or cognitive functions. Likewise, brain-damaged patients do not know that particular lesions are supposed to lead to particular kinds
of deficits. Indeed, the patients rarely know where a lesion is until after deficits are
discovered. Thus, neuropsychological findings may circumvent many issues of demand characteristics in resolving the dual-code controversy. However, this research does not eliminate experimenter biases regarding where to look for lesions
or the deficits arising from them.
Left Brain or Right Brain: Where Is Information Manipulated?
Some investigators have followed the long-standing tradition of studying patterns of
brain lesions and relating them to cognitive deficits. Initial neuropsychological research on imagery came from studies of patients with identified lesions and from
split-brain patients. Recall the Chapter 2 studies of patients who underwent surgery
that severed their right hemisphere from their left hemisphere. Researchers found
that the right hemisphere appears to represent and manipulate visuospatial knowledge in a manner similar to perception (Gazzaniga & Sperry, 1967). In contrast,
the left hemisphere appears to be more proficient in representing and manipulating
verbal and other symbol-based knowledge.
Perhaps cerebral asymmetry has evolutionary origins (Corballis, 1989). The right
hemisphere of the human brain represents knowledge in a manner that is analogous
to our physical environment. This is also the case with the brains of other animals.
Unlike the brains of other animals, however, the left hemisphere only of the human
brain has the ability to manipulate imaginal components and symbols and to
generate entirely new information (e.g., consonant and vowel sounds and geometric
shapes). For example, the word “text” as a verb did not exist just a few years ago.
Today it exists and most people know what it means, that is, to send a text message.
According to Corballis, humans alone can conceive what they have never perceived. However, a review of the findings on lateralization has led to a modified
view (Corballis, 1997). Specifically, recent neuropsychological studies of mental
Synthesizing Images and Propositions
305
rotation in both animals and humans show that both hemispheres may be partially
responsible for task performance. The apparent right-hemisphere dominance observed in humans may be the result of the overshadowing of left-hemisphere functions by linguistic abilities. Thus, it would be useful to have clear evidence of a
cerebral-hemispheric dissociation between analog imagery functions and symbolic
propositional functions. Scientists, however, will have to look deeper into brain
functioning before this issue is resolved completely.
Two Kinds of Images: Visual versus Spatial
While examining visual imagery, researchers have found that images actually may be
stored (represented) in different formats in the mind, depending on what kind of
image is involved (Farah, 1988a, 1988b; Farah et al., 1988a). Here, visual imagery
refers to the use of images that represent visual characteristics such as colors and
shapes. Spatial imagery refers to images that represent spatial features such as depth
dimensions, distances, and orientations.
Consider the case of L. H., a 36-year-old who had a head injury at age 18. The
injury resulted in lesions in the right and the left temporo-occipital regions, the right
temporal lobe, and the right inferior frontal lobe. L. H.’s injuries implicated possible
impairment of his ability to represent and manipulate both visual and spatial images.
Figure 7.12 shows those areas of L. H.’s brain where there was damage.
Figure 7.12
Damage to the Temporal Lobe.
Regions in which the brain of L. H. was damaged: the right temporal lobe and right inferior frontal
lobe, as shown in the figure at the top; and the temporo-occipital region, as shown in the figure at
the bottom.
Source: From Robert Solso, Cognitive Psychology, ed 6, p. 306. Copyright © 2000 Elsevier. Reprinted with
permission.
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Figure 7.13 L. H.’s Performance in Visual and Spatial Imagery.
L. H. was able to draw accurately various objects. Panel (a) shows what he was shown, and panel (b) shows what he
drew. However, he could not recognize the objects he copied. Despite L. H.’s severe deficits on visual-imagery tasks
[panel (c), regarding colors, sizes, shapes, etc.], L. H. showed normal ability on spatial-imagery tasks [panel (d),
regarding rotations, scanning, scaling, etc.].
Source: Reprinted from M. J. Farah, K. M. Hammond, D. N. Levine, & R. Calvanio. Visual and spatial mental imagery: Dissociable systems
of representation. Cognitive Psychology, 20, 439–462, © 1988, with permission from Elsevier.
Synthesizing Images and Propositions
307
Despite L. H.’s injuries, L. H.’s ability to see was intact. He was able satisfactorily to copy various pictures [Figure 7.13(a) and (b)]. Nonetheless, he could not recognize any of the pictures he copied. In other words, he could not link verbal labels
to the objects pictured. He performed very poorly when asked to respond verbally to
questions requiring visual imagery, such as those regarding color or shape. Surprisingly, however, L. H. showed relatively normal abilities in several kinds of tasks.
These involved: (1) rotations (2-D letters, 3-D objects); (2) mental scanning, size
scaling, matrix memory, and letter corners; and (3) state locations [Figure 7.13(c)
and (d)]. That is, his ability for several types of spatial imagery was not impaired.
This finding indicates that spatial and visual imagery may indeed be different from
each other.
Investigators have also used event-related potentials (ERP; see Chapter 2,
Table 2.3) to study visual imagery. They thereby compared brain processes associated with visual perception to brain processes associated with visual imagery (Farah
et al., 1988b). As you may recall, the primary visual cortex is located in the occipital region of the brain. During visual perception, ERPs generally are elevated in
the occipital region. If visual imagery were analogous to visual perception, we
could expect that, during tasks involving visual imagery, there would be analogous
elevations of ERPs in the occipital region.
In Farah’s study, ERPs were measured during a reading task. In one condition,
participants were asked to read a list of concrete words (e.g., cat). In the other condition, participants were asked to read a comparable list of concrete words but were
also asked to imagine the objects during reading. Each word was presented for
200 milliseconds. ERPs were recorded from the different sites in the occipital lobe
and temporal lobe regions. The researchers found that the ERPs were similar across
the two conditions during the first 450 milliseconds. After this time, however, participants in the imaginal condition showed greater neural activity in the occipital lobe
than did participants in the nonimaginal (reading-only) condition.
“Neurophysiological evidence suggests that our cognitive architecture includes
representations of both the visual appearance of objects in terms of their form,
color, and perspective, and of the spatial structure of objects in terms of their
three-dimensional layout in space” (Farah et al., 1988a, p. 459). Knowledge of
object labels (recognizing the objects by name) and attributes (answering questions
about the characteristics of the objects) taps propositional, symbolic knowledge about
the pictured objects. In contrast, the ability to manipulate the orientation (rotation)
or the size of images taps imaginal, analogous knowledge of the objects. Thus,
both sforms of representation seem to answer particular kinds of questions for knowledge use.
CONCEPT CHECK
1. Why are demand characteristics important when researchers design and interpret
experiments?
2. What kind of mental model did Johnson-Laird propose?
3. What is the difference between visual and spatial imagery?
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Spatial Cognition and Cognitive Maps
Most of the studies described thus far have involved the way in which we represent
pictorial knowledge. The studies are based on what we have perceived by looking at
and then imagining visual stimuli. Other research suggests that we may form imaginal maps based solely on our physical interactions with, and navigations through,
our physical environment. This is true even when we never have a chance to “see
the whole picture,” as from an aerial photograph or a map. Spatial cognition deals
with the acquisition, organization, and use of knowledge about objects and actions
in two- and three-dimensional space.
Cognitive maps are internal representations of our physical environment, particularly centering on spatial relationships. Cognitive maps seem to offer internal representations that simulate particular spatial features of our external environment
(Rumelhart & Norman, 1988; Wagner, 2006).
Of Rats, Bees, Pigeons, and Humans
Some of the earliest work on cognitive maps was done by Edward Tolman during
the 1930s. At this time, it was considered almost unseemly for psychologists to
try to understand cognitive processes that could not be observed or measured directly (you can’t look into a person’s head and “see” the image that person is
thinking about). In one study, the researchers were interested in the ability of
P R A C T I C A L AP P L I C A T I O N S O F C O G N I T I V E PS Y C HO L O G Y
DUAL CODES
How do you benefit from having a dual code for knowledge representation? Although
a dual code may seem redundant and inefficient, having a code for analog physical
and spatial features that is distinct from a code for symbolic propositional knowledge
actually can be very efficient. Consider how you learn material in your cognitive psychology course. Most people go to the lecture and obtain information from an instructor. They also read material from a textbook, as you are doing now. If you had only
an analog code for knowledge representation, you would have a much harder time integrating the verbal information you received from your instructor in class with the
printed information in your textbook. All your information would be in the form of
auditory-visual images gleaned from listening to and watching your instructor in class
and visual images of the words in your textbook. Thus, a symbolic code that is distinct
from the analog features of encoding is helpful for integrating across different modes of
knowledge acquisition.
Analog codes preserve important aspects of experience without interfering with underlying propositional information. For the purposes of performing well on a test, it is irrelevant whether the information was obtained in class or in the text, but later you may need
to verify the source of information to prove that your answer is correct. In this case, analogical information might help.
Television used to be analog but is now largely digital. What are the advantages of
digital television? Are there any potential disadvantages?
Spatial Cognition and Cognitive Maps
309
One-way door
Curtain
Start
box
Figure 7.14
Food
box
Research on Mental Imagery in Rats.
Edward Tolman found that rats seemed to have formed a mental map of a maze during behavioral experiments.
rats to learn a maze (Figure 7.14) (Tolman & Honzik, 1930). The rats were divided into three groups:
1. In the first group, the rats had to learn the maze. Their reward for getting from
the start box to the end box was food. Eventually, these rats learned to run the
maze without making any errors. In other words, they did not make wrong turns
or follow blind alleys.
2. A second group of rats also was placed in the maze, but these rats received no
reinforcement for successfully getting to the end box. Although their performance improved over time, they continued to make more errors than the reinforced group. These results are hardly surprising. We would expect the rewarded
group to have more incentive to learn.
3. The third group of rats received no reward for 10 days of learning trials. On the
11th day, however, food was placed in the end box for the first time. With just
one reinforcement, the learning of these rats improved dramatically. These rats
ran the maze about as well in fewer trials as the rats in the first group.
What, exactly, were the rats in Tolman and Honzik’s experiment learning? It
seems unlikely that they were learning simply “turn right here, turn left there,” and
so on. According to Tolman, the rats were learning a cognitive map, an internal
representation of the maze. Through this argument, Tolman became one of the earliest cognitive theorists. He argued for the importance of the mental representations
that give rise to behavior.
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Decades later, even very simple creatures were to appear able to form some
cognitive maps. These creatures may be able to translate imaginal representations
into a primitive, prewired, analogical, and perhaps even symbolic form. For example,
a Nobel Prize–winning German scientist studied the behavior of bees when they return to their hive after having located a source of nectar (von Frisch, 1962, 1967).
Apparently, bees not only can form imaginal maps for getting to food sources,
they also can use a somewhat symbolic form for communicating that information
to other bees. Specifically, different patterns of dances can be used to represent different meanings. For example, a round dance indicates a source less than 100 yards
from the hive. A figure-eight dance indicates a source at a greater distance. The
details of the dance (e.g., in regard to wiggle patterns) differ from one species to
another, but the basic dances appear to be the same across all species of bees. If
the lowly bee appears able to imagine the route to nectar, what kinds of cognitive
maps may be conceived in the minds of humans?
Homing pigeons are noted for their excellent cognitive maps. These birds are
known for their ability to return to their home from distant locations. This quality
made the birds useful for communication in ancient times and even in the 19th and
20th centuries. Extensive research has been completed on how pigeons form these
maps. The left hippocampus plays a pivotal role in map formation. When the left
hippocampus is lesioned, pigeons’ ability to return to their homes is impaired. However, lesioning just any part of the hippocampus already impairs homing performance
(Gagliardo et al., 2001, 2009). The left hippocampus is also crucial for the perception of landmarks within the environment (Bingman et al., 2003).
Other research suggests that the right hippocampus is involved in sensitivity to
global features of the environment (e.g., geometry of the space). The hippocampus is
involved in the formation of cognitive maps in humans as well (Iaria, 2008; Maguire, Frackowiak, & Frith, 1996).
Humans seem to use three types of knowledge when forming and using cognitive maps:
1. Landmark knowledge is information about particular features at a location and
which may be based on both imaginal and propositional representations
(Thorndyke, 1981).
2. Route-road knowledge involves specific pathways for moving from one location
to another (Thorndyke & Hayes-Roth, 1982). It may be based on both procedural knowledge and declarative knowledge.
3. Survey knowledge involves estimated distances between landmarks, much as they
might appear on survey maps (Thorndyke & Hayes-Roth, 1982). It may be represented imaginally or propositionally (e.g., in numerically specified distances).
Thus, people use both an analogical code and a propositional code for
imaginal representations such as images of maps (McNamara, Hardy, & Hirtle,
1989; Russell & Ward, 1982).
Rules of Thumb for Using Our Mental Maps: Heuristics
When we use landmark, route-road, and survey knowledge, we sometimes use rules
of thumb that influence our estimations of distance. These rules of thumb are cognitive strategies termed heuristics. For example, in regard to landmark knowledge, the
density of the landmarks sometimes appears to affect our mental image of an area.
Spatial Cognition and Cognitive Maps
311
n BELIEVE IT OR NOT
MEMORY TEST? DON’T COMPETE
WITH
CHIMPANZEES!
Can you believe that chimpanzees’ working memory for
numbers is actually better than that of humans? Japanese
researchers taught chimpanzees the numerals from 1 to 9.
Then they devised experiments that displayed a number
scattered on a touch screen. After a particular time interval, the numbers were replaced by white squares. Then,
chimpanzees and human subjects had to touch the white
squares in ascending numerical sequence. Young chimpanzees outperformed humans, both in speed and accuracy, suggesting that chimpanzees might actually have
what is often called a photographic memory (Inoue &
Matsuzawa, 2007).
As the density of intervening landmarks increases, estimates of distances increase
correspondingly. Using this rule of thumb distorts people’s mental images, however.
The more landmarks there are, the larger the distance they estimate (Thorndyke,
1981). It has also been shown that people estimate the distance between two places
to be shorter when traveling to a landmark than when traveling to a nonlandmark.
That is, if you’re traveling from a small town to the major city, the distance may
seem smaller to you than when you’re traveling from the big city to the small town
(Tversky, 2005; Wagner, 2006).
In estimations of distances between particular physical locations (e.g., cities),
route-road knowledge appears often to be weighted more heavily than survey knowledge. This is true even when participants form a mental image based on looking at a
map (McNamara, Ratcliff, & McKoon, 1984). Consider what happened when participants were asked to indicate whether particular cities had appeared on a map.
They showed more rapid response times between names of cities when the two cities
were closer together in route-road distance than when the two cities were physically
closer together “as the crow flies” (Figure 7.15).
Califordiego
Schmooville
Sturnburg
Schmeeville
Figure 7.15
Mental Maps.
Which city is closer to Sturnburg, Schmeeville or Schmooville? It appears that our use of cognitive maps often emphasizes the use of route-road knowledge, even when it contradicts survey knowledge.
Source: Based on Timothy R. McNamara, Roger Ratcliff, and Gail McKoon (1984), “The Mental Representation of Knowledge Acquired from Maps,” Journal of Experimental Psychology: LMC, 10(4), 723–732. Copyright
© 1984 by the American Psychological Association.
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
The use of heuristics in manipulating cognitive maps suggests that propositional knowledge affects imaginal knowledge (Tversky, 1981). This is so at least
when people are solving problems and answering questions about images. In some
situations, conceptual information seems to distort mental images. In these situations, propositional strategies may better explain people’s responses than strategies that are based on a mental image. For example, a study by Friedman and
Brown (2000, see also Friedman et al., 2002 and Friedman & Montello, 2006)
showed that when participants had to place cities on a map, those cities were
clustered according to conceptual information like climate. The distortions seem
to reflect a tendency to regularize features of mental maps. Thus, angles, lines,
and shapes are represented as more like pure abstract geometric forms than they
really are. Here are some examples:
1. Right-angle bias: People tend to think of intersections (e.g., street crossings) as
forming 90-degree angles more often than the intersections really do (Moar &
Bower, 1983; Smith & Cohen, 2008).
2. Symmetry heuristic: People tend to think of shapes (e.g., states or countries) as
being more symmetrical than they really are (Montello et al., 2004; Tversky &
Schiano, 1989).
3. Rotation heuristic: When representing figures and boundaries that are slightly
slanted (i.e., oblique), people tend to distort the images as being either more
vertical or more horizontal than they really are (Tversky, 1981, 1991; Wagner,
2006).
4. Alignment heuristic: People tend to represent landmarks and boundaries that are
slightly out of alignment by distorting their mental images to be better aligned
than they really are (i.e., we distort the way we line up a series of figures or
objects; Tversky, 1981, 1991).
5. Relative-position heuristic: The relative positions of particular landmarks and
boundaries is distorted in mental images in ways that more accurately reflect
people’s conceptual knowledge about the contexts in which the landmarks and
boundaries are located, rather than reflecting the actual spatial configurations
(Seizova-Cajic, 2003).
To see how the relative-position heuristic might work, close your eyes and picture a map of the United States. Is Reno, Nevada, west of San Diego, California, or
east of it? In a series of experiments, investigators asked participants questions such
as this one (Stevens & Coupe, 1978). They found that the large majority of people
believe San Diego to be west of Reno. That is, for most of us, our mental map looks
something like that in panel (a) of Figure 7.16. Actually, however, Reno is west of
San Diego. See the correct map in panel (b) of Figure 7.16.
Some of these heuristics also affect our perception of space and of forms
(Chapter 3). For example, the symmetry heuristic seems to be equally strong in
memory and in perception (Tversky, 1991). Nonetheless, there are differences
between perceptual processes and representational (imaginal or propositional) processes. For example, the relative-position heuristic appears to influence mental representation much more strongly than it does perception (Tversky, 1991).
Semantic or propositional knowledge (or beliefs) can also influence our imaginal
representations of world maps (Saarinen, 1987b, see also Louwerse & Zwaan, 2009).
Specifically, students from 71 sites in 49 countries were asked to draw a sketch map
of the world. Most students (even Asians) drew maps showing a Eurocentric view of
Spatial Cognition and Cognitive Maps
313
NEVADA
Reno
San Francisco
CALIFORNIA
San Diego
(a)
NEVADA
Reno
San Francisco
CALIFORNIA
San
Diego
(b)
Figure 7.16
The Relative Position Heuristic.
Which of these two maps (a) or (b) more accurately depicts the relative positions of Reno,
Nevada, and San Diego, California?
the world. Many Americans drew Americentric views. A few others showed views
centered on and highlighting their own countries. (Figure 7.17 shows an
Australian-centered view of the world.) In addition, most students showed modest
distortions that enlarged the more prominent, well-known countries. They also
diminished the sizes of less well-known countries (e.g., in Africa).
Finally, further work suggests that propositional knowledge about semantic categories may affect imaginal representations of maps. In one study, the researchers
studied the influence of semantic clustering on estimations of distances (Hirtle &
Mascolo, 1986). Hirtle’s participants were shown a map of many buildings and
then were asked to estimate distances between various pairs of buildings. They
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
INVESTIGATING COGNITIVE PSYCHOLOGY
Mental Maps
Which is larger in land area, India or Germany? If you are used to seeing the world in
terms of the popular Mercator map, in which the map is flat and the equator is in the
bottom half of the map, you might think that India and Germany are about the same
size. In fact, you might think that Germany may be a bit larger than India.
Now look at a globe of the world. You will see that India is actually about five times
as large as Germany. This is an example of how our cognitive maps may be based not in
reality, but rather in our exposure to the topic and to our constructions and heuristics.
tended to distort the distances in the direction of guessing shorter distances for more
similar landmarks and longer distances for less similar landmarks. Investigators found
similar distortions in students’ mental maps for the city in which they lived (Ann
Arbor, Michigan) (Hirtle & Jonides, 1985).
The work on cognitive maps shows once again how the study of mental imagery
can help elucidate our understanding of human adaptation to the environment—
that is, of human intelligence. To survive, we need to find our way around the environment in which we live. We need to get from one place to another. Sometimes, to
get between places, we need to imagine the route we will need to traverse. Mental
imagery provides a key basis for this adaptation. In some societies (Gladwin, 1970),
the ability to navigate with the help of very few cues is a life-or-death issue. If sailors
cannot do so, they eventually get lost and potentially die of dehydration or starvation. Thus, our imagery abilities are potential keys to our survival and to what makes
us intelligent in our everyday lives.
Creating Maps from What You Hear: Text Maps
We have discussed the construction of cognitive maps based on procedural knowledge (e.g., following a particular route, as a rat in a maze), propositional information
(e.g., using mental heuristics), and observation of a graphic map. In addition, we
may be able to create cognitive maps from a verbal description (Taylor & Tversky,
1992a, 1992b; Tversky, 2005). These cognitive maps may be as accurate as those
created from looking at a graphic map. Others have found similar results in studies
of text comprehension (Glenberg, Meyer, & Lindem, 1987).
Tversky noted that her research involved having the readers envision themselves in an imaginal setting as participants, not as observers, in the scene. She
wondered whether people might create and manipulate images differently when
envisioning themselves in different settings. Specifically, Tversky wondered
whether propositional information might play a stronger role in mental operations when we think about settings in which we are participants, as compared
with settings in which we are observers. As Item 4 in Table 7.3 indicates, the
findings regarding cognitive maps suggest that the construction of mental imagery
may involve both—processes analogous to perception, and processes relying on
propositional representations.
Whether the debate regarding propositions versus imagery can be resolved in the
terms in which it traditionally has been presented remains unclear. The various forms
of mental representation sometimes are considered to be mutually exclusive. In other
Spatial Cognition and Cognitive Maps
315
Text not available due to copyright restrictions
words, we think in terms of the question, “Which representation of information is correct?” Often, however, we create false dichotomies. We suggest that alternatives are
mutually exclusive, when, in fact, they might be complementary. For example, models
postulating mental imagery and those positing propositions can be seen as opposed to
each other. However, this opposition is not necessary. Rather, it is in our construction
of a relation. People possibly could use both representations. Propositional theorists
might like to believe that all representations are fundamentally propositional. Quite
possibly, though, both images and propositions are way stations toward some more basic
and primitive form of representation in the mind of which we do not yet have any
knowledge. A good case can be made in favor of both propositional and imaginal representations of knowledge. Neither is necessarily more basic than the other. The question
we presently need to address is when we use which.
CONCEPT CHECK
1. What is a cognitive map?
2. Name some heuristics that people use when manipulating cognitive maps.
3. What is a text map?
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Key Themes
This chapter illustrates some of the key themes mentioned in Chapter 1.
Structures versus processes. The debate regarding whether images are phenomenal or epiphenomenal hinges upon what kinds of mental structures are used to process stimuli. For example, when people mentally rotate objects, is the structural
representation imaginal or propositional? Either kind of mental representation could
generate processes that would enable people to see objects at different angular viewpoints. But the kinds of processes would be different—either mental manipulation of
images or mental manipulation of propositions. In order to understand cognition, we
need to understand how structures and processes interact.
Validity of causal inferences versus ecological validity. Suppose you wish to
hire air-traffic controllers. Can you assess their mental-imagery and spatialvisualization skills using paper-and-pencil tests of manipulation of geometric forms?
Or do you need to test them in a setting that is more similar to that of air-traffic
control, as through a simulation of the actual job? The paper-and-pencil test probably will yield more precise measurements, but will these measurements be valid?
There is no final answer to the question. Researchers are studying this kind of question in order to understand how best to assess people’s real-life skills.
Biological and behavioral methods. Early work by Stephen Kosslyn and his collaborators was all behavioral. The researchers investigated how people mentally manipulate various kinds of images. As time went by, the team started using biological
techniques, such as fMRI to supplement their behavioral studies. But they never saw
the two kinds of research as in opposition to each other. Rather, they viewed them as
wholly complementary, and do even today.
Summary
1. What are some of the major hypotheses regarding how knowledge is represented in the
mind? Knowledge representation comprises the
various ways in which our minds create and
modify mental structures that stand for what
we know about the world outside our minds.
Knowledge representation involves both declarative (knowing that) and nondeclarative
(knowing how) forms of knowledge. Through
mental imagery, we create analog mental
structures that stand for things that are not
presently being sensed in the sense organs.
Imagery may involve any of the senses, but
the form of imagery most commonly reported
by laypeople and most commonly studied by
cognitive psychologists is visual imagery.
Some studies (e.g., studies of blind participants
and some studies of the brain) suggest that
visual imagery itself may comprise two discrete
systems of mental representation: One system
involves nonspatial visual attributes, such as
color and shape; another involves spatial
attributes, such as location, orientation, and
size or distance scaling.
According to Paivio’s dual-code hypothesis,
two discrete mental codes for representing
knowledge exist. One code is for images and
another for words and other symbols. Images
are represented in a form analogous to the
form we perceive through our senses. In contrast, words and concepts are encoded in a symbolic form, which is not analogical.
An alternative view of image representation is
the propositional hypothesis. It suggests that both
images and words are represented in a propositional form. The proposition retains the underlying meaning of either images or words, without
any of the perceptual features of either. For example, the acoustic features of the sounds of the
words are not stored, nor are the visual features of
the colors or shapes of the images. Furthermore,
propositional codes, more than imaginal codes,
seem to influence mental representation when
participants are shown ambiguous or abstract
Summary
figures. Apparently, unless the context facilitates
performance, the use of visual images does not
always readily lead to successful performance on
some tasks requiring mental manipulations of
either abstract figures or ambiguous figures.
2. What are some of the characteristics of mental
imagery? Based on a modification of the dualcode view, Shepard and others have espoused a
functional-equivalence hypothesis. It asserts
that images are represented in a form functionally equivalent to percepts, even if the images
are not truly identical to percepts. Studies of
mental rotations, image scaling, and image
scanning suggest that imaginal task performance is functionally equivalent to perceptual
task performance. Even performance on some
tasks involving comparisons of auditory images
seems to be functionally equivalent to performance on tasks involving comparisons of auditory percepts.
Propositional codes seem less likely to influence mental representation than imaginal ones
when participants are given an opportunity to
create their own mental images. For example,
they might do so in tasks involving image sizing
or mental combinations of imaginal letters.
Some researchers have suggested that experimenter expectancies may have influenced
cognitive studies of imagery, but others have
refuted these suggestions. In any case, neuropsychological studies are not subject to such influences. They seem to support the functionalequivalence hypothesis by finding overlapping
brain areas involved in visual perception and
mental rotation.
3. How does knowledge representation benefit
from both images and propositions? Kosslyn
has synthesized these various hypotheses to suggest that images may involve both analogous
and propositional forms of knowledge representation. In this case, both forms influence our
mental representation and manipulation of
images. Thus, some of what we know about
images is represented in a form that is analogous
to perception. Other things we know about
images are represented in a propositional form.
Johnson-Laird has proposed an alternative
317
synthesis. He has suggested that knowledge
may be represented as verbally expressible propositions, as somewhat abstracted analogical
mental models, or as highly concrete and analogical mental images.
Studies of split-brain patients and patients
with lesions indicate some tendency toward
hemispheric specialization. Visuospatial information may be processed primarily in the right
hemisphere. Linguistic (symbolic) information
may be processed primarily in the left hemisphere of right-handed individuals. A case study
suggests that spatial imagery also may be processed in a different region of the brain than
the regions in which other aspects of visual imagery are processed. Studies of normal participants show that visual-perception tasks seem to
involve regions of the brain similar to the regions involved in visual-imagery tasks.
4. How may conceptual knowledge and expectancies influence the way we use images?
People tend to distort their own mental maps
in ways that regularize many features of the
maps. For example, they may tend to imagine
right angles, symmetrical forms, either vertical
or horizontal boundaries (not oblique ones),
and well-aligned figures and objects. People
also tend to employ distortions of their mental
maps in ways that support their propositional
knowledge about various landmarks.
They tend to cluster similar landmarks, to
segregate dissimilar ones, and to modify relative
positions to agree with conceptual knowledge
about the landmarks. In addition, people tend
to distort their mental maps. They increase
their estimates regarding the distances between
endpoints as the density of intervening landmarks increases.
Some of the heuristics that affect cognitive
maps support the notion that propositional information influences imaginal representations.
The influence of propositional information
may be particularly potent when participants
are not shown a graphic map. Instead, they
are asked to read a narrative passage and to
envision themselves as participants in a setting
described in the narrative.
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CHAPTER 7 • The Landscape of Memory: Mental Images, Maps, and Propositions
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe some of the characteristics of pictures
versus words as external forms of knowledge
representation.
2. What factors might lead a person’s mental
model to be inaccurate with respect to how radio transmissions lead people to be able to hear
music on a radio?
3. In what ways is mental imagery analogous (or
functionally equivalent) to perception?
4. In what ways do propositional forms of knowledge representation influence performance on
tasks involving mental imagery?
5. What are some strengths and weaknesses of ERP
studies?
6. Some people report never experiencing mental
imagery, yet they are able to solve mentalrotation problems. How might they solve such
problems?
7. What are some practical applications of
having two codes for knowledge representation?
Give an example applied to your own experiences, such as applications to studying for
examinations.
8. Based on the heuristics described in this chapter, what are some of the distortions that may be
influencing your cognitive maps for places with
which you are familiar (e.g., a college campus or
your hometown)?
Key Terms
analog codes, p. 277
cognitive maps, p. 308
declarative knowledge, p. 271
dual-code theory, p. 277
functional-equivalence
hypothesis, p. 287
heuristics, p. 310
imagery, p. 276
knowledge representation, p. 271
mental models, p. 301
mental rotation, p. 289
procedural knowledge, p. 271
propositional theory, p. 281
spatial cognition, p. 308
symbolic representation, p. 274
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and
more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Mental Rotation
Link Word
Mental Scanning
8
C
H
A
P
T
E
R
The Organization of Knowledge
in the Mind
CHAPTER OUTLINE
Declarative versus Procedural Knowledge
Organization of Declarative Knowledge
Concepts and Categories
Feature-Based Categories: A Defining View
Prototype Theory: A Characteristic View
A Synthesis: Combining Feature-Based
and Prototype Theories
Theory-Based View of Categorization
Intelligence and Concepts in Different Cultures
Semantic-Network Models
Collins and Quillian’s Network Model
Comparing Semantic Features
Schematic Representations
Schemas
Scripts
Representations of How We Do Things:
Procedural Knowledge
The “Production” of Procedural Knowledge
Nondeclarative Knowledge
Integrative Models for Representing
Declarative and Nondeclarative
Knowledge
Combining Representations: ACT-R
Declarative Knowledge within ACT-R
Procedural Knowledge within ACT-R
Parallel Processing: The Connectionist Model
How the PDP Model Works
Criticisms of the Connectionist Models
Comparing Connectionist with Network
Representations
How Domain General or Domain Specific
Is Cognition?
Key Themes
Summary
Thinking about Thinking: Analytical,
Creative, and Practical Questions
Key Terms
Media Resources
319
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CHAPTER 8 • The Organization of Knowledge in the Mind
Here are some of the questions we will explore in this chapter:
1. How are representations of words and symbols organized in the mind?
2. How do we represent other forms of knowledge in the mind?
3. How does declarative knowledge interact with procedural knowledge?
n BELIEVE IT OR NOT
THERE IS
A
SAVANT
IN
ALL
OF
US
People with autism who have an extraordinary ability
have been called autistic savants. Their abilities often
leave us incredulous—they can multiply large numbers
within a fraction of a second, remember huge amounts
of data, or they can recall any detail with their photographic memory. But people who are autistic savants
may actually not be that different from us.
Research suggests that we may all possess these
talents, but they are part of low-level information processing that we normally do not use because we think
at a higher level that is concept-driven and allows for
multisensory comparisons. For people who are autistic
savants, this low-level processing comes automatic
and naturally. Although we usually cannot consciously
control our brain activity, studies have shown that people can learn to become sensitive to low-level processing and gain access to those early states of
processing that are usually unconscious. This opens
new possibilities for behavior and self-awareness
(Birbaumer, 1999).
In this chapter we’ll learn about how we organize
concepts in our minds and how these concepts help us
think and to organize what we know.
John and Simon were college roommates and planned a trip to Arizona during
spring break. They would be hiking through the remote Spikeleaf Canyon that
hardly has been explored, was narrow, and had lots of pools in which the water collects and smooth rock slides that connect the pools. Once they arrived at the canyon, they parked their car and began the hike to the edge, and from there followed a
steep path down to the bottom. When they were almost at the bottom of the canyon, Simon suddenly tripped, fell over, and tumbled down the remainder of the
steep slope. He was unable to stand up and feared he may have broken his ankle.
Simon was in excruciating pain. John could not help him climb back up the narrow
path, and because they were in such a remote desert area, they did not have any cell
phone reception. John raced back the way they had come, got in the car, and frantically drove about half an hour until his cell phone worked so he could call for
help. Eventually, a rescue team arrived at the canyon and carried Simon back up
the canyon so he could receive treatment in the nearest hospital.
This story, which sounds just like an adventure story, actually raises a number
of questions relevant to cognitive psychology. John was panicked when he had to
leave Simon behind and could not call for help immediately, and yet he managed
to drive his car although his thoughts were completely elsewhere. How did he do
that? Fortunately, his procedural knowledge of how to drive a car was so good that
he was able to drive automatically and did not have to concentrate on any details.
He also was worried because canyons can get flooded quickly if it rains in a distant
area upriver. Such flooding would be very dangerous for his immobile friend. Therefore, John knew that he had to act fast, and he also knew how to make his cell
phone work again and which number to call to get help when the phone started
working.
Declarative versus Procedural Knowledge
321
Declarative versus Procedural Knowledge
The preceding chapter described how knowledge may be represented in the form of
propositions and images. In this chapter, we explore how our knowledge can be organized so we can retrieve it when we need it. We expand this discussion to include
various means of organizing declarative knowledge that can be expressed in words
and other symbols (i.e., “knowing that”). John knew he had to call 9-1-1, and that
to do so he would need to get into an area with cell phone reception. Consider your
own knowledge of facts about cognitive psychology, about world history, about your
personal history, and about mathematics. Your knowledge in these areas relies on
your mental organization of declarative knowledge.
In addition, this chapter describes a few of the models for representing procedural knowledge. This is knowledge about how to follow procedural steps for performing actions (i.e., “knowing how”). For example, your knowledge of how to
drive a car, how to write your signature, how to ride a bicycle to the nearest grocery
store, and how to catch a ball depends on your mental representation of procedural
knowledge. Some theorists even have suggested integrative models for representing
both declarative and procedural knowledge.
To get an idea of how declarative and procedural knowledge may interact, get
some scrap paper and a pen or pencil. Try the demonstration in Investigating Cognitive Psychology: Testing Your Declarative and Procedural Knowledge.
In addition to seeking to understand the what (the form or structure) of knowledge representation, cognitive psychologists also try to grasp the how (the processes)
of knowledge representation and manipulation. Here are some of the questions we
explore in this chapter:
• What are some of the general processes by which we select and control the disorganized array of raw data available to us through our sense organs?
• How do we relate that sensory information to the information we have available from internal sources of information (i.e., our memories and our thought
processes)?
• How do we organize and reorganize our mental representations during various
cognitive processes?
INVESTIGATING COGNITIVE PSYCHOLOGY
Testing Your Declarative and Procedural Knowledge
As quickly and as legibly as possible, write your normal signature, from the first letter of
your first name to the last letter of your last name. Don’t stop to think about which letters
come next. Just write as quickly as possible.
Turn the paper over. As quickly and as legibly as possible, write your signature
backward. Start with the last letter of your last name and work toward the first letter of
your first name.
Now, compare the two signatures. Which signature was more easily and accurately created?
For both signatures, you had available extensive declarative knowledge of which
letters preceded or followed one another. But for the first task, you also could call on
procedural knowledge, based on years of knowing how to sign your name.
322
CHAPTER 8 • The Organization of Knowledge in the Mind
• Through what mental processes do we operate on the knowledge we have in our
minds?
• To what extent are these processes domain general—common to multiple kinds
of information, such as verbal and quantitative information?
• Conversely, to what extent are these processes domain specific—used only for
particular kinds of information, such as verbal or quantitative information?
Knowledge representation and processing have been investigated by researchers
from several disciplines. Among these researchers are cognitive psychologists, neuropsychologists, and computer scientists studying AI (artificial intelligence), which
attempts to program machines to perform intelligently. The diverse approaches that
researchers take when investigating knowledge representation promote exploration
of a wide range of phenomena. They also encourage multiple perspectives of similar
phenomena. Finally, they offer the strength of converging operations—the use of
multiple approaches and techniques to address a problem.
Other than to satisfy their own idle curiosity, why do so many researchers want
to understand how knowledge is represented? The way in which knowledge is represented profoundly influences how effectively knowledge can be manipulated for performing any number of cognitive tasks. To illustrate the influence of knowledge
representation through a very crude analogy, try the following multiplication task
using a representation in either Roman or Arabic numerals:
CMLIX
LVIII
959
58
The two multiplication tasks are exactly the same, but representation in Roman
numerals probably makes it much harder for you to compute the solution, doesn’t it?
In this chapter, we first have a closer look at how declarative knowledge (concepts) is organized in our minds. We consider theories of how concepts can be
grouped into categories as well as how they can be organized by means of semantic
networks and schemas. Then we move on to the representation of procedural knowledge. And finally, we will explore models that try to combine the representation of
declarative and procedural knowledge.
Organization of Declarative Knowledge
The fundamental unit of symbolic knowledge (knowledge of correspondence
between symbols and their meaning, for example, that the symbol “3” means three)
is the concept—an idea about something that provides a means of understanding
the world (Bruner, Goodnow, & Austin, 1956; Kruschke, 2003; Love, 2003).
Often, a concept may be captured in a single word, such as apple. Each concept
in turn relates to other concepts, such as apple, which relates to redness, roundness,
or fruit.
As you can imagine, people amass a large number of concepts over the course of
their lives. How do they organize all those concepts? One way to organize them is by
means of categories. A category is a group of items into which different objects or
concepts can be placed that belong together because they share some common features, or because they are all similar to a certain prototype. For example, the word
apple can act as a category, as in a collection of different kinds of apples. But it also
can act as a concept within the category fruit. In the following sections, we will
Organization of Declarative Knowledge
323
discuss ways to organize concepts into categories. These ways include the use of defining features, prototypes, and exemplars.
Later, we will explore how concepts can be organized by means of hierarchically
organized semantic networks, as well as schemas, which are mental frameworks of
knowledge that encompass a number of interrelated concepts (Bartlett, 1932;
Brewer, 1999).
Concepts and Categories
Concepts and categories can be divided in various ways. One commonly used distinction is between natural categories and artifact categories (Kalenine et al., 2009;
Medin, Lynch, & Solomon, 2000). Natural categories are groupings that occur naturally in the world, like birds or trees. Artifact categories are groupings that are designed or invented by humans to serve particular purposes or functions. Examples of
artifact categories are automobiles and kitchen appliances. The speed it takes to assign objects to categories seems to be about the same for both natural and artifact
categories (VanRullen & Thorpe, 2001). Natural and artifact categories are relatively stable and people tend to agree on criteria for membership in them. A tiger
is always a mammal, for example; and a knife is always an implement used for
cutting.
Concepts, on the contrary, are not always stable but can change (Dunbar, 2003;
Thagard, 2003). Some categories are created just for the moment or for a specific
purpose, for example, “things you can write on.” These categories are called ad hoc
categories (Barsalou, 1983; Little, Lewandowsky, & Heit, 2006). They are described
not in words but rather in phrases. Their content varies, depending on the context.
People in rural Uganda will probably name different things that you can write on
than will urban Americans or Inuit Eskimos.
Concepts are also used in other areas like computer science. Developers try to
develop algorithms that define “spam” so that email programs can filter out unwanted messages and your mailbox is not flooded with them. However, spammers
change the nature of their messages on a regular basis so that it is hard to create an
algorithm that can catch all spam messages and can do so on a permanent basis
(Fdez-Riverola, 2007).
Concepts appear to have a basic level (sometimes termed a natural level) of
specificity, a level within a hierarchy that is preferred to other levels (Medin, Proffitt,
& Schwartz, 2000; Rosch, 1978). Suppose I show you a red, roundish edible object
that has a stem and that came from a tree. You might characterize it as a fruit, an
apple, a delicious apple, a Red Delicious apple, and so on. Most people, however,
would characterize the object as an apple. The basic, preferred level is apple. In general, the basic level is neither the most abstract nor the most specific. Of course, this
basic level can be manipulated by context or expertise (Tanaka & Taylor, 1991).
Suppose the object were held up at a fruit stand that sold only apples. You might
describe it as a Red Delicious apple to distinguish it from the other apples around it.
How can we tell what the basic level is? Why is the basic level the apple, rather
than Red Delicious apple or fruit? Or why is it cow, rather than mammal or Guernsey?
Perhaps the basic level is the one that has the largest number of distinctive features
that set it off from other concepts at the same level (Rosch et al., 1976). Thus, most
of us would find more distinguishing features between an apple and a cow, say, than
between a Red Delicious apple and a Pippin apple. Similarly, we would find few distinguishing features between a Guernsey cow and a Holstein cow. Again, not
324
CHAPTER 8 • The Organization of Knowledge in the Mind
everyone necessarily would have the same basic level, as in the case of farmers. For
our purposes, the basic level is the one that most people find to be maximally distinctive. By means of training, the basic level can be shifted to a more subordinate
level (Scott et al., 2008). For example, the more a person learns about cars, the
more he or she is likely to make elaborate distinctions among cars. Research suggests
that the differences between experts and novices are not due to qualitatively different mechanisms but rather to quantitative differences in processing efficacy (Palmeri
2004; see also Mack et al., 2009).
When people are shown pictures of objects, they identify the objects at a basic
level more quickly than they identify objects at higher or lower levels (Rosch et al.,
1976). Objects appear to be recognized first in terms of their basic level. Only afterward are they classified in terms of higher- or lower-level categories. Thus, the picture of the roundish red, edible object from a tree probably first would be identified
as an apple. Only then, if necessary, would it be identified as a fruit or a Red Delicious apple.
Now, how do people decide what objects to put into a category? There are several theories that try to explain this process. One theory suggests that we put an object only in one category if it has several defining features. Another approach
proposes that we compare an object with an averaged representation (a prototype)
to decide whether it fits into a category. Yet another is that people can categorize
objects based on their own theories about those objects. We will explore these approaches in the next sections.
Feature-Based Categories: A Defining View
The classic view of categories disassembles a concept into a set of featural components. All those features are then necessary (and sufficient) to define the category
(Katz, 1972; Katz & Fodor, 1963). This means that each feature is an essential element of the category. Together, the features uniquely define the category; they are
defining features (or necessary attributes): For a thing to be an X, it must have that
feature. Otherwise, it is not an “X.”
Consider the term bachelor. In addition to being human, a bachelor can be
viewed as comprising three features: male, unmarried, and adult. The features are
each singly necessary. If one feature is absent, the object cannot belong to the category. Thus, an unmarried male who is not an adult would not be a bachelor. We
would not refer to a 12-year-old unmarried boy as a bachelor, because he is not an
adult. Nor would we refer to just any male adult as a bachelor. If he is married, he is
out of the running. An unmarried female adult is not a bachelor, either.
Moreover, the three features are jointly sufficient. If a person has all three features, then he is automatically a bachelor. According to this view, you cannot be
male, unmarried, and an adult, and at the same time not be a bachelor. The
feature-based view applies to more than bachelorhood, of course. For example, the
term wife is made up of the features married, female, and adult. Husband comprises
the features married, male, and adult.
The feature-based view is especially common among linguists, those who study
language (Clark & Clark, 1977; Finley & Badecker, 2009). This view is attractive
because it makes categories appear so orderly and systematic. Unfortunately, it does
not work as well as it appears to at first glance. Some categories do not readily lend
themselves to featural analysis. Game is one such category. Finding anything at all
that is a common feature of all games is actually difficult to do (Wittgenstein,
1953). Some are fun; some are not. Some involve multiple players; others, such as
Organization of Declarative Knowledge
325
solitaire, do not. Some are competitive; others, such as children’s circle games (e.g.,
ring-around-the-rosy), are not. The more you consider the concept of a game, the
more you begin to wonder whether there is anything at all that holds the category
together. It is not clear that there are any defining features of a game at all. Nonetheless, we all know what we mean, or think we do, by the word game.
Another problem with the feature-based view is that a violation of those defining features does not seem to change the category we use to define them. Consider a
zebra (see Keil, 1989). Now suppose that someone painted a zebra all black. It would
then be missing the critical attribute of stripes, but we still would call it a zebra. We
run into the same problem with birds. We might think of the ability to fly as critical
to being a bird. But certainly we would agree that a robin whose wings have been
clipped is still a robin. So is an ostrich, which does not fly.
The examples of the robin and the ostrich point out another problem with the
feature-based theory. Both a robin and an ostrich share the same defining features of
birds. They are, therefore, birds. However, loosely speaking, a robin seems somehow
to be a better example of a bird than is an ostrich. Indeed, when people are asked to
rate the typicality of a robin versus an ostrich as a bird, the robin virtually always will
get a higher rating than the latter (Malt & Smith, 1984; Mervis, Catlin, & Rosch,
1976; Rosch, 1975). Children learn typical instances of a category earlier than they
learn atypical ones (Rosch, 1978). Table 8.1 shows some ratings of typicality for various instances of birds (Malt & Smith, 1984). Clearly, there are enormous differences,
although the defining features are the same. On the 7-point scale used by Malt and
Smith for ratings of the typicality of birds, bat received a rating of 1.53. This rating is
despite the fact that a bat, strictly speaking, is not even a bird at all.
In sum, the feature-based theory has some attractive features, but it does not
give a complete account of categories. Some specific examples of a category such as
bird seem to be better examples than others. Yet, they all have the same defining
features. However, the various examples may be differentially typical of the category
of birds. Thus, we need a theory of knowledge representation that better characterizes how people truly represent knowledge.
Prototype Theory: A Characteristic View
Prototype theory takes a different approach: grouping things together not by their
defining features but rather by their similarity to an averaged model of the category.
Table 8.1
Typicality Ratings for Birds
Barbara Malt and Edward Smith (1984) found enormous differences in the typicality ratings
for various instances of birds (or bird-like animals). (After Malt & Smith, 1984.)
Bird
Rating*
Bird
Rating
Robin
6.89
Sandpiper
4.47
Seagull
6.26
Chicken
3.95
Swallow
6.16
Flamingo
3.37
Falcon
5.74
Albatross
3.32
Starling
5.16
Penguin
2.63
Owl
5.00
Bat
1.53
*Ratings were made on a 7-point scale, with 7 corresponding to the highest typicality.
326
CHAPTER 8 • The Organization of Knowledge in the Mind
Prototypes and Characteristic Features A prototype is an abstract average of all
the objects in the category we have encountered before. It is the prototype that objects are compared with in order to put them into a category. Crucial are characteristic features, which describe (characterize or typify) the prototype but are not
necessary for it. Characteristic features commonly are present in typical examples of
concepts, but they are not always present.
For example, consider the prototype of a game. It might include that it usually is
enjoyable, has two or more players, and presents some degree of challenge. But a
game does not have to be enjoyable. It does not have to have two or more players.
And it does not have to be challenging. Similarly, a bird usually has wings and flies,
but the prototype is just whatever game (or bird) represents the category best. This
theory can handle the facts that (1) games seem to have no defining features at all
and (2) a robin seems to be a better example of a bird than is an ostrich.
So what exactly is a characteristic feature? Whereas a defining feature is shared
by every single object in a category, a characteristic feature need not be. Instead,
many or most instances possess each characteristic feature. Thus, the ability to fly
is typical of birds. But it is not a defining feature of a bird—an ostrich cannot fly.
According to prototype theory, it thus seems less bird-like than a robin, which can
fly. Similarly, a typical game may be enjoyable, but it need not be so. Indeed, when
people are asked to list the features of a category, such as fruit or furniture, most list
features like sweetness or “made out of wood.” These features are characteristic
rather than defining (Rosch & Mervis, 1975). You actually can compute a score
that indicates how typical an instance is of its category by listing the properties typical of a category such as fruit and then assess how many of those properties a given
instance has (Rosch & Mervis, 1975). This matters in our interactions with other
people as well: Stereotypes of different groups of people (say, Italians or psychologists) consist of a conglomerate of average features (Medin, 1989; see also Dolderer
et al., 2009).
Classical and Fuzzy Concepts Psychologists differentiate two kinds of categories:
classical concepts and fuzzy concepts. Classical concepts are categories that can be
readily defined through defining features, such as bachelor. Fuzzy concepts are categories that cannot be so easily defined, such as game or death. Their borders are, as their
name implies, fuzzy. Classical concepts tend to be inventions that experts have devised for arbitrarily labeling a class that has associated defining features. Fuzzy concepts tend to evolve naturally (Smith, 1988, 1995a; see also Brent et al., 1996).
Thus, the concept of a bachelor is an arbitrary concept we invented. Some experts
may suggest that we use the word fruit to describe any part of a plant that has seeds,
pulp, and skin. But our natural, fuzzy concept of fruit usually does not easily extend
to tomatoes, pumpkins, and cucumbers.
Classical concepts and categories may be built on defining features. Fuzzy concepts and categories are built around prototypes. According to the prototype view,
an object will be classified as belonging to a category if it is sufficiently similar to
the prototype. Exactly what is meant by similarity to a prototype can be a complex
issue, however. There are actually different theories of how this similarity should be
measured (Smith & Medin, 1981). For our purposes, we view similarity in terms of
the number of features shared between an object and the prototype. Perhaps some
features even should be weighed more heavily as being more central to the prototype
than are other features (e.g., Komatsu, 1992).
Organization of Declarative Knowledge
327
Real-World Examples: Using Exemplars Some psychologists suggest that instead
of using a single abstract prototype for categorizing a concept, we use multiple, specific exemplars. Exemplars are typical representatives of a category (Ross, 2000; Ross
& Spalding, 1994). For example, in considering birds, we might think not only of
the prototypical songbird, which is small, flies, builds nests, sings, and so on. We
also might think of exemplars for birds of prey, for large flightless birds, for
medium-sized waterfowl, and so on. Some investigators use this approach in explaining how categories are both formed and used in speeded classification situations
(Nosofsky & Palmeri, 1997; Nosofsky, Palmeri, & McKinley, 1994; see also Estes,
1994). In particular, categories are set up by creating a rule and then by storing examples as exemplars. Objects are then compared to the exemplars to decide whether
or not they belong in the category the exemplars represent.
Exemplar theories of categorization have also been criticized. One notable criticism questions the number and types of exemplars that are stored for each category
(Smith, 2005). Some theorists contend that there are not enough resources within
the mind to store all the exemplars one would need to typify membership in a category (Collier, 2005).
A recent theory called VAM (varying abstraction model) suggests that prototypes and exemplars are just the two extremes on a continuum of abstraction.
According to this theory, most of the time we use not just one abstract prototype
nor a large number of concrete exemplars for categorization. Rather, we use a number of intermediate representations that represent subgroups within the category
(Vanpaemel & Storms, 2008). For example, animals might be represented by specific exemplars of kinds of animals, such as finch or sparrow or whale, but also by
higher-order categories, such as songbird or marine mammal.
Some researchers support neither an exclusive exemplar theory nor an exclusive
rule-based theory (Rouder & Ratcliff, 2004, 2006). Rather, some combination of the
two is thought to be more appropriate. This idea is discussed in the next section.
A Synthesis: Combining Feature-Based and Prototype Theories
A full theory of categorization can combine both defining and characteristic features
(see also Hampton, 1997a; Poitrenaud et al., 2005; Smith et al., 1974, 1988;
Wisniewski, 1997, 2000), so that each category has both a prototype and a core.
A core refers to the defining features something must have to be considered an
example of a category. The prototype encompasses the characteristic features that
tend to be typical of an example (a bird can fly) but that are not necessary for being
considered an example (an ostrich).
Consider the concept of a robber. The core requires that someone labeled as a
robber be a person who takes things from others without permission. The prototype,
however, tends to identify particular people as more likely to be robbers. Take, for
example, white-collar criminals. Their crimes can include embezzling millions of dollars from their employers. These criminals are difficult to catch because they do not
look like our prototypes of robbers, no matter how much they may steal from other
people. In contrast, unkempt denizens of our inner cities sometimes are arrested for
crimes they did not commit. In part, the reason is that they more closely match the
commonly held prototype of a robber, regardless of whether or not they steal.
Two researchers tested the notion that we come to understand the importance
of defining features only as we grow older (Keil & Batterman, 1984). Younger children, they hypothesized, view categories largely in terms of characteristic features.
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CHAPTER 8 • The Organization of Knowledge in the Mind
The investigators presented children in the age range from 5 to 10 years with descriptions. Among them were two unusual individuals. The first was “a smelly,
mean old man with a gun in his pocket who came to your house and took your
TV set because your parents didn’t want it anymore and told him he could have
it.” The second was “a very friendly and cheerful woman who gave you a hug, but
then disconnected your toilet bowl and took it away without permission and with no
intention to return it.” Younger children often characterized the first description as a
better depiction of a robber than the second description. It was not until close to age
10 that children began to shift toward characterizing the second individual as more
robber-like. In other words, the younger children viewed someone as a robber even if
the person did not steal anything. What mattered was that the person had the characteristic features of a robber. However, the transition is never fully complete. We
might suspect that the first individual would be at least as likely to be arrested as
the second. Thus, the issue of categorization itself remains somewhat fuzzy, but it appears to include some aspects of defining features and some aspects of prototypicality.
n BELIEVE IT OR NOT
SOME NUMBERS ARE ODD,
AND
SOME ARE ODDER
Even classical concepts like that of an odd number seem
to have prototypes (Armstrong, Gleitman, & Gleitman,
1983). The concept of an odd number is defined easily:
An odd number is any integer not evenly divisible by 2.
So how could one number be odder than another? People found different instances of this category to be more or
less prototypical of odd numbers. For example, 7 and 13
are typical examples of odd numbers that are viewed as
quite close to the prototype for an odd number. In contrast, 15 and 21 are not seen as so prototypically odd. In
other words, people view 7 and 13 as better exemplars
of odd numbers than 15 and 21. Nevertheless, all four
numbers are actually odd.
Theory-Based View of Categorization
A departure from feature-based, prototype-based, and exemplar-based views of meaning is a theory-based view of meaning, also sometimes called an explanation-based
view.
How Do People Use Their Theories for Categorization? A theory-based view of
meaning holds that people understand and categorize concepts in terms of implicit
theories, or general ideas they have regarding those concepts (Markman, 2003,
2007). For example, what makes someone a “good sport”?
• In the componential view, you would try to isolate features of a good sport.
• In the prototype view, you would try to find characteristic features of a good
sport.
• In the exemplar view, you might try to find some good examples you have known
in your life.
• In the theory-based view, you would use your experience to construct an explanation for what makes someone a good sport.
The theory-based view might go something like this: A good sport is someone who,
when he or she wins, is gracious in victory and does not mock losers or otherwise
make them feel bad about losing. It is also someone who, when he or she loses, loses
graciously and does not blame the winner, the referee, or find excuses. Rather, he or
Organization of Declarative Knowledge
329
she takes the defeat in stride, congratulates the winner, and then moves on. Note
that in the theory-based view, it is difficult to capture the essence of the theory in
a word or two. Rather, the view of a concept is more complex.
The theory-based view suggests that people can distinguish between essential
and incidental, or accidental, features of concepts because they have complex mental representations of these concepts. One study showed how such theories might
manifest themselves in judgments about newly learned concepts (Rips, 1989). Participants received stories about a hypothetical creature. The stimuli were presented
under two experimental conditions.
In this study (Rips, 1989), one condition involved a bird-like creature called a
sorp that, through an accident, came to look like an insect. It was never stated that
the sorp was bird-like or insect-like. Rather, the circumstances of the transformation
were described in some detail. The sorp was described as having a diet consisting of
seeds and berries, as having two wings and two legs, and as nesting high in the
branches of a tree. The nest, like that of a bird, was composed of twigs and similar
materials. Moreover, the sorp was covered with bluish-gray feathers, like many birds.
But a particular sorp had a misfortune: Its nest was near the burial place of hazardous
chemicals. As the chemicals contaminated the vegetation that the sorp ate, its appearance gradually started to change. The sorp lost its feathers and instead grew a
new pair of wings that had a transparent membrane. The sorp left its nest and developed an outer shell that was brittle and iridescent. It grew two more pairs of legs, so
that it now had six legs in all. It came to be able to hold on to smooth surfaces, and
it started sustaining itself solely on the nectar of flowers. In due course, the sorp
mated with another sorp, a normal female. The female laid the fertilized eggs that
resulted from the mating in her nest and incubated them. After three weeks, normal
young sorps emerged from their shells. Note that in this description, the fact of the
sorp’s being able to mate with a normal sorp to produce normal sorps shows that the
unfortunate sorp never really changed its basic biological makeup. It remained, in
essence, a sorp.
The second condition involved an essential change in the nature of a creature.
In other words, the change was one of essence rather than of accident and involved
a creature known as a doon. During an early stage of the doon’s life, it is known as a
sorp. It has all the characteristics of a sorp (as previously described). But after a few
months, the doon sheds its feathers and then develops the same characteristics that
resulted from the unfortunate sorp’s accident. Note that in this second condition
there is a transformation identical to that of the sorp described in the first condition,
but the transformation is represented as a natural biological change rather than an
accidental one caused by proximity to hazardous chemicals.
Participants in the study were asked to provide two ratings after reading about
the sorp and the doon. The first rating was of the degree to which the sorp (in the
sorp condition) or the doon (in the doon condition) fit into the category of “bird.”
The second rating was the similarity of the sorp or doon to birds. Thus, one rating
was for category membership and the other for similarity.
There was also a control group whose members read only the description of
sorps. Control group participants were asked merely to rate the similarity of sorps
to birds. They did not have to judge how well sorps fit into the category of “bird.”
According to prototype and exemplar theories, there is no particular reason to
expect the two sets of ratings from experimental participants to show different patterns. According to these theories, people categorize objects on the basis of their
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Control
10
Categorization rating
Similarity rating
9.5
9
7.9
8
6.6
7
6
5.2
5
3.8
4
3
2
Essential change
Accidental change
Effects of Changes on Categorizing and Similarity Ratings
Figure 8.1 Similarity Ratings.
Control group participants clearly thought sorps are very similar to birds. When the sorp’s
features changed through an accident, the sorp was still rated relatively highly as belonging
to the category of birds although its rating for similarity to birds was low. When the sorp
transformed through a natural process, however, its rating for belonging to the category of
birds went down although it was judged as being quite similar to birds.
Source: From L. J. Rips, “Similarity, Typicality, and Categorization,” in Vosniadou & Ortony (Eds.), Similarity
and Analogical Reasoning, pp. 21–59. Copyright © 1989 Cambridge University Press.
similarity to a prototype or an exemplar, so the results should be the same for both
sets of ratings.
Now have a look at the results in Figure 8.1. The results for the categorization
and similarity ratings are dramatically different! When the sorp’s features changed
through an accident, it was still rated highly as belonging to the category of birds,
although participants did not perceive them as very similar to birds. However,
when the doon changed through a natural process, it was rated less highly as belonging to the category of birds although it seemed relatively similar to birds.
Control group participants had no trouble recognizing the similarity of the sorp
to a bird. The difference in patterns between the category-membership and similarity
ratings is consistent with the theory-based view of meaning.
Finding the “Essence” of Things Further support for the theory-based view comes
from work with children. A number of investigators have studied a view of meaning
called essentialism. This view holds that certain categories, such as those of “lion” or
“female,” have an underlying reality that cannot be observed directly (Gelman,
2003, 2004). For example, someone could be a female even if another individual
were incapable in his or her observations on the street of detecting that femaleness.
One instance is having short hair. Having short hair might be more typical of males
than females, yet females can have short hair. Essentialist beliefs about the characteristics of groups are often associated with the devaluation of these groups and
Organization of Declarative Knowledge
331
increased prejudice (Bastian & Haslam, 2006; Morton et al., 2009). These beliefs
suggest that members of a particular group are intrinsically one way and can’t
change; therefore, they cannot ever really belong to another group.
Gelman (2004, 2009) showed that even young children look beyond obvious
features to understand the essential nature of things. This view contradicts Piaget’s
theory of cognitive development. According to that theory, children in the age range
from roughly 8 to 11 years are “concrete” thinkers. They cannot abstract features that
are formal in nature. Yet, the work of psychologists studying essentialism suggest that
young children can and do look for hidden features that are not obvious.
For example, in one study, 165 children ages 4 to 5 years were asked to make
inferences about things like a tiger or gold (Gelman & Markman, 1986). The researchers found that even by age 4, children could make inferences using the abstract categories as opposed merely to perceptual similarity, even when these
categories conflicted with appearances.
How people learn about concepts and categories depends, in part, on the tasks they
need to do with those concepts and categories. For example, people learn about categories one way if they need to make classifications (e.g., “Is this particular animal a cat or a
dog?”) and another way if they need to make inferences (e.g., “If this animal is a dog,
how many toes will it have?”) (Yamauchi & Markman, 1998). Learning, therefore, is
strategically flexible, depending on the task that the individual will have to do; it does
not occur with a “one-size-fits-all” rigidity (Markman & Ross, 2003; Ross, 1997).
What all this means is that meaning is not just a matter of a set of features or
exemplars. From the time children are very young, they start to form theories about
the nature of objects. These theories develop with age. For example, you probably
have a theory about what makes a car a car. You could see cars looking all kinds of
strange ways. As long as they conformed to your theory, you nevertheless would
label them as cars. Theories enable us to view meaning deeply rather than just to
assign meaning on the basis of superficial features of objects.
Intelligence and Concepts in Different Cultures
Culture influences many cognitive processes, including intelligence (Lehman, Chiu,
& Schaller, 2004). As a result, individuals in different cultures may construct concepts in quite different ways, rendering results of concept-formation or identification
studies in a single culture suspect (Atran, 1999; Coley et al., 1999; Medin & Atran,
1999). Thus, groups may think about what appears superficially to be the same phenomenon—whether a concept or the taking of a test—differently. What appear to
be differences in general intelligence may in fact be differences in cultural properties
(Helms-Lorenz, Van de Vijver, & Poortinga, 2003). Helms-Lorenz and colleagues
(2003) have argued that measured differences in intellectual performance may result
from differences in cultural complexity; but complexity of a culture is extremely hard
to define, and what appears to be simple or complex from the point of view of one
culture may appear different from the point of view of another.
People in different cultures may have quite different ideas of what it means to be
smart. For example, one of the more interesting cross-cultural studies of intelligence
was performed by Michael Cole and his colleagues (Cole et al., 1971). These investigators asked adult members of the Kpelle tribe in Africa to sort terms representing
concepts. Consider what happens in Western culture when adults are given a sorting
task on an intelligence test. More intelligent people typically will sort hierarchically.
For example, they may sort names of different kinds of fish together. Then they
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place the word fish over that. They place the name animal over fish and over birds,
and so on. Less intelligent people will typically sort functionally. For example, they
may sort fish with eat. Why? Because we eat fish. Or they may sort clothes with wear
because we wear clothes. The Kpelle sorted functionally. They did so even after investigators unsuccessfully tried to get the Kpelle spontaneously to sort hierarchically.
Finally, in desperation, one of the experimenters (Glick) asked a Kpelle to sort
as a foolish person would sort. In response, the Kpelle quickly and easily sorted hierarchically. The Kpelle were able to sort this way all along. They just had not done it
because they viewed it as foolish. They probably also considered the questioners
rather unintelligent for asking such stupid questions.
The Kpelle people are not the only ones who might question Western understandings of intelligence. In the Puluwat culture of the Pacific Ocean, for example,
sailors navigate incredibly long distances. They use none of the navigational aids
that sailors from technologically advanced countries would need to get from one
place to another (Gladwin, 1970). Suppose Puluwat sailors were to devise intelligence tests for us and our fellow Americans. We and our compatriots might not
seem very intelligent. Similarly, the highly skilled Puluwat sailors might not do
well on American-crafted tests of intelligence. These and other observations have
prompted quite a few theoreticians to recognize the importance of considering cultural context when intelligence is assessed.
Semantic-Network Models
Semantic-network models suggest that knowledge is represented in our minds in the
form of concepts that are connected with each other in a web-like form. In the following, we consider a model developed by Collins and Quillian (1969) as well as
another model that is based on a comparison of semantic features.
Collins and Quillian’s Network Model
An older model still in use today is that knowledge is represented in terms of a hierarchical semantic (related to meaning as expressed in language—i.e., in linguistic
symbols) network. A semantic network is a web of elements of meaning (nodes)
that are connected with each other through links (Collins & Quillian, 1969). Organized knowledge representation takes the form of a hierarchical tree diagram. The
elements are called nodes; they are typically concepts. The connections between
the nodes are labeled relationships. They might indicate category membership (e.g.,
an “is a” relationship connecting “pig” to “mammal”), attributes (e.g., connecting
“furry” to “mammal”), or some other semantic relationship. Thus, a network provides a means for organizing concepts. The exact form of a semantic network
differs from one theory to another, but most networks look something like the highly
simplified network shown in Figure 8.2. The labeled relationships form links that
enable the individual to connect the various nodes in a meaningful way.
a
b
R
labeled relationship (link)
Figure 8.2 Structure of a Semantic Network.
In a simple semantic network, nodes serve as junctures representing concepts linked by labeled
relationships: a basic network structure showing that relationship R links the nodes a and b.
Organization of Declarative Knowledge
333
Has skin
Can move around
Animal
Eats
Breathes
Has fins
Has wings
Bird
Can fly
Fish
Has feathers
Has long
thin legs
Can sing
Canary
Ostrich
Is yellow
Is tall
Can’t fly
Can swim
Has gills
Is pink
Can bite
Shark
Salmon
Is dangerous
Is edible
Swims
upstream
to lay eggs
Figure 8.3 Hierarchical Structure of a Semantic Network.
A semantic network has a hierarchical structure. The concepts (represented through the nodes) are connected by means
of relationships (arrows) like “is” or “has.”
Source: From In Search of the Human Mind, by Robert J. Sternberg. Copyright © 1995 by Harcourt Brace & Company. Reproduced by
permission of the publisher.
In a seminal study, the participants were given statements relating concepts, such
as “A shark is a fish” and “A shark is an animal” (Collins & Quillian, 1969). They
were asked to verify the truth of the statements. Some were true; others were not. As
the object to be classified became more hierarchically remote from the category
named in the statement, people generally took longer to verify a true statement.
Thus, we could expect people to take longer to verify “A shark is an animal” than
“A shark is a fish.” The reason is that fish is an immediate superordinate category for
shark. Animal, however, is a more remote superordinate category (see Figure 8.3).
Collins and Quillian concluded that a hierarchical network representation, such
as the one shown in Figure 8.3, adequately accounted for the response times in their
study.
A hierarchical model seemed ideal to the investigators. Within a hierarchy, we
can efficiently store information that applies to all members of a category at the
highest possible level in the hierarchy. We do not have to repeat the information
at all of the lower levels in the hierarchy. Therefore, a hierarchical model provides
a high degree of cognitive economy. The system allows for maximally efficient capacity use with a minimum of redundancy. Thus, if you know that dogs and cats
are mammals, you store everything you know about mammals at the mammal level.
For example, you might store that mammals have fur and give birth to live young
whom they nurse. You do not have to repeat that information again at the
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hierarchically lower level for dogs and cats. Whatever was known about items at
higher levels in a hierarchy was applied to all items at lower levels in the hierarchy.
This concept of inheritance implies that lower-level items inherit the properties of
higher-level items. This concept, in turn, is the key to the economy of hierarchical
models. Computer models of the network clearly demonstrated the value of cognitive economy.
The Collins and Quillian study instigated a whole line of research into the
structure of semantic networks. However, many of the psychologists who studied
the Collins and Quillian data disagreed with Collins and Quillian’s interpretations.
For one thing, numerous anomalies in the data could not be explained by the
model. For example, participants take longer to verify “A lion is a mammal” than
to verify “A lion is an animal.” Yet, in a strictly hierarchical view, verification
should be faster for the mammal statement than for the animal one. After all, the
category mammal is hierarchically closer to the category lion than is the category
animal.
Comparing Semantic Features
An alternative theory is that knowledge is organized based on a comparison of
semantic features, rather than on a strict hierarchy of concepts (Smith, Shoben, &
Rips, 1974). Though this theory sounds similar to the feature-based theory of
categorization, it differs from it in a key way: Features of different concepts are
compared directly, rather than serving as the basis for forming a category. Consider
the categorization of different mammals. In the feature-based theory, each mammal
would be described by its own set of defining features—a rabbit might be defined by
its fur, long ears, hopping walk, etc. If features are compared directly, then you
would compare all mammals on the basis of the same set of features. How does this
work?
Let’s stick with the mammal example. Mammal names can be represented in
terms of a psychological space organized by three features: size, ferocity, and humanness (Henley, 1969). A lion, for example, would be high in all three. An elephant
would be particularly high in size but not so high in ferocity. A rat would be small in
size but relatively high in ferocity. Figure 8.4 shows how information might be organized within a nonhierarchical feature-based theory. Note that this representation,
too, leaves a number of questions unanswered. For example, how does the word
mammal itself fit in? It does not seem to fit into the space of mammal names. Where
would other kinds of objects fit?
Neither of the preceding two theories of representation completely specifies how
all information might be organized in a semantic network. For example, how are
parts of a whole represented in the network? Perhaps some kind of combination of
representations is used (e.g., Collins & Loftus, 1975). Other network models tend to
emphasize mental relationships that we think about more frequently rather than just
any hierarchical relationships. For example, they might emphasize the link between
birds and robins or sparrows or the link between birds and flying. They would not
emphasize the link between birds and turkeys or penguins or the link between birds
and standing on two legs.
A common method for examining semantic networks involves the use of wordstem completion. In this task, participants are presented a prime for a very short
amount of time and then given the first few letters of a word and told to complete
the stem with the first word that comes to mind. The stems could be completed with
Organization of Declarative Knowledge
335
Dog
Giraffe
RO
C
IT
Y
Mouse
FE
HUMANNESS
Elephant
SIZE
Ferocity
Size
Humanness
Mouse
4
2
2
Dog
5
4.5
5
Giraffe
3
7
2
Elephant
5
9
4
Figure 8.4 Comparison of Semantic Features.
One alternative to hierarchical network models of semantic memory involves representations
highlighting the comparison of semantic features. The features model, too, fails to explain
all the data regarding semantic memory.
a semantically related word or any number of unrelated words. Normally, participants complete these stems with a semantically related item. For example, complete
the following word:
s__m
How did you complete it? Many people, after reading this paragraph, would complete it with “stem.” But there are many other possibilities that were not primed,
such as “spam,” “slim,” and “slum,” and “sham,” to name a few.
These findings are taken to mean that the activation of one node of the
network increases the activation of related nodes. One study noted that, with the
progression of Alzheimer’s disease, the activation of related nodes is impaired. As a
result, the word stems for patients with Alzheimer’s disease more frequently are completed with words that are unrelated to the prime (Passafiume, Di Giacomo, &
Carolei, 2006).
Semantic networks were also explored with the patient H. M. (see Chapter 5
for information on H. M). As you may recall, H. M.’s hippocampus was lesioned as
a treatment for epilepsy. A side effect of this treatment was a great loss in the
ability to form new memories. However, H. M. was capable of learning at least
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some new semantic information. Although performance on semantic tasks was impaired in H. M., clearly there was some semantic learning (O’Kane, Kensinger, &
Corkin, 2004). These findings indicate that, although semantic learning can
occur without the involvement of the hippocampus, such learning is greatly improved by its use.
We may broaden our understanding of concepts further if we consider not only
the hierarchical and basic levels of a concept (Komatsu, 1992). We also should
take into account other relational information the concept contains. Specifically,
we may better understand the ways in which we derive meanings from concepts
by considering their relations with other concepts, as well as the relations
among attributes contained within a concept. For example, new multimedia
learning and instruction devices that are based on semantic network models
and use tools like mind-mapping can indeed increase knowledge acquisition
(Zumbach, 2009).
Schematic Representations
Another way to organize the many concepts we have in our minds is by means of
schemas. First we will discuss schemas in general and then have a look at scripts,
which are a particular kind of schema.
Schemas
One main approach to understanding how concepts are related in the mind is
through schemas. They are very similar to semantic networks, except that schemas
are often more task-oriented. Recall that a schema is a mental framework for organizing knowledge. It creates a meaningful structure of related concepts. For example,
we might have a schema for a kitchen that tells us the kinds of things one might
find in a kitchen and where we might find them. Of course, both concepts and schemas may be viewed at many levels of analysis. It all depends on the mind of the
individual and the context (Barsalou, 2000). Imagine your mother has a bad backache and you offer to give her a massage. Massage to you may mean rubbing her back
and perhaps kneading her shoulders. For a massage therapist, massage may encompass
much more. He distinguishes different muscles and tendons in the back and recognizes that a backache may also be related to a condition in the hips or elsewhere in
the body. Thus, he targets his treatment much more specifically. Similarly, most people do not have an elaborate schema for cognitive psychology. However, for most cognitive psychologists, the schema for cognitive psychology is richly elaborated. It
encompasses many subschemas, such as subschemas for attention, memory, and
perception.
Schemas have several characteristics that ensure wide flexibility in their use
(Rumelhart & Ortony, 1977; Thorndyke, 1984):
1. Schemas can include other schemas. For example, a schema for animals includes
a schema for cows, a schema for apes, and so on.
2. Schemas encompass typical, general facts that can vary slightly from one specific
instance to another. For example, although the schema for mammals includes a
general fact that mammals typically have fur, it allows for humans, who are less
hairy than most other mammals. It also allows for porcupines, which seem
more prickly than furry, and for marine mammals like whales that have just a
few bristly hairs.
Organization of Declarative Knowledge
337
3. Schemas can vary in their degree of abstraction. For example, a schema for
justice is much more abstract than a schema for apple or even a schema for fruit.
Schemas also can include information about relationships (Komatsu, 1992).
Some of this information includes relationships among the following:
• concepts (e.g., the link between trucks and cars);
• attributes within concepts (e.g., the height and the weight of an elephant);
• attributes in related concepts (e.g., the redness of a cherry and the redness of an
apple);
• concepts and particular contexts (e.g., fish and the ocean); and
• specific concepts and general background knowledge (e.g., concepts about particular U.S. presidents and general knowledge about the U.S. government and
about U.S. history).
Relationships within schemas that particularly interest cognitive psychologists
are causal (“if-then”) relationships. For example, consider our schema for glass. It
probably specifies that if an object made of glass falls onto a hard surface, the object may break. Schemas also include information that we can use as a basis for
drawing inferences in novel situations. For instance, suppose that a 75-year-old
woman, a 45-year-old man, a 30-year-old nun, and a 25-year-old woman are sitting
on park benches surrounding a playground. A young child falls from some
playground equipment. He calls out “Mama!” To whom is the child calling?
Chances are that, to determine your answer, you would be able to draw an inference by calling on various schemas. They would include ones for mothers, for
men and women, for people of various ages, and even for people who join religious
orders.
Researchers interested in artificial intelligence (AI) have adapted the notion of
schemas to fit various computer models of human intelligence. These researchers
devised computer models of how knowledge is represented and used. Schemas can
be used, for example, when conducting searches in large and complex databases or
to integrate masses of information (Do & Rahm, 2007; Fagin et al., 2009).
A problem with schemas is that they can give rise to stereotypes. For example,
we might have a schema for the kind of person we believe was responsible for the
destruction of the World Trade Center on September 11, 2001. This schema can
easily generate a stereotype of certain groups of people as likely terrorists. For example, if you associate a certain type of clothing or a particular belief system with the
terrorists, you may easily associate other people with the group of perpetrators just
because they happen to wear the same kind of clothing or share some of the beliefs
of the terrorists.
Scripts
One particular kind of schema is a script (Schank & Abelson, 1977). A script contains information about the particular order in which things occur. In general, scripts
are much less flexible than schemas. However, scripts include default values for the
actors, the props, the setting, and the sequence of events expected to occur. These
values taken together compose an overview of an event.
Think about a restaurant script. The script may be applied to one particular
kind of restaurant—for example, a coffee shop. A script has several features:
• props: tables, a menu, food, a check, and money
• roles to be played: a customer, a waiter, a cook, a cashier, and an owner
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• opening conditions for the script: the customer is hungry, and he or she has money
• scenes: entering, ordering, eating, and exiting
• a set of results: the customer has less money; the owner has more money; the
customer is no longer hungry; and sometimes the customer and the owner are
pleased.
Various empirical studies have been conducted to test the validity of the script
notion. In one, researchers presented their participants with 18 brief stories (Bower,
Black, & Turner, 1979). You can read one of these, representing the doctor’s office
script, in Investigating Cognitive Psychology: Scripts—The Doctor.
In the research, participants were asked to read 18 stories similar to the one in
the Investigating Cognitive Psychology box. Later, they were asked to perform one of
two tasks. In a recall task, participants were asked to recall as much as they could
about each of the stories. Here, participants showed a significant tendency to recall,
as parts of the stories, elements that were not actually in the stories but that were
parts of the scripts that the stories represented. In the recognition task, participants
were presented with sentences. They were asked to rate, on a 7-point scale, their
confidence that they had seen each of the sentences. Some of the sentences were
from the stories, others were not. Of the sentences that were not from the stories,
some were from the relevant scripts, and others were not from these scripts. Participants were more likely to characterize particular non-story sentences as having come
from the stories if the non-story sentences were script-relevant than if the non-story
sentences were not script relevant. The Bower, Black, and Turner research suggested
that scripts seem to guide what people recall and recognize—ultimately, what people
know.
In a related context, scripts also may come into play in regard to the ways in
which experts converse with and write for one another. Certainly, experts share a
jargon—specialized vocabulary commonly used within a group, such as a profession
or a trade. You may overhear psychologists engrossed in a discussion about priming
effects, but a layperson likely will not understand what they are talking about exactly.
INVESTIGATING COGNITIVE PSYCHOLOGY
Scripts—The Doctor
John was feeling bad today and decided to go see the family doctor. He checked in
with the doctor’s receptionist and then looked through several medical magazines that
were on the table by his chair. Finally, the nurse came and asked John to take off his
clothes. The doctor was very nice to him. He eventually prescribed some pills for John.
Then John left the doctor’s office and headed home.
Did John take off his clothes?
This “scripted” description of a visit to a doctor’s office is fairly typical. Notice that
in this description, as would probably happen in any verbal description of a script, some
details are missing. The speaker (or scriptwriter, in this case) may have omitted mentioning these details. Thus, we do not know for sure that John actually took off his clothes.
Moreover, the nurse probably beckoned John at some point. She or he then escorted
John to an examination room and probably took John’s temperature and his blood pressure and weighed him. The doctor probably asked John to describe his symptoms, and
so on. But we do not know any of these things for sure.
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In addition, however, experts share a common understanding of scripts that are known
by insiders to the field of expertise. For example, after reading Chapter 2, you have a
basic understanding of positron emission tomography (PET) methods. Therefore,
when someone mentions that a PET scan was used to examine the brain, you have
an idea of what happened. People outside the area of expertise do not share this understanding. In the PET example, a person who has never read or learned about PETs
might know that the result was an image of the brain but would not know that the
procedure involved the injection of a slightly radioactive form of oxygen. When trying
to understand technical manuals and technical conversations outside your own area of
expertise, you may run into vocabulary difficulties and information gaps. You lack the
proper script for interpreting the language being spoken.
Imaging studies reveal that the frontal and parietal lobes are involved in the
generation of scripts (Godbout et al., 2004). The generation of scripts requires a
great deal of working memory. Further script generation involves the use of both
temporal and spatial information.
A number of patient populations experience impaired script use. For instance,
people with schizophrenia frequently have trouble recalling and sequencing scripts.
Also, these people add events to a script that should not be included. Research indicates a relationship between difficulties with script processing and the positive
symptoms of schizophrenia (like hallucinations and illusions) on the one hand, and
dysfunction of the frontal lobes, on the other hand (Matsui et al., 2006). People
with attention deficit hyperactivity disorder (ADHD), people with autisticspectrum disorders, and even people who are aging normally also may experience
problems with scripts and may have trouble recalling the proper sequence of the
steps involved in scripts (Allain et al., 2007; Braun et al., 2004; Loth et al., 2008).
Again, the frontal lobes seem to play a central role in script generation and use.
The typicality effect is an interesting effect in script learning. In general, when a
person is learning a script, if both typical and atypical actions are provided, the atypical information will be recalled more readily. This difference is likely due to the
increased effort in processing required for atypical information as compared with typical information. When someone suffers from a closed-head injury, like a strong blow
PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
SCRIPTS IN YOUR EVERYDAY LIFE
Take a closer look at the scripts you use in your everyday life. Is your going-to-class script
different from your going-to-meals script or other scripted activities? In what ways do your
scripts differ—in structure or in details? Try making changes to your script, either in details
or in structure and see how things work. For example, you may find that you rush in the
morning to get to school or work and forget things or arrive late. Aside from the obvious
adjustment of getting up earlier, analyze the structure of your script. See if you can combine or remove steps. You could try laying out your clothes and packing your backpack
or briefcase the night before to simplify your morning routine. The bottom line? The best
way to make your scripts work better for you is first to analyze what they are and then to
correct them.
Are the scripts in your life always useful, or are there some that interfere with your getting
things done?
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to the head, the typicality effect disappears (Vakil et al., 2002). In other words, people then have roughly equal recall of typical and atypical information.
The script model has helped cognitive psychologists gain insight into knowledge
organization. Scripts enable us to use a mental framework for acting in certain situations when we must fill in apparent gaps within a given context. Without access to
mental scripts, we probably would be at a loss the first time we entered a new restaurant or a new doctor’s office. Imagine what it would be like if the nurse at the doctor’s office had to explain each step to you. When everyone in a given situation
follows a similar script, the day flows much more smoothly.
Whether we subscribe to the notion of categories, semantic networks, or schemas, the important issue is that knowledge is organized. These forms of organization
can serve different purposes. The most adaptive and flexible use of knowledge would
allow us to use any form of organization, depending on the situation. We need some
means to define aspects of the situation, to relate these concepts to other concepts
and categories, and to select the appropriate course of action, given the situation.
Next, we discuss theories about how the mind represents procedural knowledge.
CONCEPT CHECK
1. What is a concept?
2. What is a category?
3. What is the difference between prototypes and examplars?
4. What is the theory-based view of meaning?
5. What are the components of a semantic network?
6. What is a schema?
7. Why do we need scripts?
Representations of How We Do Things: Procedural Knowledge
Some of the earliest models for representing procedural knowledge (how we do
things) come from AI and computer-simulation research (see Chapter 1). Through
these models, researchers try to get computers to perform tasks intelligently, particularly in ways that simulate intelligent performance of humans. In fact, cognitive psychologists have learned a great deal about representing and using procedural
knowledge. They have had to because of the distinctive problems posed in getting
computers to implement procedures based on a series of instructions compiled in
programs. Through trial-and-error attempts at getting computers to simulate intelligent cognitive processes, cognitive psychologists have come to understand some of
the complexities of human information processing. The next section will describe
how psychologists believe procedural knowledge “works.” Afterwards, we will have
a look at some research on the brain and how it influenced theories and models.
The “Production” of Procedural Knowledge
Procedural knowledge representation is acquired through practicing the implementation of a procedure. It is not merely a result of reading, hearing, or otherwise
Representations of How We Do Things: Procedural Knowledge
341
acquiring information from explicit instructions. Once a mental representation of
nondeclarative knowledge is constructed (proceduralization is complete), that
knowledge is implicit. It is hard to make explicit by trying to put it in words. In
fact, practice tends actually to decrease explicit access to that knowledge. For example, suppose you recently have learned how to drive a standard-shift car. You may
find it easier to describe how to do so than someone who learned that skill long
ago. As your explicit access to nondeclarative knowledge decreases, however, your
speed and ease of gaining implicit access to that knowledge increases. Eventually,
most nondeclarative knowledge can be retrieved for use much more quickly than
declarative knowledge can be retrieved.
Psychologists have developed a variety of models for how procedural information is represented and processed. Each of these models involves the serial processing of information, in which information is handled through a linear sequence of
operations, one operation at a time. One way in which computers can represent
and organize procedural knowledge is in the form of sets of rules governing a production, which includes the generation and output of a procedure (Jones & Ritter,
2003). Computer simulations of productions follow production rules (“if-then”
rules), comprising an “if” clause and a “then” clause (Newell & Simon, 1972). People may use this same form of organizing knowledge or something very close to it.
For example, suppose your car is veering toward the left side of the road. Then you
should steer toward the right side of the road if you wished to avoid hitting the curb.
The “if ” clause includes a set of conditions that must be met to implement the
“then” clause. The “then” clause is an action or a series of actions that are a response
to the “if ” clause.
For a given “if-then” rule, each condition may contain one or more variables.
For each of these conditions, there may be one or more possibilities. For example,
if you want to go somewhere by car, and if you know how to drive a car, and if you
are licensed and insured to drive, and if you have a car available to you, and if you
do not have other constraints (e.g., no keys, no gas, broken engine, dead battery),
then you may execute the actions for driving a car somewhere.
When the rules are described precisely and all the relevant conditions and actions are noted, a huge number of rules are required to perform even a very simple
task. These rules are organized into a structure of routines (instructions regarding procedures for implementing a task) and subroutines (instructions for implementing a
subtask within a larger task governed by a routine). Many of these routines and subroutines are iterative, meaning that they are repeated many times during the performance of a task.
If you want to complete a particular task or use a skill, you use a production
system that comprises the entire set of rules (productions) for executing the task
or using the skill (Anderson, 1983, 1993; Gugerty, 2007; Newell & Simon, 1972;
Simon, 1999a, 1999b).
Consider an example of a simple production system for a pedestrian to cross the
street at an intersection with a traffic light (Newell & Simon, 1972). It is shown
here (with the “if” clauses indicated to the left of the arrows and the “then” clauses
indicated to the right of the arrows):
traffic-light red ! stop
traffic-light green ! move
move and left foot on pavement ! step with right foot
move and right foot on pavement ! step with left foot
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In this production system, the individual first tests to see whether the light is
red. If it is red, the person stops and again tests to see whether the light is red.
This sequence is repeated until the light turns green. At that point, the person starts
moving. If the person is moving and the left foot is on the pavement, the person will
step with the right foot. If the person is moving and the right foot is on the pavement, the person will step with the left foot.
Sometimes, production systems, like computer programs, contain bugs. Bugs are
flaws in the instructions for the conditions or for executing the actions. For example,
in the cross-the-street program, if the last line read “move and right foot on pavement ! step with right foot,” the individual executing the production system would
get nowhere. According to the production-system model, human representations of
procedural knowledge may contain some occasional bugs (Gugerty, 2007; VanLehn,
1990).
Until about the mid-1970s, researchers interested in knowledge representation
followed either of two basic strands of research. AI and information-processing researchers were refining various models for representing procedural knowledge. Cognitive psychologists and other researchers were considering various alternative
models for representing declarative knowledge. By the end of the 1970s, some integrative models of knowledge representation began to emerge.
Nondeclarative Knowledge
As mentioned previously, knowledge traditionally has been described as either declarative or procedural. One can expand the traditional distinction between declarative and procedural knowledge to suggest that nondeclarative knowledge may
encompass a broader range of mental representations than just procedural knowledge
(Squire, 1986; Squire et al., 1990). Specifically, in addition to declarative knowledge, we mentally represent the following forms of nondeclarative knowledge:
•
•
•
•
perceptual, motor, and cognitive skills (procedural knowledge);
simple associative knowledge (classical and operant conditioning);
simple non-associative knowledge (habituation and sensitization); and
priming (fundamental links within a knowledge network, in which the activation of information along a particular mental pathway facilitates the subsequent
retrieval of information along a related pathway or even the same mental pathway; see Chapter 4).
INVESTIGATING COGNITIVE PSYCHOLOGY
Procedural Knowledge
Ask a friend if he or she would like to win $20. The $20 can be won if your friend can
recite the months of the year within 30 seconds—in alphabetical order. Go! In the years
that we have offered this cash to the students in our courses, not a single student has
ever won, so your $20 is probably safe. This demonstration shows how something as
common and frequently used as the months of the year is bundled together in a certain
order. It is very difficult to rearrange their names in an order that is different from their
commonly used or more familiar order.
Representations of How We Do Things: Procedural Knowledge
343
All of these nondeclarative forms of knowledge are usually implicit. You are not
aware of the different steps you carry out when you act, and it is hard for you to spell
them out explicitly.
Squire’s primary inspiration for his model came from three sources: his own
work; a wide range of neuropsychological research done by others, including studies
of amnesic patients and animal studies; and human cognitive experiments. Consider
an example: Work with amnesic patients reveals clear distinctions between the neural systems for representing declarative knowledge versus neural systems for some of
the nondeclarative forms of knowledge. For instance, amnesic patients often continue to show procedural knowledge even when they cannot remember that they
possess such knowledge. They often they show improvements in performance on
tasks requiring skills. These improvements indicate some form of new knowledge representation, despite an inability to remember ever having had previous experience
with the tasks. For example, an amnesic patient who is given repeated practice in
reading mirror writing will improve as a result of practice, but he or she will not
recall ever having engaged in the practice (Baddeley, 1989).
Another paradox of human knowledge representation also is demonstrated by
amnesics. Although amnesics do not show normal memory abilities under most circumstances, they do show the priming effect. Recall from Chapter 4 that, in priming, particular cues and stimuli seem to activate mental pathways, which in turn
enhance the retrieval or cognitive processing of related information. For example,
if someone asks you to spell the word sight, you will probably spell it differently, depending on several factors. These factors include whether you have been primed to
think about sensory modalities (“s-i-g-h-t”), about locations for an archaeological dig
(“s-i-t-e”), or about lists of references (“c-i-t-e”). When amnesic participants have no
recall of the priming and cannot explicitly recall the experience during which priming occurred, priming still affects their performance.
Try the experiment on priming in Investigating Cognitive Psychology: Priming. It
requires you to draw on your store of declarative knowledge.
The preceding examples illustrate situations in which an item may prime another item that is somehow related in meaning. We actually may differentiate two
types of priming: semantic priming and repetition priming (Pesciarelli et al., 2007;
INVESTIGATING COGNITIVE PSYCHOLOGY
Priming
Recruit at least two (and preferably more) volunteers. Separate them into two groups. For
one group, ask them to unscramble the following anagrams (puzzles in which you must
figure out the correct order of letters to make a sensible word): ZAZIP, GASPETHIT,
POCH YUSE, OWCH MINE, ILCHI, ACOT.
Ask the members of the other group to unscramble the following anagrams: TECKAJ,
STEV, ASTEREW, OLACK, ZELBAR, ACOT.
For the first group, the correct answers are pizza, spaghetti, chop suey, chow mein,
chili, and a sixth item. The correct answers for the second group are jacket, vest,
sweater, cloak, blazer, and a sixth item. The sixth item in each group may be either
taco or coat.
Did your volunteers show a tendency to choose one or the other answer, depending on
the preceding list with which they were primed?
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CHAPTER 8 • The Organization of Knowledge in the Mind
Posner et al., 1988). In semantic priming, we are primed by a meaningful context or
by meaningful information. Such information typically is a word or cue that is meaningfully related to the target that is used. Examples are fruits or green things, which
may prime lime. In repetition priming, a prior exposure to a word or other stimulus
primes a subsequent retrieval of that information. For example, hearing the word
lime primes subsequent stimulation for the word lime. Both types of priming have
generated a great deal of research, but semantic priming often particularly interests
cognitive psychologists.
According to spreading-activation theories, the amount of activation between a
prime and a given target node is a function of two things: the number of links connecting the prime and the target, and the relative strengths of each connection. This
view holds that increasing the number of intervening links tends to decrease the likelihood of the priming effect. But increasing the strength of each link between the
prime and its target tends to increase the likelihood of the priming effect. This model
has been well supported (e.g., McNamara, 1992). Furthermore, the occurrence of
priming through spreading activation is taken by most psychologists as support for a
network model of knowledge representation in memory processes. In particular, the
notions of priming effects through spreading activation within a network model
have led to the emergence of a newer model. It is called a connectionist model of
knowledge representation and will be considered in more detail in the next section.
CONCEPT CHECK
1. What is procedural knowledge?
2. What are the different kinds of nondeclarative knowledge?
3. What are two types of priming?
Integrative Models for Representing Declarative
and Nondeclarative Knowledge
So far, we have considered models for the representation of either declarative or procedural knowledge. Next, we explore some models that attempt to explain both.
The first model is the ACT-R model, which is based on semantic networks and production systems. Then we look at findings that are using the human brain, rather
than computers, as a model. One such theory we will consider in detail: the connectionist model.
Last, we will discuss the question of whether psychologists should try to find
models that explain all domains of knowledge representation (e.g., declarative and
procedural knowledge), or whether it makes more sense to develop models that specialize in a particular domain.
Combining Representations: ACT-R
An excellent example of a theory that combines forms of mental representation is
the ACT (adaptive control of thought) model of knowledge representation and
information processing (Anderson, 1976, 1993; Anderson et al., 2001, 2004).
In his ACT model, John Anderson synthesized some of the features of serial
Integrative Models for Representing Declarative and Nondeclarative Knowledge
345
information-processing models and some of the features of semantic-network models.
In ACT, procedural knowledge is represented in the form of production systems. Declarative knowledge is represented in the form of propositional networks. Anderson
(1985) defined a proposition as being the smallest unit of knowledge that can be
judged to be either true or false. Recall from Chapter 7 that propositions describe
abstract relationships among elements. For example, “Bobby likes cheese sticks” is a
proposition, but neither “Bobby” nor “cheese sticks” is a proposition. ACT is an
evolved form of earlier models (Anderson, 1972; Anderson & Bower, 1973).
Anderson intended his model to be so broad in scope that it would offer an
overarching theory regarding the entire architecture of cognition. In Anderson’s
view, individual cognitive processes such as memory, language comprehension, problem solving, and reasoning are merely variations on a central theme. They all reflect
an underlying system of cognition. The most recent version of ACT, ACT-R
(where the R stands for rational), is a model of information processing that integrates
a network representation for declarative knowledge and a production-system representation for procedural knowledge (Anderson, 1983; Figure 8.5).
In ACT-R, networks include images of objects and corresponding spatial configurations and relationships. They also include temporal information, such as relationships involving the sequencing of actions, events, or even the order in which items
appear. Anderson referred to the temporal information as “temporal strings.” He
noted that they contain information about the relative time sequence. Examples
would be before/after, first/second/third, and yesterday/tomorrow. These relative
time sequences can be compared with absolute time referents, such as 2 P.M., September 4, 2004. The model is under constant revision and currently includes information about statistical regularities in the environment (Anderson, 1991, 1996;
Weaver, 2008). It is also used to examine learning processes that are reflected in
the cortex (Anderson et al., 2004).
Declarative Knowledge within ACT-R
Anderson’s declarative network model, like many other network models (e.g.,
Collins & Loftus, 1975), contains a mechanism by which information can be retrieved and also a structure for storing information. Recall that within a semantic
network, concepts are stored at various nodes within the network. According to
Anderson’s model (and various other network models), the nodes can be either
inactive or active at a given time. An active node is one that is, in a sense,
“turned on.” A node can be turned on—activated—directly by external stimuli,
such as sensations, or it can be activated by internal stimuli, such as memories or
thought processes. Also, it can be activated indirectly, by the activity of one or
more neighboring nodes.
Given each node’s receptivity to stimulation from neighboring nodes, there is
spreading activation within the network from one node to another. But there are
limits on the amount of information (number of nodes) that can be activated at
any one time. (Danker et al., 2008; Shastri, 2003). Of course, as more nodes are
activated and the spread of activation reaches greater distances from the initial
source of the activation, the activation weakens. Therefore, the nodes closely related
to the original node have a great deal of activation. However, nodes that are more
remotely related are activated to a lesser degree. For instance, when the node for
mouse is activated, the node for cat also is strongly activated. At the same time, the
node for deer is activated (because a deer is an animal as well), but to a much lesser
degree.
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CHAPTER 8 • The Organization of Knowledge in the Mind
Application
Production
memory
Declarative
memory
Storage
Match
Working
memory
Retrieval
Execution
Performances
Encoding
Outside world
(a)
FEED ON
Relation
Agent
Object
WOLF
Agent
CARCASS
Agent
Subject
Relation
Relation
MADE OF
CHASE
Object
(b)
SHEEP
Relation
EAT
Object Object
MEAT
Figure 8.5 Components of the ACT-R Model and a Propositional Network.
(a) John Anderson’s most recent version of ACT-R comprises declarative knowledge (“declarative memory”), procedural knowledge (“procedural memory”), and working memory (the
activated knowledge available for cognitive processing, which has a limited capacity).
(b) The diagram shows a propositional network representing the facts that wolves feed on
carcasses, eat meat, and chase sheep. The network can be extended arbitrarily to represent
more information.
Sources: From The Legacy of Solomon Asch: Essays in Cognition and Social Psychology, by Irwin Rock.
Copyright © 1990 by Lawrence Erlbaum Associates. Reprinted by permission; Reisberg, 2007 Cognition.
ACT-R also suggests means by which the network changes as a result of activation. For one thing, the more often particular links between nodes are used, the
stronger the links become. In a complementary fashion, activation is likely to spread
along the routes of frequently traveled connections. It is less likely to spread along
infrequently used connections between nodes.
Integrative Models for Representing Declarative and Nondeclarative Knowledge
347
Consider an analogy. Imagine a complex set of water pipes interlinking various
locations. When the water is turned on at one location, the water starts moving
through various pipes. It is showing a sort of spreading activation. At various interconnections, a valve is either open or closed. It thus either permits the flow to continue through or diverts the flow (the activation) to other connections.
To carry the analogy a bit further, processes such as attention can influence the
degree of activation throughout the system. Consider the water system again. The
higher the water pressure in the system, the farther along the water will spread
through the system of pipes. To relate this metaphor back to spreading activation,
consider what happens when we are thinking about an issue and various associations
seem to come to mind regarding that issue (for example, you think about tomorrow’s
dinner and that you have to make a shopping list, and then it occurs to you that you
long promised to invite your parents for dinner, and so on). We are experiencing the
spread of activation along the nodes that represent our knowledge of various aspects
of the problem and, possibly, its solution.
To help explain some aspects of spreading activation, picture the pipes as being
more flexible than normal pipes. These pipes gradually can expand or contract; it all
depends on how frequently they are used. The pipes along routes that are traveled
frequently may expand to enhance the ease and speed of travel along those routes.
The pipes along routes that are seldom traveled gradually may contract. Similarly, in
spreading activation, connections that frequently are used are strengthened. Connections that are seldom used are weakened. Thus, within semantic networks, declarative knowledge may be learned and maintained through the strengthening of
connections as a result of frequent use. The theory of spreading activation has been
applied to a number of other cognitive concepts. These concepts include social cognition and bilingualism (Dixon & Maddox, 2005; Green, 1998).
Procedural Knowledge within ACT-R
How does Anderson explain the acquisition of procedural knowledge? Such knowledge is represented in production systems rather than in semantic networks. Knowledge representation of procedural skills occurs in three stages: cognitive, associative,
and autonomous (Anderson, 1980). See Table 8.2 for examples of each of these
three stages.
Our progress through these stages is called proceduralization (Anderson et al.,
2004; Oellinger et al., 2008). Proceduralization is the overall process by which we transform slow, explicit information about procedures (“knowing that”) into speedy, implicit, implementations of procedures (“knowing how”). (Recall the discussion of
automatization in Chapter 4. This is a term used by other cognitive psychologists to
describe essentially the same process as proceduralization.) One means by which we
make this transformation is through composition. During this stage, we construct a
single production rule that effectively embraces two or more production rules. It thus
streamlines the number of rules required for executing the procedure. For example,
consider what happens when we learn to drive a standard-shift car. We may compose
a single procedure for what were two separate procedures. One was for pressing down
on the clutch. The other was for applying the brakes when we reach a stop sign.
These multiple processes are combined together into the single procedure of driving.
Another aspect of proceduralization is “production tuning.” It involves the two
complementary processes of generalization and discrimination. We learn to generalize
existing rules to apply them to new conditions. For example, we can generalize our use
of the clutch, the brakes, and the accelerator to a variety of standard-shift cars.
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CHAPTER 8 • The Organization of Knowledge in the Mind
Table 8.2
Three Stages of Acquisition of Procedural Knowledge Using the Example
of Learning to Drive a Standard-Shift Car
Stage
Example
Cognitive stage
We think about explicit rules for
implementing the procedure.
We must explicitly think about each rule for stepping on the
clutch pedal, the gas pedal, or the brake pedal. Simultaneously, we also try to think about when and how to shift
gears.
Associative stage
We consciously practice using
the explicit rules extensively,
usually in a highly consistent
manner.
We carefully and repeatedly practice following the rules in a
consistent manner. We gradually become more familiar with
the rules. We learn when to follow which rules and when to
implement which procedures.
Autonomous stage
We use these rules automatically
and implicitly without thinking
about them. We show a high
degree of integration and coordination, as well as speed and
accuracy.
At this time we have integrated all the various rules into a
single, coordinated series of actions. We no longer need to
think about what steps to take to shift gears. We can concentrate instead on listening to our favorite radio station. We
simultaneously can think about going to our destination,
avoiding accidents, stopping for pedestrians, and so on.
Finally, we learn to discriminate new criteria for meeting the conditions we face.
For example, what happens after we have mastered driving a particular standard-shift
car? If we drive a car with a different number of gears or with different positions for
the reverse gear, we must discriminate the relevant information about the new gear
positions from the irrelevant information about the old gear positions. Taatgen and
Lee (2003) demonstrated that the learning of even extremely complex tasks—for instance, air-traffic controlling—can be described through these three processes.
Thus far, the models of knowledge representation presented in this chapter have
been based largely on computer models of human intelligence. As the foregoing discussion shows, information-processing theories based on computer simulations of human cognitive processes have greatly advanced our understanding of human
knowledge representation and information processing.
An alternative approach to understanding knowledge representation in humans
has been to study the human brain itself. Much of the research in psychobiology has
offered evidence that many operations of the human brain do not seem to process
information step-by-step, bit-by-bit. Rather, the human brain seems to engage in
multiple processes simultaneously. It acts on myriad bits of knowledge all at once.
Such models do not necessarily contradict step-by-step models. First, people seem
likely to use both serial and parallel processing. Second, different kinds of processes
may be occurring at different levels. Thus, our brains may be processing multiple
pieces of information simultaneously. They combine into each of the steps of which
we are aware when we process information step by step.
Parallel Processing: The Connectionist Model
Computer-inspired information-processing theories assume that humans, like computers, process information serially. That is, information is processed one step after another. Some aspects of human cognition may indeed be explained in terms of serial
processing, but psychobiological findings and other cognitive research seem to indicate other aspects of human cognition. These aspects involve parallel processing, in
Integrative Models for Representing Declarative and Nondeclarative Knowledge
349
which multiple operations go on all at once. We have seen how the information
processing of a computer has served as a metaphor for many models of cognition.
Similarly, our increasing understanding of how the human brain processes information also serves as a metaphor for many of the recent models of knowledge representation in humans.
The human brain seems to handle many operations and to process information
from many sources simultaneously—in parallel. In fact, it seems necessary that we
are able to process information in parallel: A computer responds to an input within
nanoseconds (millionths of a second), but an individual neuron may take up to 3
milliseconds to fire in response to a stimulus. Consequently, serial processing in the
human brain would be far too slow to manage the amount of information the brain
handles. For example, most of us can recognize a complex visual stimulus within
about 300 milliseconds. If we processed the stimulus serially, only a few hundred
neurons would have had time to respond, which is not enough for the perception
of a complex stimulus. Therefore, the distribution of parallel processes better explains the speed and accuracy of human information processing.
As a result of these considerations, many contemporary models of knowledge representation emphasize the importance of parallel processing in human cognition.
As a further result of interest in parallel processing, some computers have been
made to simulate parallel processing, such as through so-called neural networks of
interlinked computer processors.
At present, many cognitive psychologists are exploring the limits of parallel processing models. According to parallel distributed processing (PDP) models or connectionist models, we handle very large numbers of cognitive operations at once
through a network distributed across incalculable numbers of locations in the brain
(McClelland & Rogers, 2003; McClelland, Rumelhart, & the PDP Research Group,
1986; Rogers & McClelland, 2008).
How the PDP Model Works
The mental structure within which parallel processing is believed to occur is a network. In connectionist networks, all forms of knowledge are represented within the
network structure. Recall that the fundamental element of the network is the node.
Each node is connected to many other nodes. These interconnected patterns of
nodes enable the individual to organize meaningfully the knowledge contained in
the connections among the various nodes. In many network models, each node represents a concept.
The network of the PDP model is different in key respects from the semantic
network described earlier. In the PDP model, the network comprises neuron-like
units (McClelland & Rumelhart, 1981, 1985; Rumelhart & McClelland, 1982).
They do not, in and of themselves, actually represent concepts, propositions, or
any other type of information. Thus, the pattern of connections represents the
knowledge, not the specific units. The same idea governs our use of language. Individual letters (or sounds) of a word are relatively uninformative, but the pattern of
letters (or sounds) is highly informative. Similarly, no single unit is very informative,
but the pattern of interconnections among units is highly informative. Figure 8.6
illustrates how just six units (dots) may be used to generate many more than six
patterns of connections between the dots.
The PDP model demonstrates another way in which a brain-inspired model differs from a computer-inspired one. Differing cognitive processes are handled by differing patterns of activation, rather than as a result of a different set of instructions
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Pattern A
Pattern B
Pattern E
A
Pattern C
Pattern F
B
Pattern G
Pattern D
Pattern H
A
D
C
E
D
C
E
F
BAD
A
A
D
E
FAD
F
F
CAB
B
C
Pattern I
B
A
B
D
C
E
BED
F
B
A
D
C
E
ACE
F
B
C
D
E
F
ADE
Figure 8.6 Knowledge Represented by Patterns of Connections.
Each individual unit (dot) is relatively uninformative, but when the units are connected into various patterns, each
pattern may be highly informative, as illustrated in the patterns at the top of this figure. Similarly, individual letters are
relatively uninformative, but patterns of letters may be highly informative. Using just three-letter combinations, we can
generate many different patterns, such as DAB, FED, and other patterns shown in the bottom of this figure.
from a computer’s central processing unit. In the brain, at any one time, a given
neuron may be inactive, excitatory, or inhibitory.
• Inactive neurons are not stimulated beyond their threshold of excitation. They
do not release any neurotransmitters into the synapse (the interneuronal gap).
• Excitatory neurons release neurotransmitters that stimulate receptive neurons at
the synapse. They increase the likelihood that the receiving neurons will reach
their threshold of excitation.
• Inhibitory neurons release neurotransmitters that inhibit receptive neurons. They
reduce the likelihood that the receiving neurons will reach their threshold of
excitation.
Furthermore, although the action potential of a neuron is all or none, the
amounts of neurotransmitters and neuromodulators released may vary. (Neuromodulators are chemicals that can either increase or inhibit neural activation.) The frequency of firing also may vary. This variation affects the degree of excitation or
inhibition of other neurons at the synapse.
Integrative Models for Representing Declarative and Nondeclarative Knowledge
351
Similarly, in the PDP model, individual units may be inactive, or they may send
excitatory or inhibitory signals to other units. That is not to say that the PDP model
actually indicates specific neural pathways for knowledge representation. We are still
a long way from having more than a faint glimmer of knowing how to map specific
neural information. Rather, the PDP model uses the physiological processes of the
brain as a metaphor for understanding cognition. According to the PDP model, connections between units can possess varying degrees of potential excitation or inhibition. These differences can occur even when the connections are currently inactive.
The more often a particular connection is activated, the greater is the strength of
the connection, whether the connection is excitatory or inhibitory.
According to the PDP model, whenever we use knowledge, we change our representation of it. Thus, knowledge representation is not really a final product.
Rather, it is a process or even a potential process. What is stored is not a particular
pattern of connections. It is a pattern of potential excitatory or inhibitory connection strengths. The brain uses this pattern to re-create other patterns when stimulated to do so.
When we receive new information, the activation from that information either
strengthens or weakens the connections between units. The new information may
come from environmental stimuli, from memory, or from cognitive processes. The
ability to create new information by drawing inferences and making generalizations
allows for almost infinite versatility in knowledge representation and manipulation.
This versatility is what makes humans—unlike computers—able to accommodate incomplete and distorted information. Information that is distorted or incomplete is considered to be degraded. According to the PDP model, human minds are
flexible. They do not require that all aspects of a pattern precisely match to activate
a pattern. Thus, when enough distinctive (but not all) aspects of a particular pattern
have been activated by other attributes in the description, we can re-create the correct pattern even though there is some degraded information. This cognitive flexibility also greatly enhances our ability to learn new information.
By using the PDP model, cognitive psychologists attempt to explain various
general characteristics of human cognition. These characteristics include our ability
to respond flexibly, dynamically, rapidly, and relatively accurately, even when we
are given only partial or degraded information. In addition, cognitive psychologists
attempt to use the model to explain specific cognitive processes. Examples of such
processes are perception, reasoning, reading, language comprehension, priming, and
the Stroop effect, as well as other memory processes (Elman et al., 1996; Kaplan
et al., 2007; Rogers & McClelland, 2008; Smolensky, 1999; Welbourne & Ralph,
2007).
An example of the efforts to apply PDP models to specific cognitive processes
can be seen through the exploration of dyslexia, or reading disability. A specific
PDP model for the description of how we read was developed. This model involves
pathways for both phonological and semantic representations (Plaut et al., 1996).
Computer simulations with this model have been able to mimic normal reading.
When one of these two pathways is damaged, these simulations are able to imitate
the behavioral manifestations of dyslexia (Welbourne & Ralph, 2007). These simulations help researchers understand what processes are malfunctioning in people with
reading disabilities.
Connectionist models of knowledge representation explain many phenomena of
knowledge representation and processing, such as perception and memory. These
processes may be learned gradually by our storing knowledge through the
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CHAPTER 8 • The Organization of Knowledge in the Mind
strengthening of patterns of connections within the network. But connectionist
models are not flawless.
Criticisms of the Connectionist Models
One general criticism is that connectionist networks neglect properties that neural
systems have, or that they propose properties that neural networks do not have. Furthermore, critics ask why any model should be more credible than another for explaining cognitive mechanisms just because it resembles the structure of the brain
(Thomas & McClelland, 2008).
Many aspects of the connectionist models are not yet well defined. For example,
a connectionist model is less effective in explaining how people can remember a single event (Schacter, 1989a). How do we suddenly construct a whole new interconnected pattern for representing what we know about a memorable event, such as
graduation day?
Similarly, connectionist models do not satisfactorily explain how we often quickly
can unlearn established patterns of connections when we are presented with contradictory information (Ratcliff, 1990; Treadway et al., 1992). For example:
1. Suppose that you are told that the criteria for classifying parts of plants as fruits
are that they must have seeds, pulp, and skin.
2. You also are told that whether they are sweeter than other plant parts is not
important.
3. Now you are given the task of sorting various photos of plant parts into groups
that are or are not fruits.
4. What happens? You will sort tomatoes and pumpkins with apples and other
fruits, even if you did not previously consider them to be fruits.
These shortcomings of connectionist systems can be bypassed. It may be that
there are two learning systems in the brain (McClelland, McNaughton, &
O’Reilly, 1995). One system corresponds to the connectionist model in resisting
change and in being relatively permanent. The complementary system handles
rapid acquisition of new information. It holds the information for a short time. It
then integrates the newer information with information in the connectionist system. Evidence from neuropsychology and connectionist network modeling seem to
corroborate this account (McClelland, McNaughton, & O’Reilly, 1995). Thus, the
connectionist system is spared. But we still need a satisfactory account of the other
learning system.
The preceding models of knowledge representation and information processing
clearly have profited from technological advances in computer science, in brain imaging, and in the neuropsychological study of the human brain in action. These are
techniques that few would have predicted to have been so promising 40 years ago.
Thus, it would be foolish to predict that specific avenues of research will lead us in
particular directions. Nonetheless, particular avenues of research do hold promise.
For example, using powerful computers, researchers are attempting to create
parallel-processing models via neural networks. Increasingly sophisticated techniques
for studying the brain offer intriguing possibilities for research. Case studies, naturalistic studies, and traditional laboratory experiments in the field of cognitive psychology also offer rich opportunities for further exploration. Some researchers are trying
to explore highly specific cognitive processes, such as auditory processing of speech
Integrative Models for Representing Declarative and Nondeclarative Knowledge
353
sounds. Others are trying to investigate fundamental processes that underlie all aspects of cognition. Which type of research is more valuable?
Comparing Connectionist with Network Representations
How do connectionist models compare with network models? Figure 8.7 shows the
concept of a robin as represented by both a network model and a connectionist
model.
In the network representation, the nodes represent concepts. An individual
builds up a knowledge base about a robin over time as more and more information
is acquired about robins. Note that information about robins is embedded in a general network representation that goes beyond just robins. One’s understanding of robins partly depends on the relationship of the robin to other birds and even other
kinds of living things. Indeed, perhaps the most fundamental feature of the robin is
that it is a living thing. So this information is represented at the top to show that it
is an extremely general characteristic of a robin. Living things are living and can
grow, so this information is also represented at a very general level. As one moves
down the network, information gets more and more specific. For example, we learn
that a robin is a bird and that it is partly red.
In contrast, the connectionist network represents patterns of activation.
Here, too, the network shows knowledge that goes beyond just birds. But the
knowledge is in the connections rather than in the nodes. Through activation
of certain connections, knowledge about a robin is built up. A strong connection
is one that is activated many times, whereas a weak one is activated only on rare
occasions.
Text not available due to copyright restrictions
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CHAPTER 8 • The Organization of Knowledge in the Mind
How Domain General or Domain Specific Is Cognition?
Should cognitive psychologists try to find a set of mental processes that is common
across all domains of knowledge representation and processing? Or should they study
mental processes specific to a particular domain? In early AI research, investigators
believed that the ideal was to write programs that were as domain general as possible. Although none of the programs truly worked in all domains, they were a good
start. Similarly, in the broader field of cognitive psychology, the trend in the 1960s
through the mid-1970s was to strive for domain-general understandings of cognitive
processes (Miller, Galanter, & Pribram, 1960; Simon, 1976).
Starting in the late 1970s, the balance shifted toward domain specificity. In part,
this was because of striking demonstrations regarding the role of specific knowledge
in chess playing (Chase & Simon, 1973; De Groot, 1965; see Chapter 11). A key
book, The Modularity of Mind, presented an argument for extreme domain specificity
(Fodor, 1983). In this view, the mind is modular, divided into discrete modules that
operate more or less independently of each other. According to Fodor, each independently functioning module can process only one kind of input, such as language
(e.g., words), visual percepts (e.g., faces), and so on.
Further evidence for the domain specificity of face recognition can be observed
in studies employing functional magnetic resonance imaging (fMRI) methods. In
one study, it was observed that when subjects viewed faces and houses, different
brain areas were active. It thus appears that there are both specialized brain and cognitive processes for the processing of faces. This finding is taken to suggest that there
is domain specificity for facial recognition (Yovel & Kanwisher, 2004). Studies have
found domain specificity for other things like scenes and bodies as well (Downing
et al., 2006).
Fodor (1983) asserted the modularity (distinct origins) of lower-level processes
such as the basic perceptual processes involved in lexical access. However, the application of modularity has been extended to higher intellectual processes as well
(Gardner, 1983). Also, Fodor’s book emphasized the modularity of specific cognitive
functions, such as lexical access to word meanings, as distinct from word meanings
derived from context. These functions primarily have been observed in cognitive experiments. However, issues of modularity also have been important in neuropsychological research. For example, there are discrete pathological conditions associated
with discrete cognitive deficits.
Recently, there has been more of an attempt to integrate domain-specific and
domain-general perspectives in our thinking about knowledge representation and
processing. In the chapters that follow, you may wish to reflect on whether the processes and forms of knowledge representation are primarily domain general or primarily domain specific.
CONCEPT CHECK
1. What is the ACT-R model?
2. How is procedural knowledge represented in the ACT-R model?
3. What is parallel processing?
4. How does a connectionist network represent knowledge?
5. What is domain specificity?
Key Themes
355
IN THE LAB OF JAMES L. MCCLELLAND
Neural-Network Model
another pattern of activity representing the
past tense form of the word. The network
In my laboratory, we attempt to underoperates by propagating activation from
stand the implications of the idea that huthe input units to the output units. What deman cognitive processes arise from the
termines whether a unit will be active is the
interactions of neurons in the brain. We
pattern of incoming connection activation
develop computational models that dito each unit. The incoming connections
rectly carry out some human cognitive
are modulated by weights like synapses
task using simple, neuron-like processing
between neurons that modulate the effect
JAMES L. MCCLELLAND
units. We believe that the properties of
of an input on an output. If the overall efthe underlying hardware have important
fect of the input is positive, the unit comes
implications for the nature and organization of cognitive
on; if negative, it goes off.
processes in the brain.
We trained this network with pairs of items repreAn important case in point is the process of assignsenting the present and past tenses of familiar words.
ing the past tense to a word in English. Consider the
After we trained it with the 10 most frequent words
formation of the past tense of like, take, and gleat. (Gleat
(most of which are exceptions), the network could prois not a word in English, but it might be. For example,
duce the past tenses of these words, but it did not know
we might coin the word gleat to refer to the act of saluthow to deal with other words. We then trained it with
ing in a particular way.) In any case, most people agree
the 10 frequent words plus 400 more words, most of
that the past tense of like is liked; the past tense of take is
which were regular, and we found that early in training,
took; and the past tense of gleat is gleated.
it tended to overregularize most of the exceptions (e.g., it
Before the advent of neural network models, everysaid “taked” instead of “took”), even for those words that
one in the field assumed that to form the past tense of a
it had previously produced correctly. After more training,
novel verb like gleat, one would need to use a rule
it recovered its ability to produce exceptions correctly,
(e.g., to form the past tense of a word, add -[e]d).
while still producing regular past tenses for words like
Also, developmental psychologists observed that
like and for many novel items like gleat. Thus, the model
young children occasionally made interesting errors like
accounted for the developmental pattern in which chilsaying “taked” instead of “took,” and they interpreted
dren first deal correctly with exceptions, then learn how
this as indicating that the children were (over)applying
to deal with regular words and novel words and overthe past tense rule. They also assumed that to produce
regularize exceptions, and then deal correctly with regu“took” a child would need to memorize this particular
lar words, novel words, and exceptions.
item. For familiar but regular words like like, either the
Our model illustrates that in a neural network, it is
rule or the look-up mechanism might be used.
not necessary to have separate mechanisms to deal with
In the brain, a single mechanism might be used to
rules and exceptions. This conclusion remains controverproduce the past tenses of both regular and exceptional
sial but continues to gain ground. Other work in my lab
items. To explore this possibility, Rumelhart and I creand in other labs extends these ideas to reading, other
ated a simple neural network model. The model takes
aspects of language including grammar, and even to
as its input a pattern of activity representing the present
semantics, where there are many things like penguins
tense form of a word and produces on its output
and elephants that have exceptional properties.
Key Themes
This chapter brings out several of the key themes described in Chapter 1.
Rationalism versus empiricism. How do we assign meaning to concepts? The
featural view is largely a rationalistic one. Concepts have sets of features that are
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CHAPTER 8 • The Organization of Knowledge in the Mind
largely a priori and that are the same from one person to another. The underlying
notion is that one could understand a concept by a detailed dictionary definition,
pretty much without reference to people’s experience. The prototype, exemplar, and
theory-based views are much more empirically based. They assign a major role to
experience. For example, theories may change with experience. The theory of a concept such as a “dog” that a 3-year-old child has may be very different from that of a
10-year-old child.
Validity of causal inference versus ecological validity. Early research on concepts, such as that of Bruner, Goodnow, and Austin, used abstract concepts, such
as geometric forms that could be of different colors, shapes, and sizes. But in her
work, Eleanor Rosch called this approach into question. Rosch argued that natural
concepts show few of the characteristics of artificial ones. Studying artificial concepts, therefore, might yield information that applied to those concepts but not necessarily to real-world ones. Modern researchers tend to study real-world concepts
more than artificial ones.
Applied versus basic research. Basic research on concepts has generated a great
deal of applied research. For example, market researchers are very interested in people’s conceptualizations of commercial products. They use empirical and statistical
techniques to understand how products are conceived. Often, then, advertising
serves to reposition the products in customers’ minds. For example, a car that is
viewed as in the category of “economy cars” may be moved, through advertising, to
a more “upscale car” category.
Summary
1. How are representations of words and symbols organized in the mind? The fundamental
unit of symbolic knowledge is the concept.
Concepts may be organized into categories,
which may include other categories. They may
be organized into schemas, which may include
other schemas. They also may vary in application and in abstractness.
Finally, they may include information about
relationships between concepts, attributes, contexts, and general knowledge and information
about causal relationships. There are different
general theories of categorization. They include
feature-based definitional categories, prototypebased categories, and exemplar-based approaches. One of the forms for schemas is the
script. An alternative model for knowledge organization is a semantic network, involving a
web of labeled relations between conceptual
nodes. An early network model, based on the
notion of cognitive economy, was strictly
hierarchical. But subsequent ones have tended
to emphasize the frequency with which particular associations are used.
2. How do we represent other forms of knowledge in the mind? Many cognitive psychologists
have developed models for procedural knowledge. These are based on computer simulations
of such representations. An example of such a
model is the production system.
3. How does declarative knowledge interact with
procedural knowledge? An important model in
cognitive psychology is ACT, as well as its updated revision, ACT-R. It represents both procedural knowledge in the form of production
systems and declarative knowledge in the form
of a semantic network. In each of these models,
the metaphor for understanding both knowledge representation and information processing
is based on the way in which a computer processes information. For example, these models
underscore the serial processing of information.
Research on how the human brain processes
information has shown that brains, unlike computers, use parallel processing of information. In
addition, it appears that much of information
processing is not localized only to particular
areas of the brain. Instead it is distributed across
Key Terms
various regions of the brain all at once. At a
microscopic level of analysis, the neurons
within the brain may be inactive, or they may
be excited or inhibited by the actions of other
neurons with which they share a synapse. Finally, studies of how the brain processes information have shown that some stimuli seem to
prime a response to subsequent stimuli so that it
becomes easier to process the subsequent
stimuli.
A model for human knowledge representation and information processing based on
what we know about the brain is the parallel
distributed processing (PDP) model. It is also
called a connectionist model. In such models,
it is held that neuron-like units may be excited
or inhibited by the actions of other units, or
357
they may be inactive. Further, knowledge is represented in terms of patterns of excitation or
inhibition strengths, rather than in particular
units. Most PDP models also explain the priming effect by suggesting the mechanism of
spreading activation.
Many cognitive psychologists believe that
the mind is at least partly modular. It has different activity centers that operate fairly independently of each other. However, other
cognitive psychologists believe that human cognition is governed by many fundamental operations. According to this view, specific cognitive
functions are merely variations on a theme. In
all likelihood, cognition involves some modular, domain-specific processes and some fundamental, domain-general processes.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Define declarative knowledge and procedural
knowledge, and give examples of each.
2. What is a script that you use in your daily life?
How might you make it work better for you?
3. Describe some of the attributes of schemas, and
compare and contrast two of the schema models
mentioned in this chapter.
4. In your opinion, why have many of the models
for knowledge representation come from people
with a strong interest in artificial intelligence?
5. What are some advantages and disadvantages
of hierarchical models of knowledge
representation?
6. How would you design an experiment to test
whether a particular cognitive task was better
explained in terms of modular components,
or in terms of some fundamental underlying
domain-general processes?
7. What are some practical examples of the
forms of nondeclarative knowledge in
Squire’s model? (For ideas on conditioning, see
Chapter 1; for ideas on habituation or on
priming, see Chapter 4.)
8. How might you use semantic priming to enhance the likelihood that a person will think of
something you would like the person to think of
(e.g., your birthday, a restaurant to visit, or a
movie to view)?
Key Terms
ACT, p. 344
ACT-R, p. 345
artifact categories, p. 323
basic level, p. 323
category, p. 322
characteristic features, p. 326
concept, p. 322
connectionist models, p. 349
converging operations, p. 322
core, p. 327
defining features, p. 324
exemplars, p. 327
jargon, p. 338
modular, p. 354
natural categories, p. 323
networks, p. 323
nodes, p. 332
parallel distributed processing
(PDP) models, p. 349
parallel processing, p. 348
production, p. 341
production system, p. 341
prototype p. 326
prototype theory, p. 325
schemas, p. 323
script, p. 337
serial processing, p. 341
spreading activation, p. 345
theory-based view of meaning, p. 328
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CHAPTER 8 • The Organization of Knowledge in the Mind
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and
more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Prototypes
Absolute Identification
Implicit Learning
9
C
H
A
P
T
E
R
Language
CHAPTER OUTLINE
What Is Language?
Properties of Language
The Basic Components of Words
The Basic Components of Sentences
Understanding the Meaning of Words, Sentences,
and Larger Text Units
Language Comprehension
Understanding Words
The View of Speech Perception as Ordinary
The View of Speech Perception as Special
Understanding Meaning: Semantics
Understanding Sentences: Syntax
Syntactical Priming
Speech Errors
Analyzing Sentences: Phrase-Structure Grammar
A New Approach to Syntax: Transformational
Grammar
Relationships between Syntactical
and Lexical Structures
Reading
When Reading Is a Problem—Dyslexia
Perceptual Issues in Reading
Lexical Processes in Reading
Fixations and Reading Speed
Lexical Access
Intelligence and Lexical-Access Speed
Understanding Conversations and Essays:
Discourse
Comprehending Known Words: Retrieving Word
Meaning from Memory
Comprehending Unknown Words: Deriving Word
Meanings from Context
Comprehending Ideas: Propositional
Representations
Comprehending Text Based on Context
and Point of View
Representing the Text in Mental Models
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
359
360
CHAPTER 9 • Language
Here are some questions we will explore in this chapter:
1.
2.
3.
4.
What properties characterize language?
What are some of the processes involved in language?
How do perceptual processes interact with the cognitive processes of reading?
How does discourse help us understand individual words?
n BELIEVE IT OR NOT
DO
CHINESE THINK
AMERICANS ?
THE
THAN
ABOUT
NUMBERS DIFFERENTLY
How languages name numbers and how they are pronounced differs widely. There are even significant differences between English and French. For example, in
English, the number 80 is called “eighty,” and in French
it is “quatre-vingt” (literally, “four twenty,” or 4 × 20).
Do those differences in language influence how our
brain processes numbers and mathematics? This is what a
Chinese research team set out to explore. Native Chinese
speakers and native American speakers worked on numerical tasks while being monitored by an fMRI machine. The
results found that for simple addition tasks, different areas of
the brain were activated for Chinese and English speakers:
English speakers used processes that involved the left perisylvian cortices, whereas Chinese speakers used a visuopremotor network for the addition tasks. The results suggest
that language influences the way non-language-related
content is processed. It is also possible that the Chinese
language’s brevity for numbers (e.g., number words in Chinese generally contain fewer syllables than in English) increases working memory capacity, which in turn can result
in more efficient processing (Tang et al., 2006).
In this chapter we will explore what language is,
how we process language, and how it can influence
our understanding of facts and the environment.
I stood still, my whole attention fixed upon the motions of her fingers. Suddenly, I felt a misty consciousness as of something forgotten—a thrill of returning thought; and somehow the mystery of language was revealed to me.
I knew then that “w-a-t-e-r” meant the wonderful cool something that
was flowing over my hand. That living word awakened my soul, gave it
light, joy, set it free! … Everything had a name, and each name gave
birth to a new thought. As we returned to the house every object which
I touched seemed to quiver with life…. I learned a great many new
words that day … words that were to make the world blossom for me.
—Helen Keller, Story of My Life
Helen Keller became both blind and deaf at 19 months of age after a severe childhood illness. She was first awakened to a sentient, thought-filled, comprehensible
world through her teacher, Anne Sullivan. The miracle worker held one of Helen’s
hands under a spigot from which a stream of water gushed over Helen’s hand. All
the while she spelled with a manual alphabet into Helen’s other hand the mindawakening word “w-a-t-e-r.”
Language is the use of an organized means of combining words in order to communicate with those around us. It also makes it possible to think about things and
processes we currently cannot see, hear, feel, touch, or smell. These things include
ideas that may not have any tangible form. As Helen Keller demonstrated, the words
we use may be written, spoken, or otherwise signed (e.g., via American Sign
What Is Language?
361
Language [ASL]). Even so, not all communication—exchange of thoughts and
feelings—is through language. Communication encompasses other aspects—nonverbal
communication, such as gestures or facial expressions, can be used to embellish or to
indicate. Glances may serve many purposes. For example, sometimes they are deadly,
other times, seductive. Communication can also include touches, such as handshakes,
hits, and hugs. These are only a few of the means by which we can communicate.
Psycholinguistics is the psychology of our language as it interacts with the human mind. It considers both production and comprehension of language (Gernsbacher & Kaschak, 2003a, 2003b; Wheeldon, Meyer, & Smith, 2003). Four areas of
study have contributed greatly to an understanding of psycholinguistics:
• linguistics, the study of language structure and change;
• neurolinguistics, the study of the relationships among the brain, cognition, and
language;
• sociolinguistics, the study of the relationship between social behavior and language (Carroll, 1986); and
• computational linguistics and psycholinguistics, the study of language via computational methods (Coleman, 2003; Gasser, 2003; Lewis, 2003).
This chapter first briefly describes some general properties of language. The next
sections discuss the processes of language. These processes include how we understand the meanings of particular words, and how we structure words into meaningful
sentences. After our exploration of general language processes we turn to the question of how we read. And last but not least, we discuss how comprehension of larger
language and text units, like essays or conversation, works. Chapter 10 describes the
broader context within which we use language. This context includes the psychological and social contexts of language.
What Is Language?
There are almost 7,000 languages spoken in the world today (Lewis, 2009). New
Guinea is the country with the most languages in the world—it has more than 850
indigenous languages, which means that on average, each language has just about
7,000 speakers. Surprisingly, there are still languages today that have not even
been “discovered” and named by scientists. A linguist who traveled to southwestern
China’s Yunnan province in 2006 discovered 18 languages, spoken by members of
the Phula ethnic group, that never before had been defined and named (Erard,
2009). It is to be expected that there are many more languages that linguists do
not yet know about. Part of the reason for the Phula languages’ not having been
discovered earlier is that speakers of the language live in mountainous areas that
are hard to access. What exactly constitutes a language, and are there some things
that all languages have in common?
Properties of Language
Languages can be strikingly different, but they all have some commonalities (Brown,
1965; Clark & Clark, 1977; Glucksberg & Danks, 1975). No matter what language
you speak, language is:
1. communicative: Language permits us to communicate with one or more people
who share our language.
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CHAPTER 9 • Language
2. arbitrarily symbolic: Language creates an arbitrary relationship between a symbol
and what it represents: an idea, a thing, a process, a relationship, or a
description.
3. regularly structured: Language has a structure; only particularly patterned arrangements of symbols have meaning, and different arrangements yield different
meanings.
4. structured at multiple levels: The structure of language can be analyzed at more
than one level (e.g., in sounds, meaning units, words, and phrases).
5. generative, productive: Within the limits of a linguistic structure, language users
can produce novel utterances. The possibilities for creating new utterances are
virtually limitless.
6. dynamic: Languages constantly evolve.
Let’s examine the six properties of language in more detail. The communicative
property of language may be the most obvious feature, but it is also the most remarkable one. As an example, you can write what you are thinking and feeling so that
others may read and understand your thoughts and feelings. Yet, as you may know
from your own experience, there are occasional flaws in the communicative property
of language. Despite the frustrations of miscommunications, however, for one person
to be able to use language to communicate to another is impressive.
What may be more surprising is the second property of language. We communicate through our shared system of arbitrary symbolic reference to things, ideas, processes, relationships, and descriptions (Steedman, 2003). Words are symbols that
were chosen arbitrarily to represent something else, such as a “tree,” “swim,” or
“brilliant.” The thing or concept in the real world that a word refers to is called
referent. By consensual agreement, these combinations of letters or sounds may be
meaningful to us. But the particular symbols themselves do not lead to the meaning
of the word, which is why different languages use very different sounds to refer to the
same thing (e.g., Baum, árbol, tree).
Symbols are convenient because we can use them to refer to things, ideas, processes, relationships, and descriptions that are not currently present, such as the Amazon River. We even can use symbols to refer to things that never have existed, such
as dragons or elves. And we can use symbols to refer to things that exist in a
form that is not physically tangible, such as calculus, truth, or justice. Without arbitrary symbolic reference, we would be limited to symbols that somehow resembled
the things they are symbolizing (e.g., we would need a treelike symbol to represent
a tree).
Two principles underlying word meanings are the principle of conventionality and
the principle of contrast (Clark, 1993, 1995; Diesendruck, 2005). The principle of
conventionality simply states that meanings of words are determined by conventions—they have a meaning upon which people agree. According to the principle
of contrast, different words have different meanings. Thus, when you have two different words, they represent two things that are at least slightly different. Otherwise,
what would be the point of having two different words for the same thing?
The third property is the regular structure of language: Particular patterns of
sounds and of letters form meaningful words. Random sounds and letters, however,
usually do not. Furthermore, particular patterns of words form meaningful sentences,
paragraphs, and discourse. Most others make no sense. Later in this chapter, we will
look more closely at the structure of language.
363
© Hemera Technologies/Photos.com/Jupiterimages
What Is Language?
Signs that resemble the object they represent (i.e., their referent) are called icons. These pictographs are
icons that were used in ancient Egyptian hieroglyphics. In contrast, most language involves the manipulation of symbols, which bear only an arbitrary relation to their referents.
The fourth property is that language is structured at multiple levels. Any meaningful utterance can be analyzed at more than one level. Let’s see at what levels psycholinguists study language. They look at:
• sounds, such as p and t;
• words, such as “pat,” “tap,” “pot,” “top,” “pit,” and “tip;”
• sentences, such as “Pat said to tap the top of the pot, then tip it into the pit;”
and
• larger units of language, such as this paragraph or even this book.
A fifth property of language is productivity (sometimes termed generativity). Productivity refers here to our vast ability to produce language creatively. However, our
use of language does have limitations. We have to conform to a particular structure
and use a shared system of arbitrary symbols. We can use language to produce an
infinite number of unique sentences and other meaningful combinations of words.
Although the number of sounds (e.g., s as in “hiss”) used in a language may be
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CHAPTER 9 • Language
finite, the various sounds can be combined endlessly to form new words and new
sentences. Among them are many novel utterances—linguistic expressions that are
brand new and have never been spoken before by anyone. Thus, language is inherently creative. None of us possibly could have heard previously all the sentences we
are capable of producing and that we actually produce in the course of our everyday
lives. Any language appears to have the potential to express any idea in it that can
be expressed in any other language. However, the ease, clarity, and succinctness of
expression of a particular idea may vary greatly from one language to the next. Thus,
the creative potential of different languages appears to be roughly the same.
Finally, the productive aspect of language quite naturally leads to the dynamic,
evolutionary nature of language. Individual language users coin words and phrases
and modify language usage. The wider group of language users either accepts or rejects the modifications. Each year, recently coined words are added to the dictionary, signifying the extensive acceptance of these new words. For example, you
may be familiar with the words netiquette (a blend of “network” and “etiquette,” referring to appropriate behavior on-line), emoticon (a blend of “emotion” and “icon,”
referring to punctuation symbols used in emails to indicate emotions), and webinar
(referring to a seminar held on-line). All of these words have been created just in
recent years. Can you think of other newly minted words that did not exist a decade ago?
Similarly, words that are no longer used are removed from the dictionary, further contributing to the evolution of language. To imagine that language would
never change is almost as incomprehensible as to imagine that people and environments would never change. For example, the modern English we speak now evolved
from Middle English, and Middle English evolved from Old English.
To give you an example of how English has evolved, here is a sample from the
epic poem Beowulf, written in Old English around 900 A.D. On the right, you can
see a translation in modern English.
Hwæt! We Gardena in geardagum,
þeodcyninga, þrym gefrunon,
hu ða æþelingas ellen fremedon.
Lo, praise of the prowess of people-kings
of spear-armed Danes, in days long sped,
we have heard, and what honor the
athelings won!
And here is the beginning of the Canterbury Tales by Geoffrey Chaucer, written in
Middle English in the 14th century:
Whan that aprill with his shoures
soote
The droghte of march hath perced to
the roote,
And bathed every veyne in swich
licour
When April with his showers sweet
with fruit
The drought of March has pierced unto
the root
And bathed each vein with liquor that
has power
Although we can delineate various properties of language, it is important always to
keep in mind the main purpose of language: to construct a mental representation of
a situation that enables us to understand the situation and communicate about it
(Budwig, 1995; Radvansky & Dijkstra, 2007; Zwaan & Radvansky, 1998).
In other words, ultimately, language is primarily about use, not just about one
set of properties or another. For example, it provides the basis for linguistic encoding
What Is Language?
365
in memory. You are able to remember things better because you can use language to
help you recall or recognize them.
To conclude, many differences exist among languages. Nevertheless, there are
some common properties. Among them are communication, arbitrary symbolic reference, regularity of structure, multiplicity of structure, productivity, and change.
Next, we consider, in more detail how language is used. Then we observe some universal aspects of how we humans acquire our primary language.
The Basic Components of Words
Language can be broken down into many smaller units. It is much like the analysis
of molecules into basic elements by chemists. The smallest unit of speech sound is
the phone, which is simply a single vocal sound. A given phone may or may not be
part of a particular language (Minagawa-Kawai at al., 2007; Munhall, 2003; Roca,
2003b). A click of your tongue, a pop of your cheek, or a gurgling sound are all
phones. These sounds, however, are not used to form distinctive words in North
American English. A phoneme is the smallest unit of speech sound that can be
used to distinguish one utterance in a given language from another. In English, phonemes are made up of vowel or consonant sounds, like a, i, s, and f. For example, we
can distinguish among “sit,” “sat,” “fat,” and “fit,” so the /s/ sound, the /f/ sound, the
/i/ sound, and the /Æ/ sound are all phonemes in English (as is the /t/ sound). These
sounds are produced by alternating sequences of opening and closing the vocal tract.
Different languages use different numbers and combinations of phonemes. North
American English has about 40 phonemes, as shown in Table 9.1. Hawaiian has
about 13 phonemes. Some African dialects have up to 60.
In English, the difference between the /p/ and the /b/ sound is an important distinction. These sounds function as phonemes in English because they constitute the
difference between different words. For example, English speakers distinguish between “they bit the buns from the bin” and “they pit the puns from the pin” (a
well-structured but meaningless sentence). The study of the particular phonemes of
a language is called phonemics.
Phonetics is the study of how to produce or combine speech sounds or to represent them with written symbols (Roca, 2003a). Whereas phonemes are relevant to a
given language, phones, as studied in phonetics, are differentiable sounds irrespective
of language. Linguists may travel to remote villages to observe, record, and analyze
different languages. The study of phonetic inventories of diverse languages is one of
the ways linguists gain insight into the nature of language (Hoff & Shatz, 2007;
Ladefoged & Maddieson, 1996). In many cases, however, it is hard to explore a
given language because many languages are going extinct: It is estimated that about
two languages die each month (Crystal, 2002). Language death occurs for a variety
of reasons, including members leaving tribal areas in favor of more urban areas,
genocide, globalization, and the introduction of a new language to an area (Grimes,
2010; Mufwene, 2004). Language death is occurring at such an alarming rate that
some estimates suggest that 90% of the world’s languages will be extinguished within
the next generation (Abrams & Strogatz, 2003).
At the next level of the hierarchy after the phoneme is the morpheme—the
smallest unit of meaning within a particular language. The word recharge contains
two morphemes, “re-” and “charge,” where “re” indicates a repeated action. The
word “cable” consists of only one morpheme although it is made up of two syllables;
but the syllables “ca” and “ble” do not have any inherent meaning.
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CHAPTER 9 • Language
North American English Phonetic Symbols
Table 9.1
The phonemes of a language constitute the repertoire of the smallest units of sound that
can be used to distinguish one meaningful utterance from another in the given language.
Consonants
h
[p ]
pit
Vowels
[ð]
though
[ij]
fee
[p]
spit
[s]
sip
[ı]
fit
[th]
tick
[z]
zap
[ej]
fate
[t]
stuck
[ʃ]
ship
[ɛ]
let
h
[k ]
keep
[ʒ]
azure
[æ]
bat
[k]
skip
[h]
hat
[uw]
boot
[tʃ]
chip
[j]
yet
[ʊ]
book
[ʤ]
judge
[w]
witch
[ow]
note
[b]
bib
[ʍ]
which
[ɔj]
boy
[d]
dip
[l]
leaf
[ɔ]
bore
[D]
butter
reef
[ɑ]
pot
[g]
get
[r]
[rˌ ]
bird
[ə]
roses
[f]
fit
[m]
moat
[ʌ]
shut
[v]
vat
[n]
note
[aw]
crowd
[θ]
thick
[ŋ]
sing
[aj]
lies
Source: O’Grady, W., Archibald, J., Aronoff, M., and Rees-Miller, J. Contemporary Linguistics, 3rd ed., Bedford St. Martins.
English courses may have introduced you to two forms of morphemes—root words
and affixes. Root words are the portions of words that contain the majority of meaning. These roots cannot be broken down into smaller meaningful units. They are the
items that have entries in the dictionary (Motter et al., 2002). Examples of roots are
the words “fix” and “active.” We add the second form of morphemes, affixes, to these
root words. Affixes include prefixes, which precede the root word, and suffixes, which
follow the root word. Look at the word affixes. It contains three morphemes: af-,
-fix, -es. Af- is a prefix variant of the prefix ad-, meaning “toward,” “to,” or “near.”
In contrast, –fix is the root word. Finally, –es is a suffix that indicates the plural of a
noun. Similarly, the word proactive contains the prefix pro-, and the root word -active.
Linguists analyze the structure of morphemes and of words in general in a way
that goes beyond the analysis of roots and affixes. Content morphemes are the words
that convey the bulk of the meaning of a language. Function morphemes add detail
and nuance to the meaning of the content morphemes or help the content morphemes fit the grammatical context. Examples are the suffix -ist, the prefix de-, the
conjunction and, or the article the. For example, most American kindergartners
know to add special suffixes to indicate the following:
• Verb tense: You study often. You studied yesterday. You are studying now.
• Verb and noun number: The professor assigns homework. The teaching assistants
assign homework.
• Noun possession: The student’s textbook is fascinating.
What Is Language?
367
• Adjective comparison: The wiser of the two professors taught the wisest of the
three students.
The lexicon is the entire set of morphemes in a given language or in a given
person’s linguistic repertoire. The average adult speaker of English has a lexicon of
about 80,000 morphemes (Miller & Gildea, 1987). Children in grade 1 in the
United States have approximately 10,000 words in their vocabularies. By grade 3,
they have about 20,000. By grade 5, they have reached about 40,000, or half of their
eventual adult level of attainment (Anglin, 1993). By combining morphemes, most
adult English speakers have a vocabulary of hundreds of thousands of words. For example, by attaching just a few morphemes to the root content morpheme study, we
have student, studious, studied, studying, and studies. Vocabulary is built up slowly. It
develops through many diverse exposures to words and clues as to their meanings
(Akhtar & Montague, 1999; Hoff & Naigles, 1999; Woodward & Markman, 1998).
One of the ways in which English has expanded to embrace an increasing vocabulary
is by combining existing morphemes in novel ways. Some suggest that a part of William Shakespeare’s genius lay in his enjoying the creation of new words by combining
existing morphemes. He is alleged to have coined more than 1,700 words—8.5% of
his written vocabulary—and countless expressions—including the word countless itself, but also other words like inauspicious, pander, and dauntless (Lederer, 1991).
The Basic Components of Sentences
Although we put together sentences so seemingly easy when we speak, a substantial
framework of rules hides behind our creation of these sentences. Syntax refers to the
way in which we put words together to form sentences. It plays a major role in our
understanding of language. A sentence comprises at least two parts. The first is a
noun phrase, which contains at least one noun (often the subject of the sentence)
and includes all the relevant descriptors of the noun (like “big” or “fast”). The second is a verb phrase (predicate), which contains at least one verb and whatever the
verb acts on, if anything. Linguists consider the study of syntax to be fundamental to
understanding the structure of language. The syntactical structure of language specifically is addressed later in this chapter.
INVESTIGATING COGNITIVE PSYCHOLOGY
Syntax
Identify which of the following are noun phrases:
(1) the round, red ball on the corner; (2) and the; (3) round and red; (4) the ball;
(5) water; (6) runs quickly. (Hint: Noun phrases [NP] can be the subject or object of
a sentence, for example “
[NP]
bounces
[NP]
.”)
Identify which of the following are verb phrases: (1) the boy with the ball; (2) and the
bouncing ball; (3) rolled; (4) ran across the room; (5) gave her the ball; (6) runs quickly.
(Hint: Verb phrases [VP] contain verbs, as well as anything on which the verb acts [but
not the subject of the action]. For example, “The psychology student
[VP]
.”)
Answers:
Noun phrases : ð1Þ; ð4Þ; ð5Þ
Verb phrases : ð3Þ; ð4Þ; ð5Þ; ð6Þ
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CHAPTER 9 • Language
Table 9.2
Summary Description of Language
All human languages can be analyzed at many levels. Here we analyze the sentence “It takes a heap of sense to
write good nonsense.”
Language Input
#
D
e
c
o
d
i
n
g
#
Language Output
Phonemes
Distinctive subset of all possible phones
in a language
… /t/ þ /ā / þ /k/ þ /s/ …
Morphemes
From the distinctive lexicon of morphemes
… take (content morpheme) þ s (plural function morpheme) …
Words
From the distinctive vocabulary of words
It þ takes þ a þ heap þ of þ sense þ to þ write þ
good þ nonsense.
Phrase
Noun phrases (NP): a noun and its descriptors
Verb phrases (VP): a verb and whatever it acts on
NP þ VP
It (NP) takes a heap of sense to write good nonsense (VP)
It takes a heap of sense to write good nonsense.
Sentences
Based on the language’s syntax—syntactical
structure
"
E
n
c
o
d
i
n
g
"
“It takes a heap of sense to write good nonsense” was
first written by Mark Twain (Lederer, 1991, p. 131).
Discourse
Comprehend Language
Produce Language
Understanding the Meaning of Words,
Sentences, and Larger Text Units
When we read and speak, it is important not only to comprehend words and sentences but also to figure out the meaning of whole conversations or larger written
pieces. Semantics is the study of meaning in a language. A semanticist would be
concerned with how words and sentences express meaning. Discourse encompasses
language use at the level beyond the sentence, such as in conversation, paragraphs,
stories, chapters, and entire works of literature. (You will learn more about discourse
later in this chapter.) Table 9.2 summarizes the various aspects of language. The
next section discusses how we understand language through speech perception and
further analysis.
CONCEPT CHECK
1. What are some important properties of language?
2. What is the difference between phonemes and morphemes?
3. What is semantics?
Language Comprehension
Many processes are involved when we try to understand what somebody says. First of
all, we need to perceive and recognize the words that are being said. Then we need
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369
to assign meaning to those words. In addition, we have to make sense of sentences
we hear. These processes will be discussed in the next sections.
Understanding Words
Have you ever needed to communicate with someone over the phone, but the
speech you heard was garbled because of faulty cell phone reception? If so, you will
agree that speech perception is fundamental to language use in our everyday lives.
Understanding speech is crucial to human communication. In this section, we investigate how we perceive speech. We also reflect on the question of whether speech is
somehow special among all the various sounds we can perceive.
We are able to perceive speech with amazing rapidity. On the one hand, we can
perceive as many as fifty phonemes per second in a language in which we are fluent
(Foulke & Sticht, 1969). When confronted with non-speech sounds, on the other
hand, we can perceive less than one phone per second (Warren et al., 1969). This
limitation explains why foreign languages are difficult to understand (when we hear
them), and sound like they are spoken quickly. The sounds of their letters and letter
combinations are different from the sounds corresponding to the same letters and
letter combinations in our native language. For example, the author’s Spanish
sounds “American” because he tends to reinterpret Spanish sounds in terms of the
American English phonetic system, rather than the Spanish one.
Another problem we face when we try to understand what somebody else is
saying is that no word sounds exactly the same when it is spoken across the various speakers who say the word. There is a lot of variability across people in the
pronunciation of words. People speak faster or slower, or they may pronounce
sounds differently depending on where they come from. For example, one of the
author’s elementary school teachers pronounced “get” in a way that sounded like
“git.” Speech sounds are very variable, but even if a word sounds different every
time we hear it, we still need to be able to figure out what word it is. What makes
it even more complicated is that often we pronounce more than one sound at the
same time. This is called coarticulation. One or more phonemes begin while other
phonemes still are being produced. For example, say the words “palace” and
“pool.” They both begin with a p sound. But can you notice a difference in the
shape of your lips when you say the p of “pool” as compared to the p of “palace”?
You are already preparing for the following vowel as you pronounce the p sound,
and this impacts the sound you produce. Not only do phonemes within a word
overlap, but the boundaries between words in continuous speech also tend to
overlap.
The process of trying to separate the continuous sound stream into distinct
words is called speech segmentation. Figure 9.1 shows a spectrogram that records physical sound patterns. As you can see, there is often no pause between words, while at
the same time, there can be breaks within words. That is to say, the recording of
speech sound waves poorly resembles what we hear.
This overlapping of speech sounds may seem to create additional problems for
perceiving speech, but coarticulation is viewed as necessary for the effective transmission of speech information (Liberman et al., 1967). Thus, speech perception is
viewed as different from other perceptual abilities because of both the linguistic
nature of the information and the particular way in which information must be
encoded for effective transmission.
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CHAPTER 9 • Language
Image not available due to copyright restrictions
Spectrograms record physical sound patterns.
Coarticulation can be observed in nonverbal language as well. A number of
studies have been completed that examine speech production in skilled signers
(i.e., people who communicate in sign language). People who are skilled signers
can convey many paragraphs worth of information in less than a minute (Lupton,
1998). A great deal of coarticulation occurs in skilled use of American Sign Language (ASL) (Grosvald & Corina, 2008; Jerde, Soechting, & Flanders, 2003). This
coarticulation affects a number of aspects of the sign, both as it begins and as it leads
into another sign. The affected aspects include hand shape, movement, and position
(Yang & Sarkar, 2006). Coarticulation occurs more frequently with more informal
forms of ASL (Emmorey, 1994). People who are just learning sign language are
more likely to use the more formal form. Later, as people become more skillful,
they typically begin to use the more informal forms. Therefore, as skill and fluency
increase, so does the incidence of coarticulation. Coarticulation is a result of the anticipation of the next sign, much in the same way that verbal coarticulation is based
on the anticipation of the next word. This coarticulation does not, however,
typically impair understanding. These observations support the unique nature of
language perception, regardless of whether its format is spoken or signed.
So, how do we perceive speech with such ease? There are many alternative theories of speech perception to explain our facility. These theories differ mainly as to
whether speech perception is viewed as special, or ordinary, with respect to other
types of auditory perception.
The View of Speech Perception as Ordinary
One approach to speech perception suggests that when we perceive speech, we use
the same processes as when we perceive other sounds like the crowing of a rooster.
These kinds of theories emphasize either template-matching or feature-detection processes. They suggest that there are different stages of neural processing: In one stage,
speech sounds are analyzed into their components. In another stage, these components are analyzed for patterns and matched to a prototype or template (Kuhl,
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371
1991; Massaro, 1987; Stevens & Blumstein, 1981). One theory of this kind is the
phonetic refinement theory (Pisoni et al., 1985; see, for example, Hanson et al., 2010).
It says that we start with an analysis of auditory sensations and shift to higher-level
processing. We identify words on the basis of successively paring down the possibilities for matches between each of the phonemes and the words we already know
from memory. In this theory, the initial sound that establishes the set of possible
words we have heard need not be the first phoneme alone. You may have observed
this phenomenon yourself on a conscious level. Have you ever been watching a
movie or listening to a lecture when you heard only garbled sound? It takes you
a few moments to figure out what the speaker must have said. To decide what
you heard, you may have gone through a conscious process of phonetic refinement.
A similar theoretical idea is embodied by the TRACE model (McClelland &
Elman, 1986; Mirman et al., 2008). According to this model, speech perception begins with three levels of feature detection: the level of acoustic features, the level of
phonemes, and the level of words. According to this theory, speech perception is
highly interactive. In Chapter 8, you were introduced to network theories, and the
TRACE model works in a similar fashion of spreading activation. Phonemic information changes activation patterns in the network while information about words or
their meaning can influence the analysis as well by prediction of which words are
likely to appear next. Therefore, lower levels affect higher levels and vice versa.
One attribute these theories have in common is that they all require decisionmaking processes above and beyond feature detection or template matching. Thus,
the speech we perceive may differ from the speech sounds that actually reach our
ears. The reason is that cognitive and contextual factors influence our perception
of the sensed signal. For example, the phonemic-restoration effect involves integrating what we know with what we hear when we perceive speech (Kashino, 2006;
Samuel, 1981; Warren, 1970; Warren & Warren, 1970).
Suppose that you were in an experiment. You are listening to a sentence having
.” For the
the following pattern: “It was found that the *eel was on the
final word, one of the following words is inserted: axle, shoe, table, or orange. In addition, the speaker inserts a cough instead of the initial sound where the asterisk appeared in “*eel.” Virtually all participants are unaware that a consonant has been
deleted. The sound they recall having heard differs according to the context. The
participants recall hearing “the wheel was on the axle,” “the heel was on the shoe,”
“the meal was on the table,” or “the peel was on the orange.” In essence, they restore
the missing phoneme that best suits the context of the sentence.
How well do we understand words that we hear without any context? Researchers recorded speech acts by different individuals and then presented individual words
without any context to their participants. Depending on whether the speaker spoke
at a slow, normal, or fast speed, the isolated words were only correctly identified
68% (slow speech) to 41% of the time (fast speech; Miller & Isard, 1963).
Phonemic restoration is similar to the visual phenomenon of closure, which
is based on incomplete visual information. Indeed, one main approach to auditory
perception attempts to extend to various acoustic events, including speech, the
Gestalt principles of visual perception (Bregman, 1990; Shahin et al., 2009). These
principles include, for example, symmetry, proximity, and similarity. Thus, theories
that consider speech perception as ordinary use general perceptual principles of
feature-detection and Gestalt psychology. They thereby attempt to explain how listeners understand speech. Other theorists, however, view speech perception as special.
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The View of Speech Perception as Special
Some researchers suggest that speech-perception processes differ from the processes
we use when we hear other sounds. We will explore this view further in the next
sections by reviewing research on categorical perception and the motor theory of
speech perception.
Categorical Perception One phenomenon in speech perception that led to the notion of specialization was the finding of categorical perception—discontinuous categories of speech sounds. That is, although the speech sounds we actually hear
comprise a continuum of variation in sound waves, we experience speech sounds
categorically. This phenomenon can be seen in the perception of the consonant–
vowel combinations ba, da, and ga. A speech signal would look different for each
of these syllables. Some patterns in the speech signal lead to the perception of ba.
Others lead to the perception of da. And still others lead to perception of ga.
Additionally, the sound patterns for each syllable may differ as a result of other
factors like pitch. The ba that you said yesterday differs from the ba you say today.
But it is not perceived as different: It is perceived as belonging to the same category
as the ba you said a few days ago or will say tomorrow. However, a non-speech sound
such as a tone would be perceived as different. Here, continuous differences in pitch
(how high or low the tone is) are heard as continuous and distinct.
In a classic study, researchers used a speech synthesizer to mimic this natural
variation in syllable acoustic patterns. By this means, they also were able to control
the acoustic difference between the syllables (Liberman et al., 1957). They created a
series of consonant–vowel sounds that changed in equal increments from ba to da to
ga. People who listened to the synthesized syllables, however, heard a sudden switch.
It was from the sound category of ba to the sound category of da (and likewise from
the category of da to that of ga). Discrimination of differences within one sound category was relatively poor, whereas discrimination between categories (e.g., between
ba and da) was enhanced. Although all the sounds differed from each other acoustically (and their acoustic distance was equal), people did not really perceive differences between the sounds that represented the same category. They only heard
differences when the sounds represented different categories. That is, discrimination
of two neighboring bas was poor, whereas discrimination of ba from its neighboring
da was preserved. Normal perceptual processing should discriminate equally between
all equally spaced pairs of the different sounds along the continuum, however. The
researchers thus concluded that speech is perceived via specialized processes.
A number of studies have further examined categorical perception in people
with reading disabilities. In children with learning disabilities, the perceptual ability
to discriminate between categories is impaired. Conversely, the perceptual ability to
discriminate within categories is enhanced in these same children (Breier et al.,
2005). That is, children at risk of reading disabilities, compared with children who
are not at risk, use less phonological information even though they perceive more
subtle acoustic (sound) differences when performing a categorical-perception task
(Breier et al., 2004). These and other findings led the researchers to investigate the
notion that speech perception relies on special processes.
The Motor Theory of Speech Perception The findings described above also led to
the early, but still influential, motor theory of speech perception (Galantucci,
Fowler, & Turvey, 2006; Liberman et al., 1967; Liberman & Mattingly, 1985). According to the motor theory, we use the movements of the speaker’s vocal tract to
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373
INVESTIGATING COGNITIVE PSYCHOLOGY
Understanding Schemas
Ask a friend to do an experiment with you. Tell your friend that you are going to say a
sentence and ask them what it means. Say the following sentence to your friend: “In mud
eels are, in clay none are.” Ask your friend the meaning of what you just said.
Chances are that your friend did not understand the sentence. Why? Your friend
was not applying the appropriate schema to understand your utterance. Ask your friend
to think of him- or herself as a fish who doesn’t want to be eaten by eels. Now, repeat
this sentence to your friend. Can your friend understand the sentence now? Many people can after they have the context. (Although, there are still some people who will not
be able to understand this utterance, so you will have to give them stronger hints.)
perceive what he says. Observing that a speaker rounds his lips or presses his lips
together provides the listener with phonetic information. Thus, the listener uses specialized processes involved in producing speech to perceive speech. In fact, there is
substantial overlap between the parts of the cortex that are involved in speech production and speech perception.
So, how can the motor theory of speech perception be tested? In a recent study,
researchers had participants listen to continuous acoustic signals. As we know from
the section on categorical perception, people categorize continuous sounds as syllables like “ga” and “ba”. With repetitive transcranial magnetic stimulation (rTMS),
participants’ lip representation in the primary motor cortex was then interrupted.
With the motor cortex’s lip representation impaired, participants had a much harder
time distinguishing between speech sounds that involved the lips or tip of the tongue in their articulation (e.g., “ba” and “da”). However, differentiation between
sounds that do not involve lip articulation (e.g., “ka” and “ga”) was not impaired.
These findings support the notion that motor parts of the cortex are not only involved in the production of speech but also in speech perception (Moettoenen &
Watkins, 2009).
Since the early work of Liberman and colleagues, the phenomenon of categorical perception has been extended to the perception of other kinds of stimuli, such as
color and facial emotion. This extension weakens the claim that speech perception
is special (Galantucci, Fowler, & Turvey, 2006; Jusczyk, 1997). However, supporters
of the speech-is-special position still maintain that other forms of evidence indicate
that speech is perceived via specialized processes.
One such distinctive aspect of human speech perception can be seen in the
so-called McGurk effect (McGurk & MacDonald, 1976). This effect involves the
synchrony of visual and auditory perceptions: When watching a movie, an auditory
syllable is perceived differently depending on whether you see the speaker make the
sound that matches the pronunciation of the syllable or make another sound that
does not match the syllable spoken. Imagine yourself watching a movie. As long as
the soundtrack corresponds to the speakers’ lip movements, you encounter no problems. However, suppose that the soundtrack indicates one thing, such as da. At the
same time, the actor’s lips clearly make the movements for another sound, such as
ba. You are likely to hear a compromise sound, such as tha. It is neither what was
said nor what was seen. You somehow synthesize the auditory and visual information. You thereby come up with a result that is unlike either. For this reason, poorly
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dubbed movies can be confusing. You are vaguely aware that the lips are saying one
thing, and you are hearing something else entirely.
In one set of studies, Nicholls, Searle, and Bradshaw (2004) studied the McGurk
effect with respect to lip reading. The experimenters covered half of the speaker’s
mouth, while either matching or mismatching auditory and visual information. The
experimenters found that, when the left side of the mouth was covered, there was
little change in the occurrence of the McGurk effect. However, when the right
side of the mouth was covered, the occurrence of the McGurk effect dropped dramatically. Then the researchers used an inverted video of the left side of the mouth,
such that it appeared to be the right side of the mouth, and saw the McGurk effect
rebound (Nicholls, Searle, & Bradshaw, 2004). These findings suggest that the right
side, or what is perceived as the right side of the mouth, is attended to more in lip
reading. Hence, lack of correspondence between what the right side of the mouth
says and what is heard are the more likely to lead to the McGurk effect.
The McGurk effect seems to have a physiological basis in the superior temporal
sulcus (STS). Researchers presented their participants with stimuli like the ones described above that evoke the McGurk effect. However, when they used transcranial
magnetic stimulation (TMS) to interrupt activity of the STS in their participants,
the likelihood of the McGurk effect was significantly reduced (Beauchamp et al.,
2010).
In normal conversation, we use lip reading to augment our perception of speech.
It is particularly important in situations in which background noise may make
speech perception more difficult. The motor theory accounts for this integration
quite easily because articulatory information includes visual and auditory information. However, believers in other theories interpret these findings as support for
more general perceptual processes. They believe these processes naturally integrate
information across sensory modalities (Galantucci et al., 2006; Massaro, 1987;
Massaro & Cohen, 1990).
Is a synthesis of these opposing views possible? Perhaps one reason for the complexity of this issue lies in the nature of speech perception itself. It involves both
linguistic and perceptual attributes. From a purely perceptual perspective, speech is
just a relatively complex signal that is not treated qualitatively differently from other
signals. From a psycholinguistic perspective, speech is special because it lies within
the domain of language, a special human ability. Indeed, cognitive psychology textbooks differ in terms of where speech perception is discussed. Sometimes it is discussed in the context of language, other times in the context of perception. Thus,
the diversity of views on the nature of speech perception can be seen as reflecting
the differences in how researchers treat speech. They view it either as regular acoustic signals or as more special phonetic messages (Remez, 1994).
Understanding Meaning: Semantics
Language is very difficult to put into words.
—Voltaire
The opening of this chapter quoted Helen Keller’s description of her first awareness
that words had meanings. You probably do not remember the moment that words
first came alive to you, but your parents surely do. In fact, one of the greatest joys
of being a parent is watching your children’s amazing discovery that words have
meanings. In semantics, denotation is the strict dictionary definition of a word.
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375
Connotation is a word’s emotional overtones, presuppositions, and other nonexplicit meanings. Taken together, denotation and connotation form the meaning
of a word. Because connotations may vary between people, there can be variation
in the meaning formed. Imagine the word snake. For many people, the connotation
of snake is negative or dangerous. Others, say a biologist specializing in snakes (called
a herpetologist), would have a very different and probably much more positive connotation for the word snake.
How do we understand word meanings in the first place? Recall from previous
chapters that we encode meanings into memory through concepts. These include
ideas, to which we may attach various characteristics and with which we may connect various other ideas, such as through propositions (Rey, 2003). They also include images and perhaps motor patterns for implementing particular procedures.
Here, we are concerned only with concepts, particularly in terms of words as arbitrary symbols for concepts.
Actually, when we think of words as representing concepts, words are economical ways in which to manipulate related information. For example, when you think
about the single word desk, you also may conjure all these things:
•
•
•
•
•
all the instances of desks in existence anywhere;
instances of desks that exist only in your imagination;
all the characteristics of desks;
all the things you might do with desks; and
all the other concepts you might link to desks (e.g., things you put on or in
desks or places where you might find desks).
Having a word for something helps us to add new information to our existing
information about that concept. For example, you have access to the word desk.
When you have new experiences related to desks or otherwise learn new things
about desks, you have a word around which to organize all this related information.
Recall, too, the constructive nature of memory. Having word labels (e.g., “washing clothes,” “peace march”) has several effects. First, it facilitates the ease of understanding and remembering a text passage. Second, it enhances subjects’ recall of the
shape of a droodle. (Recall that a droodle is essentially a doodle puzzle: You see a
doodle and you have to guess what it is.) Third, it affects the accuracy of eyewitness
testimony. Having words as concepts for things helps us in our everyday nonverbal
n BELIEVE IT OR NOT
CAN IT REALLY BE HARD
TO
STOP CURSING?
In psychology, the involuntary utterance of socially inappropriate words or sentences is called coprolalia. There is a
range of other coprophenomena, like making socially inappropriate gestures (copropraxia) and drawings (coprographia). Often, these utterances are related to obscene,
religious, or ethnical content. They are not expressed out of
anger but rather result from a kind of urge that the speaker
cannot control and that can cause him or her considerable
embarrassment. Coprolalia is often part of a neurological
disorder called Tourette syndrome, which exhibits a widely
variable pattern of tics (like suddenly and involuntarily
kicking in the air or pulling one’s earlobe). Tourette syndrome
usually starts in childhood and stabilizes after adolescence.
As of today, it is not entirely clear what causes tics, but studies indicate that generally the cortical-striatal-thalamocortical
pathways are involved. Different tics seem to be caused by
different brain mechanisms. Coprolalia, in particular, involves activation of the brain’s language regions, caudate,
thalamus, and cerebellum. It can also occur outside of Tourette syndrome in people who have suffered strokes or encephalitis, for example (Freeman et al., 2008). Indeed,
even cases of Tourette syndrome patients swearing in sign
language have been reported.
CHAPTER 9 • Language
Published in The New Yorker 3/22/1993 by J.B. Handelsman/www.Cartoonbank.com
376
“ ‘Born in conservation,’ if you don’t mind. ‘Captivity’ has negative connotations.”
interactions. For example, our concepts of skunk and of dog allow us more easily to
recognize the difference between the two, even if we see an animal only for a moment (Ross & Spalding, 1994). Depending on which we saw, this rapid recognition
enables us to respond appropriately. Clearly, being able to comprehend the conceptual meanings of words is important. But how do we retrieve the meanings of words?
All words are stored in our mental lexicon, which contains both the words and
their meanings. One observation that hints at how we represent meaning comes
from studies with people who once had normal language skills but at some point
contracted lesions of the temporal lobes of the brain. When certain of those people
were asked to indicate the meaning of a picture, their problems in naming objects
were not arbitrary. One group of patients had trouble recognizing animate things,
like animals and plants. Another group of patients was challenged in recognizing
things that were manufactured, like tools. Warrington and colleagues (Warrington
& McCarthy, 1987; Warrington & Shallice, 1984) have suggested criteria for determining the difference between manufactured and living things. Objects that are
made by humans are mostly distinguished by means of their function. Do we use
an object to get from one point to another, or to open something? Living things,
in contrary, are mainly distinguished by means of their looks. A horse looks different
than a donkey, and both differ from what a cow looks like. So when we retrieve the
meaning of words from our memory, we may rely on their perceptual features and
the function (as well as some other characteristics). This interpretation is in line
with the findings of the lesion studies: People who had sustained damage in regions
that are involved in perceptual processing have trouble recognizing living things.
People with lesions in areas that are involved in the processing of functional information have more trouble recognizing man-made things.
As you may have noticed, many words in English have more than one meaning:
Take the word “foot,” for example. “I have a very wide foot,” refers to the foot as a
Language Comprehension
377
body part. “She lives at the foot of the hill,” indicates that a person is living at the
bottom part of a hill. Generally, words have a dominant meaning that is used more
often, and one or more subordinate meanings. In the example with the word “foot,”
people typically think of a body part, which is the dominant meaning. The bottom
part of a hill is a subordinate meaning. What meaning you ultimately ascribe to the
word depends largely on the context in which it appears.
Understanding Sentences: Syntax
An equally important part of the psychology of language is the analysis of linguistic
structure. Not only words convey meaning; the structure of sentences does as well.
For example, “The man hunted the lion.” has a different meaning from “The lion
hunted the man.” Syntax is the systematic way in which words can be combined
and sequenced to make meaningful phrases and sentences (Carroll, 1986). Whereas
studies of speech perception chiefly investigate the phonetic structure of language,
syntax focuses on the study of the grammar of phrases and sentences. In other words,
it considers the regularity of structure.
Although you have heard the word grammar before in regard to how people
should structure their sentences, psycholinguists use the word grammar in a slightly
different way. Specifically, grammar is the study of language in terms of noticing regular patterns. These patterns relate to the functions and relationships of words in a
sentence. They extend as broadly to the level of discourse and narrowly to the pronunciation and meaning of individual words.
In your English courses, you may have been introduced to prescriptive grammar.
This kind of grammar prescribes the “correct” ways in which to structure the use of
written and spoken language. Of greater interest to psycholinguists is descriptive grammar, in which an attempt is made to describe the structures, functions, and relationships of words in language.
Consider an example of a sentence that illustrates the contrast between prescriptive and descriptive approaches to grammar: When Mario observes his father carrying upstairs an unappealing bedtime book, he responds, “Daddy, what did you bring
that book that I don’t want to be read to out of up for?” (Pinker, 1994, p. 97). Mario’s utterance might shiver the spine of any prescriptive grammarian. But Mario’s
ability to produce such a complex sentence, with such intricate internal interdependencies, would please descriptive grammarians.
The study of syntax allows analysis of language in manageable—and therefore relatively easily studied—units. Also, it offers limitless possibilities for exploration. There
are virtually no bounds to the possible combinations of words that may be used to
form sentences. Earlier, we referred to this property as the productivity of language.
In English, as in any language, we can take a particular set of words (or morphemes,
to be more accurate) and a particular set of rules for combining the items and produce
a breathtakingly vast array of meaningful utterances. Suppose you were to go to the
U.S. Library of Congress and randomly select any sentence from any book. You then
searched for an identical sentence in the vast array of sentences in the books therein.
Barring intentional quotations, you would be unlikely to find the identical sentence.
People demonstrate a remarkable knack for understanding syntactical structure.
Read through the following demonstration in the Investigating Cognitive Psychology:
Your Sense of Grammar box and try to find the sentences that are not grammatical.
Fluent speakers of a language can recognize syntactical structure immediately.
We can do so whether particular sentences and particular word orders are or are
CHAPTER 9 • Language
INVESTIGATING COGNITIVE PSYCHOLOGY
Your Sense of Grammar
Mark an asterisk next to the sentences that are not grammatical, regardless of whether
the sentences are meaningful or accurate:
1. The student the book.
2. Bought the book.
3. Bought the student the book.
4. The book was bought by the student.
5. By whom was the book bought?
6. By student the bought book.
7. The student was bought by the book.
8. Who bought the book?
9. The book bought the student.
10. The book bought.
Answers:
1; 2; 3; 6; 10
378
not grammatical (Bock, 1990; Pinker, 1994). We can do so even when the sentences are meaningless. For example, we can evaluate Chomsky’s sentence, “Colorless green ideas sleep furiously.” Or we can evaluate a sentence composed of
nonsense words, as in Lewis Carroll’s poem “Jabberwocky,” “ ’Twas brillig and the
slithy toves did gyre and gimble in the wabe.”
In the following, we explore the properties and impact of syntax in more detail. We
have a look at the phenomena of syntactical priming and speech errors and consider
two approaches to analyzing sentences: phrase-structure grammar and transformational
grammar. We will also explore the interaction between words and sentence structures.
Syntactical Priming
Just as we show semantic priming of word meanings in memory (that is, we react
faster to words that are related in meaning to a prior presented word), we show syntactical priming of sentence structures. In other words, we spontaneously tend to use
syntactical structures and read faster sentences that parallel the structures of sentences we have just heard (Bock, 1990; Bock, Loebell, & Morey, 1992; Sturt et al.,
2010). For example, a speaker will be more likely to use a passive construction (e.g.,
“The student was praised by the professor”) after hearing a passive construction. He
or she will do so even when the topics of the sentences differ. Even children as
young as age 3 described a series of new items with the same sentence structure
used by an experimenter (Bencini & Valian, 2008).
Another example of syntactical priming is sentence priming. In this type of experiment, participants are presented with a sentence. Participants then are presented
with new sentences and are asked to rate the degree to which they are grammatically
correct. If a sentence has the same structure as the previously presented item, it is
rated as more nearly grammatically correct (Luka & Barsalou, 2005), independent
of its actual degree of grammatical correctness. Participants in the experimental
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379
group may have read the sentence, “Amanda carried Fernando the package,”
whereas control-group participants read the sentence, “Amanda carried the package
to Fernando.” Both groups were then asked to rate the test sentence, “Igor lugged
Dr. Frankenstein the corpse.” As you can see, this sentence is structurally similar to
the first sentence that participants in the experimental group were asked to read; it
does not resemble the structure of the first sentence that control-group participants
read. And indeed, participants from the experimental group rated the test sentence
as more grammatical than did control-group participants.
Speech Errors
Other evidence of our uncanny aptitude for syntax is shown in the speech errors we
produce. Even when we accidentally switch the placement of two words in a sentence,
we still form grammatical, if meaningless or nonsensical, sentences. We almost invariably switch nouns for nouns, verbs for verbs, prepositions for prepositions, and so on.
For example, we may say, “I put the oven in the cake.” But we will probably not say,
“I put the cake oven in the.” We usually even attach (and detach) appropriate function morphemes to make the switched words fit their new positions. For example,
when meaning to say, “The butter knives are in the drawer,” we may say, “The butter
drawers are in the knife.” Here, we change “drawer” to plural and “knives” to singular
to preserve the grammaticality of the sentence. Even so-called agrammatic aphasics,
who have extreme difficulties in both comprehending and producing language, preserve syntactical categories in their speech errors (Butterworth & Howard, 1987; Garrett, 1992). In Chapter 10, we consider slips of the tongue in more detail.
Analyzing Sentences: Phrase-Structure Grammar
The preceding examples seem to indicate that we humans have some mental mechanism for classifying words according to syntactical categories. This classification
mechanism is separate from the meanings for the words (Bock, 1990). When we
compose sentences, we seem to analyze and divide them into functional components. This process is called parsing. We assign appropriate syntactical categories (often called “parts of speech,” e.g., noun, verb, article) to each component of the
sentence. We then use the syntax rules for the language to construct grammatical
sequences of the parsed components.
Early in the 20th century, linguists who studied syntax largely focused on how
sentences could be analyzed in terms of sequences of phrases, such as noun phrases
and verb phrases, which were mentioned previously. They also focused on how
phrases could be parsed into various syntactical categories, such as nouns, verbs,
and adjectives. Such analyses look at the phrase-structure grammar—they analyze
the structure of phrases as they are used. Let’s have a closer look at the sentence:
“The girl looked at the boy with the telescope.”
First of all, the sentence can be divided into the noun phrase (NP) “The girl” followed by a verb phrase (VP) “looked at the boy with the telescope.” The noun
phrase can be further divided into a determiner (“the”) and a noun (“girl”). Likewise, the verb phrase can be further subdivided. However, the analysis of how to
divide the verb phrase depends on what meaning the speaker had in mind. You
may have noticed that the sentence can have two meanings:
(a) The girl looked with a telescope at the boy, or
(b) The girl looked at a boy who had a telescope.
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CHAPTER 9 • Language
IN THE LAB OF STEVEN PINKER
The Psychology of Language
at once: convey a proposition, and maintain social relationships. The anthropoloI have always thought of language as a
gist Alan Fiske has found that in every
window into human nature. Early in my
culture, the relationship between two
career I tried to identify the mental mepeople falls into a small number of types:
chanisms that children use to acquire their
communality (warmth and sharing), domimother tongue as a way of shedding light
nance, and reciprocity (tit-for-tat exchanges
on the nature-nurture debate. I then foor equal distribution of resources). We discused on the meaning and syntax of
tinguish these sharply: for example, everySTEVEN PINKER
verbs—why you can pour water into a
one knows that good friends shouldn’t
glass, but you can’t pour a glass with water, and why
engage in a business transaction, like one selling his
you can fill a glass with water, but you can’t fill water
car to the other, because the act of negotiating a price
into a glass—to illuminate the basic concepts of human
(reciprocity) clashes with the rules of a friendship (comthought such as causation, agency, space, time, and
munal sharing), putting a strain on the relationship. The
substance. For a number of years I studied regular
problem with language is that the very act of making a
verbs, like walk-walked and play-played to get insight
request in words can clash with the ongoing relationabout the computational architecture of human cogniship type: an imperative like “Give me the guacamole”
tion and how they differ from irregular verbs like singassumes a dominance relationship (you’re bossing
sang and bring-brought to understand the interaction
someone around) that clashes with friendship; a bribe
between computation and memory.
like “If I give you $50, will you let me drive away?”
Currently I am using “indirect speech”—innuendo,
treats the officer as a business customer rather than a
euphemism, doublespeak, shilly-shallying—as a winsuperior. So, to treat your fellow diner as an equal, or
dow into social relationships. People often don’t blurt
to probe whether the officer is receptive to a bribe
out what they mean in so many words but veil their
without challenging the current relationship, people
intentions in innuendo, counting on their listeners to
use indirect speech. Basically, they are seeking plausi“catch their drift” or “read between the lines.” Here
ble deniability of a transaction that presupposes a
are some examples:
different relationship model than the one currently in
force.
• If you could pass the guacamole, that would be aweWe test this idea by having people imagine themsome [a polite request].
selves in the shoes of someone receiving a bribe, a
• Gee, officer, is there some way we could take care
threat, or a sexual come-on, which is posed either diof the ticket right here, without going to court or dorectly or with innuendo; and then to indicate how confiing a lot of paperwork? [a bribe]
dent they are in what they think the speaker intends,
• Would you like to come up and see my etchings?
whether they feel threatened or offended, how easy it
[a sexual come-on]
would be to resume a normal relationship if the offer is
• I hear you’re the jury foreman in the Soprano trial. It’s
rebuffed, and other questions. We also have people
an important civic responsibility. You’ve got a wife
role-play these interactions while hooked up to
and kids. We know you’ll do the right thing. [a threat]
electrophysiological recording equipment to measure
Why don’t people just say what they mean? The
their sense of threat and challenge in measures such
reason, I believe, is that language has to do two things
as heart rate and blood pressure.
In case (a), the verb phrase contains a verb (V; “looked”), and two prepositional
phrases (PP; “at the boy” and “with the telescope”). In case (b), the verb phrase
would again contain the verb “looked,” but there is just one prepositional phrase
(“looked at the boy with the telescope”). You can then work your way further
Language Comprehension
381
INVESTIGATING COGNITIVE PSYCHOLOGY
Syntax
Using the following 10 words, create 5 strings of words that make grammatical sentences. Also create five sequences of words that violate the syntax rules of English grammar: ball, basket, bounced, into, put, red, rolled, tall, the, woman.
Finished? Now think about the steps involved in producing the sentences. To complete the preceding task, you mentally classified the words into syntactical categories,
even if you did not know the correct labels for the categories. You then arranged the
words into grammatical sequences according to the syntactical categories for the words
and your implicit knowledge of English syntax rules. Most 4-year-olds can demonstrate
the same ability to parse words into categories and to arrange them into grammatical
sentences. Of course, most 4-year-olds probably cannot label the syntactical categories
for any of the words.
down and divide the prepositional phrases further into prepositions, determiners,
nouns, etc (see Figure 9.2 for details).
The rules governing the sequences of words are termed phrase-structure rules.
Linguists often use tree diagrams, such as the ones shown in Figure 9.2, to observe
the interrelationships of phrases within a sentence. Various other models also have
been proposed (e.g., relational grammar, Farrell, 2005; Perlmutter, 1983a; lexicalfunctional grammar; Bresnan, 1982).
Tree diagrams help to reveal the interrelationships of syntactical classes within
the phrase structures of sentences (Clegg & Shepherd, 2007; Wasow, 1989). In
particular, such diagrams show that sentences are not merely organized chains of
words, strung together sequentially. Rather, they are organized into hierarchical
structures of embedded phrases. The use of tree diagrams helps to highlight many
aspects of how we use language, including both our linguistic sophistication and
our difficulties in using language. As you can see in Figure 9.2, our example sentence is depicted in two different ways, depending on its meaning. By observing
tree diagrams of ambiguous sentences, psycholinguists can better pinpoint the
source of confusion.
A New Approach to Syntax: Transformational Grammar
In 1957, Noam Chomsky revolutionized the study of syntax. He suggested that to
understand syntax, we must observe not only the interrelationships among phrases
within sentences. Additionally, we have to consider the syntactical relationships between sentences. Specifically, Chomsky observed that particular sentences and their
tree diagrams show peculiar relationships.
For example, consider the following sentences:
S1: Susie greedily ate the crocodile.
S2: The crocodile was eaten greedily by Susie.
Oddly enough, a phrase-structure grammar would not show any particular relation at all between sentences S1 and S2. Indeed, phrase-structure analyses of S1 and
S2 would look almost completely different (Figure 9.3). Yet, the two sentences differ
only in voice. The first sentence is expressed in the active voice and the second in
382
CHAPTER 9 • Language
S
(a)
NP
Det
VP
N
V
PP
PP
P
NP
Det N
The
girl looked at
P
the boy
Det
N
with the telescope
S
(b)
VP
NP
Det
N
PP
V
P
NP
Det
P
N
The
girl
looked
at
the
Det
N
boy with the telescope
Figure 9.2 Phrase-Structure Grammar (part 1).
Phrase-structure grammars illustrate the hierarchies of phrases within sentences. Here you
can see two possible ways to analyze the sentence “The girl looked at the boy with the
telescope.” The abbreviations used in the tree diagrams are: S (sentence), NP (noun phrase),
VP (verb phrase), PP (prepositional phrase), N (noun), V (verb), Det (determiner), and
P (preposition).
S1:
S1
NP
N
S2
S2:
VP
Adv
NP
V
NP
VP
Det N
V
Adv
Det N
Susie
S3:
greedily
the crocodile
S3
P
VP
Adv
S4
NP
V
NP
N
VP
V
Adv
N
The crocodile greedily ate
Susie
N
The crocodile was eaten greedily by Susie
S4:
NP
Det N
ate
PP
PP
P
Susie was eaten greedily
by
Det
N
the crocodile
Figure 9.3 Phrase-Structure Grammar (part 2).
Phrase-structure grammars show surprising dissimilarities between sentences S1 and S2, yet surprising similarities
between S1 and S3 or between S2 and S4. Noam Chomsky suggested that to understand syntax, we also must
consider a way of viewing the interrelationships among various phrase structures.
Language Comprehension
383
the passive voice. But both sentences represent the same proposition “ate (greedily)
(Susie, crocodile).” Recall from Chapter 7 that propositions may be used to illustrate
that the same underlying meanings can be derived through alternative means of
representation.
Consider another pair of sentences that have the same meaning:
S3: The crocodile greedily ate Susie.
S4: Susie was eaten greedily by the crocodile.
Again, the sentences have the same meaning, but phrase-structure grammar
would show no relationship between S3 and S4. What’s more, phrase-structure
grammar would show some similarities of surface structure between S1 and S3 as
well as S2 and S4. The pairs of sentences clearly have quite different meanings,
particularly to Susie and the crocodile. Apparently, an adequate grammar would
address the fact that sentences with similar surface structures can have very different meanings.
This observation and other observations of the interrelationships among various
phrase structures led linguists to go beyond merely describing various individual
phrase structures. They began to focus their attention on the relationships among
different phrase structures. Linguists may gain deeper understanding of syntax by
studying the relationships among phrase structures that involve transformations of
elements within sentences (Chomsky, 1957). Specifically, Chomsky suggested a
way to supplement the study of phrase structures. He proposed the study of transformational grammar, which involves transformational rules. These rules guide the
ways in which an underlying proposition can be arranged into a sentence. There
are obviously many different sentences that can express the same proposition.
A simple way of looking at Chomsky’s transformational grammar is to say that
“Transformations … are rules that map tree structures onto other tree structures”
(Wasow, 1989, p. 170). For example, transformational grammar considers how the
tree-structure diagrams in Figure 9.3 are interrelated. With application of transformational rules, the tree structure of S1 can be mapped onto the tree structure of S2.
Similarly, the structure of S3 can be mapped onto the tree structure of S4.
In transformational grammar, deep structure refers to an underlying syntactical
structure that links various phrase structures through various transformation rules. In
contrast, surface structure refers to any of the various phrase structures that may
result from such transformations. Many casual readers of Chomsky have misunderstood Chomsky’s terms. They incorrectly inferred that deep structures refer to profound underlying meanings of sentences, whereas surface structures refer only to
superficial interpretations of sentences. This is not the case. Chomsky meant only
to show that differing phrase structures may have a relationship that is not immediately apparent by using phrase-structure grammar alone. For example, the sentences,
“Susie greedily ate the crocodile,” and “The crocodile was eaten greedily by Susie”
have a relationship that cannot be seen just by looking at the phrase-structure grammar. For detection of the underlying relationship between two phrase structures,
transformation rules must be applied.
Relationships between Syntactical and Lexical Structures
Chomsky (1965, cited in Wasow, 1989) also addressed how syntactical structures
may interact with lexical structures, that is, words. He suggested that our mental lexicon contains more than the semantic meanings attached to each word (or
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CHAPTER 9 • Language
morpheme). In addition, each lexical item also contains syntactical information.
This syntactical information for each lexical item indicates three things:
• the syntactical category of the item, such as noun versus verb;
• the appropriate syntactical contexts in which the particular morpheme may be
used, such as pronouns as subjects versus as direct objects; and
• any idiosyncratic information about the syntactical uses of the morpheme, such
as the treatment of irregular verbs.
For example, there would be separate lexical entries for the word spread categorized as a noun and for spread as a verb. Each lexical entry also would indicate which
syntactical rules to use for positioning the word. The rules that are applicable depend on which category is applicable in the given context. For example, as a verb,
spread would not follow the article the. As a noun, however, spread would be allowed
to do so. Even the peculiarities of syntax for a given lexical entry would be stored in
the lexicon. For example, the lexical entry for the verb spread would indicate that
this verb deviates from the normal syntactical rule for forming past tenses by adding
-ed to the stem used for the present tense.
You may wonder why we would clutter up our mental lexicon with so much
syntactical information. There is an advantage to attaching syntactical, contextsensitive, and idiosyncratic information to the items in our mental lexicon. If we
add to the complexity of our mental lexicon, we can simplify drastically the number
and complexity of the rules we need in our mental syntax. For example, by attaching
information about the idiosyncratic treatment of irregular verbs (e.g., spread or fall)
to our mental lexicon, we do not have to endure different syntactical rules for each
verb. By making our lexicon more complex, we allow our syntax to be simpler. In
this way, appropriate transformations may be simple and relatively context-free.
Once we know the basic syntax of a language, we easily can apply the rules to all
items in our lexicon. We then can gradually expand our lexicon to provide increasing complexity and sophistication.
Not all cognitive psychologists agree with all aspects of Chomsky’s theories (e.g.,
Bock, Loebell, & Morey, 1992; Devitt, 2008; Garrett, 1992; Jackendoff, 1991).
Many particularly disagree with his emphasis on syntax (form) over semantics
(meaning). The suggestion that syntactic rules influence the creation of a deep structure, which is then transformed through the application of more rules into a surface
structure, left psychologists wondering about the significance of meaning. A theory
that put so much emphasis on syntax seemed insufficient to explain the processes of
how we use language to express meaning. Nonetheless, several cognitive psychologists have proposed models of language comprehension and production that include
key ideas of syntax.
How do we link the elements in our mental lexicon to the elements in our syntactical structures? Various models for such bridging have been proposed (Bock, Loebell, & Morey, 1992; Culicover & Jackendoff, 2005; Jackendoff, 1991). According
to some of these models, when we parse sentences by syntactical categories, we create slots for each item in the sentence. Consider, for example, the sentence, “Juan
gave María the book from the shelf.” There is a slot for a noun used as: (1) a subject
(Juan); (2) as a direct object (the book); (3) as an indirect object (María); and (4)
as objects of prepositions (the shelf). There are also slots for the verb, the preposition, and the articles.
Language Comprehension
385
PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
SPEAKING WITH NON-NATIVE ENGLISH SPEAKERS
Given what you now know about processes of speech perception, semantics, and syntax,
think about ways to make your speech production easier for others to perceive. If you are
speaking to someone whose primary language differs from yours, try slowing down your
speech, thus exaggerating the length of time between words. Be sure to enunciate consonant sounds carefully, without making your vowel sounds too long. Use simpler sentence
constructions. Break down lengthy and involved sentences into smaller units. Insert longer
pauses between sentences to give the person time to translate the sentence into propositional form. Communication may feel more effortful but will probably be more effective.
Think about conversations with people who suffer from hearing impairments. How can you
help them understand you? Do you apply the same strategies as with foreigners, or maybe
some others?
In turn, lexical items contain information regarding the kinds of slots into
which the items can be placed. The information is based on the kinds of thematic
roles the items can fill. Thematic roles are ways in which items can be used in the
context of communication. Several roles have been identified. In particular, these
are the roles of:
•
•
•
•
•
•
•
the
the
the
the
the
the
the
agent, the “doer” of any action;
patient, the direct recipient of the action;
beneficiary, the indirect recipient of the action;
instrument, the means by which the action is implemented;
location, the place where the action occurs;
source, where the action originated; and
goal, where the action is going (Bock, 1990; Fromkin & Rodman, 1988).
According to this view of how syntax and semantics are linked, the various syntactical slots can be filled by lexical entries with corresponding thematic roles. For
example, the slot of subject noun might be filled by the thematic role of agent.
Nouns that can fill agent roles can be inserted into slots for subjects of phrases. Patient roles correspond to slots for direct objects. Beneficiary roles fit with indirect
objects, and so on. Nouns that are objects of prepositions may be filled with various
thematic roles. These roles include location, such as “at the beach”; source, such as
“from the kitchen”; and goal, such as “to the classroom.”
CONCEPT CHECK
1. What is coarticulation, and why is it important?
2. What does the view of speech perception as ordinary suggest?
3. What is categorical perception?
4. Describe a study that is evidence for the motor theory of speech perception.
5. What is syntactical priming?
6. What is the difference between phrase-structure grammar and transformational grammar?
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Reading
Because reading is so complex, a discussion of how we engage in this process could
be placed in any of a number of chapters in this book. At minimum, reading involves perception, language, memory, thinking, and intelligence (Adams, 1990,
1999; Garrod & Daneman, 2003; Smith, 2004): You have to recognize the letters
on this page, put them together to form words that have meaning, keep their meaning in memory until you have finished reading the sentence or even paragraph, and
think about what message the writer tried to communicate to you. Although there
are so many different processes going on, we read with remarkable speed and accuracy: the average adult reads prose at about 250-300 words per minute.
In a typical day, we repeatedly encounter written language. Every day we see
signs, billboards, labels, and notices. These items contain a wealth of information
that helps us make decisions and understand situations. As a result, the ability to
read is fundamental to our everyday lives.
When Reading Is a Problem—Dyslexia
To better understand what processes are involved in reading, let us first look at people who have trouble reading. People who have dyslexia—difficulty in deciphering,
reading, and comprehending text—can suffer greatly in a society that puts a high
premium on fluent reading (Sternberg & Spear-Swerling, 1999; Terras et al.,
2009). Problems in phonological processing, and thus in word identification, pose
“the major stumbling block in learning to read” (Pollatsek & Rayner, 1989, p. 403;
see also Grodzinsky, 2003). Several different processes may be impaired in dyslexia:
• Phonological awareness, which refers to awareness of the sound structure of spoken language. A typical way of assessing phonological awareness is through a
phoneme-deletion task. Children are asked to say, for example, “goat” without
the “-t.” Another task that is used is phoneme counting. Children might be
asked how many different sounds there are in the word “fish.” The correct answer is three.
• Phonological reading, which entails reading words in isolation. Teachers sometimes call this skill “word decoding” or “word attack.” For measurement of the
skill, children might be asked to read words in isolation. Some of the words
might be quite easy; others, difficult. Individuals with dyslexia often have more
trouble recognizing the words in isolation than in context. When given context,
they use the context to figure out what the word means.
• Phonological coding in working memory. This process is involved in remembering
strings of phonemes that are sometimes confusing. It might be measured by comparing working memory for confusable versus non-confusable phonemes. For example, a child might be assessed for how well he or she remembers the string t,
b, z, v, g versus the string o, x, r, y, q. Most people will have more difficulty
with the first string. But individuals with dyslexia, who have problems in phonological coding in working memory, will have particular trouble.
• Lexical access refers to one’s ability to retrieve phonemes from long-term
memory. The question here is whether one can quickly retrieve a word from
long-term memory when it is seen. For example, if you see the word pond, do
you immediately recognize the word as pond, or does it take you a while to retrieve it?
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387
There are several different kinds of dyslexia. The most well-known kind is developmental dyslexia, which is difficulty in reading that starts in childhood and typically
continues throughout adulthood. Most commonly, children with developmental dyslexia have difficulty in learning the rules that relate letters to sounds (Démonet,
Taylor, & Chaix, 2004; Shaywitz & Shaywitz, 2005). A second kind of dyslexia is
acquired dyslexia, which is typically caused by traumatic brain damage. A perfectly
good reader who experiences a brain injury may acquire dyslexia (Coslett, 2003).
Developmental dyslexia is believed to have both biological and environmental
causes. A major dispute in the field is the role of each. People with developmental
dyslexia often have been found to have abnormalities in certain chromosomes, most
notably, 3, 6, and 15 (Paracchini, Scerri, & Monaco, 2007). Neuropsychological
studies suggest that readers with dyslexia exhibit hypoactivation (that is, too little
activation) in their left temporo-parietal cortex as compared with regular readers.
Other brain regions show atypical activation in dyslexic readers, for example, the
left prefrontal region (linked with working memory), the left middle and superior
temporal gyri (linked with receptive language), and the left occipito-temporal regions (associated with the visual analysis of letters; Gabrieli, 2009). However, educational interventions can help reduce the impairments in reading caused by dyslexia
(Bakker, 2006).
In the following section, we examine three different processes that contribute to
our ability to read: perceptual, lexical, and comprehension processes.
Perceptual Issues in Reading
A very basic but important step in reading is the activation of our ability to recognize letters. When you are reading, you somehow manage to perceive the correct
letter when it is presented in a wide array of typestyles and typefaces. For example,
you can perceive it correctly in capital and lowercase forms, and even in cursive
forms. Such aspects are called orthographic. You then must translate the letter into a
sound, creating a phonological code (relating to sound). This translation is particularly difficult in English because English does not always ensure a direct correspondence between a letter and a sound. George Bernard Shaw, playwright and lover of
the English language, observed the illogicality of English spellings. He suggested
that, in English, it would be perfectly reasonable to pronounce “ghoti” as “fish.”
You would pronounce the “gh” as in rough, the “o” as in women, and the “ti” as in
nation. That brings up another perplexing “Englishism”: How do you pronounce
“ough”? Try the words dough, bough, bought, through, and cough—had enough?
After you somehow manage to translate all those visual symbols into sounds,
you must sequence those sounds to form a word (Pollatsek & Miller, 2003). Then
you need to identify the word and figure out what the word means. Ultimately you
move on to the next word and repeat the process all over again. You continue this
process with subsequent words to formulate a single sentence. You continue this process for as long as you read. Clearly, the normal ability to read is not at all simple.
About 36 million American adults have not yet learned to read at an eighth-grade
level (Conn & Silverman, 1991). There were no significant changes in literacy between 1992 and 2003 (http://nces.ed.gov/naal/kf_demographics.asp). On the one
hand, the statistics on low literacy and illiteracy should alarm us and provoke us to
action. On the other hand, we may need to reconsider our possibly lessthan-favorable appraisal of those who have not yet mastered the task of reading.
To undertake such a challenge—at any age—is difficult indeed.
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When learning to read, novice readers must come to master two basic kinds of
processes: lexical processes and comprehension processes. Lexical processes are used
to identify letters and words. They also activate relevant information in memory
about these words. Comprehension processes are used to make sense of the text as
a whole (and are discussed later in this chapter). The separation and integration of
both bottom-up and top-down approaches to perception can be seen as we consider
the lexical processes of reading.
Lexical Processes in Reading
We are about to explore, in more detail, the lexical processes involved in reading.
First, we take a closer look at fixations in our eye movements that help us read.
Then, we discuss how we identify words so we can retrieve their meaning from our
memory (lexical access); and finally, we consider what connection there is between
lexical-access speed and intelligence.
Fixations and Reading Speed
When we read, our eyes do not move smoothly along a page or even along a line of
text. Rather, our eyes move in saccades—rapid sequential movements—as they fixate
on successive clumps of text. The fixations are like a series of “snapshots” (Pollatsek
& Rayner, 1989), and are of variable length (Carpenter & Just, 1981). Readers fixate for a longer time on longer words than on shorter words. They also fixate longer
on less familiar words (i.e., words that appear less frequently in the English language)
than on more familiar words (i.e., words of higher frequency). The last word of a
sentence also seems to receive an extra long fixation time. This can be called “sentence wrap-up time” (Carpenter & Just, 1981; Warren et al., 2009).
Although most words are fixated, not all of them are. Readers fixate up to about
80% of the content words in a text. These words include nouns, verbs, and other
words that carry the bulk of the meaning. (Function words, such as the and of, serve
a supporting role to the content words.) Just what is the visual span of one of these
fixations? It appears that we can extract useful information from a perceptual window of characters about four characters to the left of a fixation point and about 14
or 15 characters to the right of it. These characters include letters, numerals, punctuation marks, and spaces. Saccadic movements leap an average of about seven to
nine characters between successive fixations. So some of the information we extract
may be preparatory for subsequent fixation (Pollatsek & Rayner, 1989; Rayner et al.,
1995). When students speed-read, they show fewer and shorter fixations (Just,
Carpenter, & Masson, 1982). But apparently their greater speed is at the expense
of comprehension of anything more than just the gist of the passage (Homa, 1983).
Lexical Access
An important aspect of reading is lexical access—the identification of a word that
allows us to gain access to the meaning of the word from memory. Most psychologists who study reading believe that lexical access is an interactive process. It combines information of different kinds, such as the features of letters, the letters
themselves, and the words comprising the letters (Morton, 1969).
Investigators (McClelland et al., 2009; Rumelhart & McClelland 1981, 1982)
developed an interactive-activation model suggesting that activation of particular
lexical elements occurs at multiple levels. Moreover, activity at each of the levels is
interactive (Figure 9.4).
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Figure 9.4 Word Recognition.
David Rumelhart and James McClelland used this figure to illustrate how activation at the feature level, the letter level, and
the word level may interact during word recognition. In this figure, lines terminating in arrows prompt activation, and
lines terminating in dots (blue circles) prompt inhibition. For example, the feature for a solid horizontal bar at the top of a
letter leads to activation of the T character but to inhibition of the N character. Similarly, at the letter level, activation of T
as the first letter leads to activation of TRAP and TRIP but to inhibition of ABLE. Going from the top down, activation of the
word TRAP leads to inhibition of A, N, G, and S as the first letter but to activation of T as the first letter.
Source: From Richard E. Meyer, “The Search for Insight: Grappling with Gestalt Psychology’s Unanswered Questions,” in The Nature of Insight, edited by R. J. Sternberg and J. E. Davidson. Copyright © 1995 MIT Press. Reprinted with permission from MIT Press.
The interactive-activation model distinguishes among three levels of processing
following visual input—the feature level, the letter level, and the word level. The
model assumes that information at each level is represented separately in memory.
Information passes from one level to another bidirectionally. In other words, processing occurs in each of two directions. First, it is bottom-up, starting with sensory data
and working up to higher levels of cognitive processing. Second, it is top-down,
starting with high-level cognition operating on prior knowledge and experiences related to a given context. The interactive view implies that not only do we use the
visually or orally perceptible features of letters to help us identify words, but we also
use the features we already know about words to help us identify letters. For this
reason, the model is referred to as “interactive” (Plaut et al., 1996).
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Other theorists have suggested alternatives to Rumelhart and McClelland’s
model (e.g., Meyer & Schvaneveldt, 1976; Paap et al., 1982), but the distinctions
among interactive models go beyond the scope of this introductory text. Support for
word-recognition models involving discrete levels of processing comes from studies
of cerebral processing (Harley, 2008; Petersen et al., 1988; Posner et al., 1988,
1989). Studies that map brain metabolism indicate that different regions of the brain
become activated during passive visual processing of word forms, as opposed to semantic analysis of words or even spoken pronunciation of the words. These studies
involve the use of techniques such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), discussed in Chapter 2.
In addition to neuropsychological support, a number of word-recognition models
have been simulated on computers (e.g., Harm & Seidenberg, 2004). Both models
aptly predicted a word-superiority effect as well as a pseudoword-superiority effect.
The word-superiority effect is similar to the configural-superiority effect and the
object-superiority effect (mentioned in regard to top-down influences on perception).
In the word-superiority effect, letters are read more easily when they are embedded
in words than when they are presented either in isolation or with letters that do not
form words. People take substantially longer to read unrelated letters than to read
letters that form a word (Cattell, 1886). This effect is sometimes called the
Reicher-Wheeler effect, named for two researchers who did early investigations of
this effect (Reicher, 1969; Wheeler, 1970).
To observe the word-superiority effect, researchers use an experimental paradigm
called the lexical-decision task. In this paradigm, a string of letters is presented very
briefly. It then is either removed or covered by a visual mask, a pattern that wipes
out the previously presented stimulus from iconic memory (see Chapter 5 for more
information about the iconic memory store). The participant then is asked to make
a decision about whether the string of letters is a word.
To observe the word-superiority effect, the standard lexical-decision task is modified to examine the processing of letters. Participants are presented very briefly with
either a word or a single letter, followed by a visual mask. Participants then are given
a choice of two letters and have to decide which letter they just saw. For example,
participants may be presented with the word “WORK” when the test stimulus
is “K.” The alternatives to choose from might be “D” and “K.” They are presented
as “_ _ _ D” and “_ _ _ K,” which correspond to the target “WORK” and a similar
word “WORD,” respectively. Participants then are instructed to choose the letter they
saw. Participants are more accurate in choosing the correct letter when it is presented
in the context of a word than they are in choosing the correct letter when it is presented in isolation (Johnston & McClelland, 1973). Even letters in pronounceable
pseudowords (e.g., “MARD”) are identified more accurately than letters in isolation.
However, strings of letters that cannot be pronounced as words (e.g., “ORWK”) do
not aid in identification (Grainger et al., 2003; Pollatsek & Rayner, 1989).
There is also a sentence-superiority effect (Cattell, 1886; Perfetti, 1985): People
take about twice as long to read unrelated words as to read words in a sentence
(Cattell, 1886). The sentence-superiority effect can be seen in other paradigms as
well. For example, suppose that a reader very briefly sees a degraded stimulus. The
word window, for example, might be shown but in degraded form (Figure 9.5).
When the word is standing by itself in this form, it is more difficult to recognize
than when it is preceded by a sentence context. An example of such a context would
.”
be, “There were several repair jobs to be done. The first was to fix the
Reading
391
0%
21%
42%
Figure 9.5 Word Degradation.
This figure shows instances of the word “window” and of the word “pepper,” in which each
word is clearly legible, somewhat legible, or almost completely illegible. Percentages indicate
degree of degradation.
(Perfetti, 1985). Having a meaningful context for a stimulus helps the reader to perceive it.
Context effects work at both conscious and preconscious levels. At the conscious level, we have active control over the use of context to determine word
meanings. At the preconscious level, the use of context is probably automatic and
outside our active control. Participants seem to make lexical decisions more quickly
when presented with strings of letters that commonly are associated pairs of words
(e.g., “doctor” and “nurse” or “bread” and “butter”). They respond more slowly
when presented with unassociated pairs of words, with pairs of non-words, or with
pairs involving a word and a non-word (Hyoenae, J., & Lindeman, 2008; Meyer &
Schvaneveldt, 1971; Schvaneveldt, Meyer, & Becker, 1976).
Intelligence and Lexical-Access Speed
Some investigations on information processing and intelligence have focused on
lexical-access speed—the speed with which we can retrieve information about words
(e.g., letter names) stored in our long-term memories (Hunt, 1978). This speed can
be measured with a letter-matching, reaction-time task first proposed by Posner and
Mitchell in 1967 (Hunt, 1978).
Participants are shown pairs of letters, such as “A A,” “A a,” or “A b.”
For each pair, they indicate whether the letters constitute a match in name (e.g.,
“A a” match in name of letter of the alphabet but “A b” do not). They also are
given a simpler task where they are asked to indicate whether the letters match
physically (e.g., “A A” are physically identical, whereas “A a” are not). The variable of interest is the difference between their speed for the first set of tasks, involving name-matching, and their speed for the second set, involving matching of
physical characteristics. The difference in reaction time between the two kinds of
tasks is said to provide a measure of speed of lexical access. This score is based on
a subtraction of name-match minus physical-match reaction time. The subtraction
controls for mere perceptual-processing time. Students with lower verbal ability
take longer to gain access to lexical information than do students with higher verbal ability (Hunt, 1978). These results suggest that lexical access is a component of
verbal ability.
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CONCEPT CHECK
1. Which processes can be impaired in dyslexia?
2. What is lexical access?
3. Give an example for the word-superiority effect.
Understanding Conversations and Essays: Discourse
The preceding sections discussed, at a general level, aspects of how we understand
written and spoken language. However, in our understanding of language, not only
do words and sentences play a role, but so does the greater context in which they
appear. This section discusses more specifically the processes involved in understanding and using language in the greater context in which we encounter it. Discourse
involves units of language larger than individual sentences—in conversations, lectures, stories, essays, and even textbooks (Di Eugenio, 2003). Just as grammatical
sentences are structured according to systematic syntactical rules, passages of discourse are structured systematically (see Investigating Cognitive Psychology: Discourse).
By adulthood, most of us have a firm grasp of how sentences are sequenced into
a greater whole (discourse structure). From our knowledge of discourse structure, we
can derive meanings of sentence elements that are not apparent by looking at isolated sentences. To see how sentences influence the interpretation of other
sentences, try out the Investigating Cognitive Psychology: Deciphering Text box.
INVESTIGATING COGNITIVE PSYCHOLOGY
Discourse
The following series of sentences is taken from a short story by O. Henry (William
Sydney Porter, 1899–1953) titled “The Ransom of Red Chief.” Actually, the following
sequence of sentences is incorrect. Without knowing anything else about the story, try
to figure out the correct sequence of sentences.
1.
The father was respectable and tight, a mortgage financier and a stern, upright
collection-plate passer and forecloser.
2.
We selected for our victim the only child of a prominent citizen named Ebenezer
Dorset.
3.
We were down South in Alabama—Bill Driscoll and myself—when this kidnapping
idea struck us.
4.
Bill and me figured that Ebenezer would melt down for a ransom of two thousand
dollars to a cent.
Hint: O. Henry was a master of irony, and by the end of the story the would-be
kidnappers paid the father a hefty ransom to take back his son so that they could quickly
escape from the boy.
The sequence used by O. Henry, ex-convict and expert storyteller, was 3, 2, 1, 4.
Is that the order you chose? How did you know the correct sequence for these
sentences?
Understanding Conversations and Essays: Discourse
393
INVESTIGATING COGNITIVE PSYCHOLOGY
Deciphering Text
Rita gave Thomas a book about problem solving. He thanked her for the book. She
asked, “Is it what you wanted?” He answered enthusiastically, “Yes, definitely.” Rita
asked, “Should I get you the companion volume on decision making?” He responded,
“Please do.”
In the second and third sentences, who were the people and things being referred
to with the pronouns “He,” “her,” “She,” and “it”? Why was the noun “book” preceded
by the article “a” in the first sentence and by the article “the” in the second one? How do
you know what Thomas’s answer, “Yes, definitely,” means? What is the action being
requested in the response, “Please do”?
Cognitive psycholinguists who analyze discourse particularly are intrigued by
how we are able to answer the questions posed in the preceding example. When
grasping the meanings of pronouns (e.g., he, she, him, her, it, they, them, we, us),
how do we know to whom (or to what) the pronouns are pointing? How do we
know the meanings of what could seem like cryptic utterances (e.g., “Yes, definitely”)? What does the use of the definite article the (as opposed to the indefinite
article a) signify to listeners regarding whether a noun was mentioned previously?
How do you know what event is being referenced by the verb do? The meanings of
pronouns, ellipses, definite articles, event references, and other local elements within
sentences usually depend on the discourse structure within which these elements appear (Grosz, Pollack, & Sidner, 1989).
For understanding discourse, we often rely not only on our knowledge of discourse structure but also on our knowledge of a broad physical, social, or cultural
context within which the discourse is presented (Cook & Gueraud, 2005; van Dijk,
2006). Our understanding of the meaning of a paragraph is influenced by our existing
knowledge and expectations. For example, this cognitive psychology textbook will be
easier to read if you have taken an introductory psychology course than if you have
not taken such a course. When reading the sentences in the Investigating Cognitive
Psychology: Effects of Expectations in Reading box, pause between sentences and think
about what you know and what you expect, based on your knowledge.
The next sections explore in more detail how we comprehend larger units of
language, like essays. We discuss how we retrieve known words from memory and
how we infer the meaning of new words. We explore how we understand ideas communicated in text and how our interpretation depends on our point of view. Finally,
we consider how we can represent text in mental models.
Comprehending Known Words: Retrieving Word
Meaning from Memory
Semantic encoding is the process by which we translate sensory information (that is,
the written words we see) into a meaningful representation. This representation is
based on our understanding of the meanings of words. In lexical access, we identify
words based on letter combinations. We thereby activate our memory in regard to
the words. In semantic encoding, we take the next step and gain access to the
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CHAPTER 9 • Language
INVESTIGATING COGNITIVE PSYCHOLOGY
Effects of Expectations in Reading
1.
Susan became increasingly anxious as she prepared for the upcoming science
exam. (What do you know about Susan?)
2.
She had never written an exam before, and she wasn’t sure how to construct an
appropriate test of the students’ knowledge. (How have your beliefs about Susan
changed?)
3.
She was particularly annoyed that the principal had even asked her to write the
exam.
4.
Even during a teachers’ strike, a school nurse should not be expected to take on the
task of writing an examination. (How did your expectations change over the course
of the four sentences?)
In the preceding example, your understanding at each point in the discourse was influenced by your existing knowledge and expectations based on your own experiences
within a particular context. Thus, just as prior experience and knowledge may aid us in
lexical processing of text, so may they also aid us in comprehending the text itself. What
are the main reading-comprehension processes? The process of reading comprehension
is so complex that many entire courses and myriad volumes are devoted exclusively to
the topic, but we focus here on just a few processes. These include semantic encoding,
acquiring vocabulary, comprehending ideas in text, creating mental models of text, and
comprehending text based on context and point of view.
meaning of the word stored in memory. Sometimes we cannot semantically encode
the word because its meaning does not already exist in memory. We then must find
another way in which to derive the meanings of words, such as from noting the context in which we read them.
To engage in semantic encoding, the reader needs to know what a given word
means. Knowledge of word meanings (vocabulary) very closely relates to the ability
to comprehend text. People who are knowledgeable about word meanings tend to be
good readers and vice versa. A reason for this relationship appears to be that readers
simply cannot understand text well unless they know the meanings of the component words. For example, in one study, recall of the semantic content of a passage
was much better when participants had a greater relevant vocabulary (Beck, Perfetti,
& McKeown, 1982). In children, vocabulary size is positively related to performance
on a number of semantic-understanding tasks, including retelling (both written and
oral), decoding ability, and the ability to draw inferences across sentences (Hagtvet,
2003). A number of studies suggest that in order to grasp meaning of a sample of
text with ease, one should know approximately 95% of the vocabulary (Nation,
2001; Read, 2000). Still other studies suggest that, for one to enjoy reading a text,
one needs to understand about 98% of the vocabulary (Hu & Nation, 2000).
People with larger vocabularies are able to access lexical information more rapidly than are those with smaller vocabularies (Hunt, 1978). Verbal information often is presented rapidly—whether in listening or in reading. The individual who can
gain access to lexical information rapidly is able to process more information per
unit of time than can one who can only gain access to such information slowly.
Understanding Conversations and Essays: Discourse
395
Comprehending Unknown Words: Deriving
Word Meanings from Context
Another way in which having a larger vocabulary contributes to text comprehension
is through learning from context. Whenever we cannot semantically encode a word
because its meaning is not already stored in memory, we must engage in some kind
of strategy to derive meaning from the text. In general, we must either search for a
meaning, using external resources, such as dictionaries or teachers, or formulate a
meaning. Using context cues, we formulate the meaning based on the existing information stored in memory.
People learn most of their vocabulary indirectly. They do so not by using external resources but by figuring out the meanings of the flidges from the surrounding
information (Werner & Kaplan, 1952).
For example, if you tried to look up the word flidges in the dictionary, you did
not find it there. From the structure of the sentence you probably figured out that
flidges is a noun. From the surrounding context you probably figured out that it is a
noun having something to do with words or vocabulary. In fact, flidges is a nonsense
word we used as a placeholder for the word words to show how you would gain a
fairly good idea of a word’s meaning from its context.
One study found that the ability to figure out meanings of words from context
was impaired in children with low reading comprehension. If those children had
good vocabularies, however, direct instruction could help them learn the meanings
of new words just as well as did children with high reading comprehension (Cain,
Oakhill, & Lemmon, 2004).
What happens when adults have to learn word meanings from sentence contexts? Studies have found that people with large or small vocabularies (high verbal/
low verbal) learn word meanings differently. High-verbal participants perform a deeper analysis of the possibilities for a new word’s meaning than do low-verbal participants. In particular, the high-verbal participants used a well-formulated strategy for
figuring out word meanings. The low-verbal participants seemed to have no clear
strategy at all (van Daalen-Kapteijns & Elshout-Mohr, 1981; see also Sternberg &
Powell, 1983).
Comprehending Ideas: Propositional Representations
What factors influence our comprehension of what we read? Walter Kintsch has developed a model of text comprehension based on his observations (Kintsch, 1990,
2007; Kintsch & van Dijk, 1978). According to the model, as we read, we try to
hold as much information as possible in working (active) memory to understand
what we read. However, we do not try to store the exact words we read in working
(active) memory. Rather, we try to extract the fundamental ideas from groups of
words. We then store those fundamental ideas in a simplified representational form
in working memory.
The representational form for these fundamental ideas is the proposition. Propositions were defined in more detail in Chapter 7. For now, it suffices to say that a
proposition is the briefest unit of language that can be independently found to be
true or false. For example, the sentence, “Penguins are birds, and penguins can fly”
contains two propositions. You can verify independently whether penguins are birds
and whether penguins can fly. In general, propositions assert either an action (e.g.,
flying) or a relationship (e.g., membership of penguins in the category of birds).
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According to Kintsch, working memory holds propositions rather than words. Its
limits are thus taxed by large numbers of propositions rather than by any particular
number of words (Kintsch & Keenan, 1973). When a string of words in text requires
us to hold a large number of propositions in working memory, we have difficulty
comprehending the text. When information stays in working memory a longer
time, it is better comprehended and better recalled subsequently. Because of the limits of working memory, however, some information must be moved out of working
memory to make room for new information.
According to Kintsch, propositions that are thematically central to the understanding of the text will remain in working memory longer than propositions that
are irrelevant to the theme of the text passage. Kintsch calls the thematically crucial
propositions macropropositions. He further calls the overarching thematic structure of
a passage of text the macrostructure. In an experiment testing his model, Kintsch and
an associate asked participants to read a 1,300-word text passage (Kintsch & van
Dijk, 1978). The participants then had to summarize the key propositions in the passage immediately, at one month, or at three months after reading the passage. What
happened after three months? Participants recalled the macropropositions and the
overall macrostructure of the passage about as well as could participants who summarized it immediately after reading it. However, the propositions providing nonthematic details about the passage were not recalled as well after one month and
not at all well after three months.
Comprehending Text Based on Context
and Point of View
What we remember from a given passage of text often depends on our point of view.
For example, suppose that you were reading a text passage about the home of a
wealthy family. It described many of the features of the house, such as a leaky roof,
a fireplace, and a musty basement. It also described the contents of the house, such
as valuable coins, silverware, and television sets. How might your encoding and
comprehension of the text be different if you were reading it from the point of
view of a prospective purchaser of the home as opposed to the viewpoint of a prospective cat burglar? In a study using just such a passage, people who read the passage from the viewpoint of a cat burglar remembered far more about the contents of
the home. In contrast, those who read from the viewpoint of a homebuyer remembered more about the condition of the house (Anderson & Pichert, 1978). In fact,
varying the retrieval situations or cues can cause different details to be remembered.
Researchers found that differing retrieval instructions did not affect accuracy but did
affect the specific details recalled (Gilbert & Fisher, 2006).
Representing the Text in Mental Models
Once words are semantically encoded or their meaning is derived from the use of
context, the reader still must create a mental model of the text that is being read.
This mental model simulates what is going on in the world (Craik, 1943; see
Johnson-Laird, 1989, 2010). A mental model may be viewed as a sort of internal
working model of the situation described in the text, as the reader understands it.
In other words, the reader creates some sort of mental representation that contains
the main elements of the text. These elements are represented in a way that is relatively easy to grasp or at least that is simpler and more concrete than the text itself.
Understanding Conversations and Essays: Discourse
397
For example, suppose that you read the sentence, “The loud bang scared Alice.” You
may form a picture of Alice becoming scared on hearing a loud noise. Or you may
access propositions stored in memory regarding the effects of loud bangs.
A given passage of text or even a given set of propositions (to refer back to
Kintsch’s model) may lead to more than one mental model (Johnson-Laird, 1983).
In fact, you may need to modify your mental model. Whether you do so depends on
whether the next sentence is, “She tried to steer off the highway without losing control of the car,” or “She ducked to avoid being shot.” In representing the loud bang
that scared Alice, more than one mental model is possible. If you start out with a
different model than the one required in a given passage, your ability to comprehend
the text depends on your ability to form a new mental model. You can hold in mind
only a limited number of mental models at any given time (Johnson-Laird, Byrne, &
Schaeken, 1992). Therefore, when one of the models is incorrect, it must be rejected
to make room for new models.
To form mental models, you must make at least tentative inferences (preliminary conclusions or judgments) about what is meant but not said. In the first case,
you are likely to assume that a tire blew out. In the second case, you may infer that
someone is shooting a gun. Note that neither of these things is stated explicitly. The
construction of mental models illustrates that, in addition to comprehending the
words themselves, we also need to understand how words combine into meaningfully
integrated representations of narratives or expositions. Passages of text that lead unambiguously to a single mental model are easier to comprehend than are passages
that may lead to multiple mental models (Johnson-Laird, 1989).
Inferences can be of different kinds. One of the most important kinds is a bridging inference (Haviland & Clark, 1974; Mc Namara et al., 2006). This is an inference a reader or listener makes when a sentence seems not to follow directly from
the sentence preceding it. In essence, what is new in the second sentence goes one
step too far beyond what is given in the previous sentences. Consider, for example,
two pairs of two sentences:
1. John took the picnic out of the trunk. The beer was warm.
2. John took the beer out of the trunk. The beer was warm.
Readers took about 180 milliseconds longer to read the first pair of sentences
than the second. Haviland and Clark suggested a reason for this greater processing
time. It was that, in the first pair, information needed to be inferred (the picnic included beer) that was directly stated in the second pair.
Although most researchers emphasize the importance of inference-making in
reading and forms of language comprehension (e.g., Graesser & Kreuz, 1993; Cain
& Oakhill, 2007), not all researchers agree. According to the minimalist hypothesis,
readers make inferences based only on information that is easily available to them.
They do so only when they need to make such inferences to make sense of adjoining
sentences (McKoon & Ratcliff, 1992a; Ratcliff & McKoon, 2008). We believe that
the bulk of the evidence regarding the minimalist position indicates that it is itself
too minimalist. Readers appear to make more inferences than this position suggests
(Suh & Trabasso, 1993; Trabasso & Suh, 1993).
To summarize, our comprehension of what we read depends on several abilities.
First is gaining access to the meanings of words, either from memory or on the basis
of context. Second is deriving meaning from the key ideas in what we read. Third is
extracting the key information from the text, based on the contexts surrounding
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CHAPTER 9 • Language
INVESTIGATING COGNITIVE PSYCHOLOGY
Using Redundancy to Decipher Cryptic Text
Read the following passage:
Aoccdrnig to a rseearch at an Elingsh uinervtisy, it dseon’t mttaer in
waht oredr the ltteers in a wrod are; the olny iprmoatnt tihng is that the
frist and lsat ltteres are at the rghit pclae. The rset can be a toatl mses
and you can sitll raed it wouthit porbelm. Tihs is bcuseae we do not
raed ervey lteter by itslef but the wrod as a wlohe.
Although most people cannot read the above passage as quickly as they can if all
the letters are in the right order, they still can understand what the passage says.
what we read and on the ways in which we intend to use what we read. And fourth
is forming mental models that simulate the situations about which we read.
CONCEPT CHECK
1. What is discourse?
2. What technique can you apply when you come across a word you don’t know in a
text?
3. Does readers’ point of view influence their text comprehension?
4. Is there a limit to the number or complexity of mental models one can have about a
given text?
Key Themes
This chapter deals with a number of the major themes reviewed in Chapter 1.
Rationalism versus empiricism. Most psychologists emphasize empirical techniques in their research. But linguists such as Chomsky have emphasized more rationalistic techniques. They analyze language, typically without formally collecting
empirical data at all, at least in the cognitive psychologists’ sense of what constitutes
such data. The stunning insights of Chomsky show that the two methods complement each other. Many insights can evolve from rationalism. They then can be
tested by empirical methods.
Domain generality versus domain specificity. In particular, to what extent is
language special? Is it a domain apart from other domains, or simply one more cognitive domain like any other? Many psychologists today believe that there is indeed
something special about language. At the same time, cognitive processes operate on
it so that people use their language in practically all the other domains in which
they work. For example, many mathematical and physical problems are presented
with words.
Summary
1. What properties characterize language? There
are at least six properties of language, defined as
the use of an organized means of combining
words in order to communicate. (1) Language
permits us to communicate with one or more
people who share our language. (2) Language
Summary
creates an arbitrary relationship between a symbol and its referent—an idea, a thing, a process,
a relationship, or a description. (3) Language
has a regular structure; only particular sequences of symbols (sounds and words) have
meaning. Different sequences yield different
meanings. (4) The structure of language can
be analyzed at multiple levels (e.g., phonemic
and morphemic). (5) Despite having the limits
of a structure, language users can produce novel
utterances; the possibilities for generating new
utterances are virtually limitless. (6) Languages
constantly evolve.
Language involves verbal comprehension—
the ability to comprehend written and spoken
linguistic input, such as words, sentences, and
paragraphs. It also involves verbal fluency—the
ability to produce linguistic output. The smallest units of sound produced by the human vocal tract are phones. Phonemes are the smallest
units of sound that can be used to differentiate
meaning in a given language. The smallest semantically meaningful unit in a language is a
morpheme. Morphemes may be either roots or
affixes—prefixes or suffixes. Affixes in turn may
be either content morphemes, conveying the
bulk of the word’s meaning, or function morphemes, augmenting the meaning of the word.
A lexicon is the repertoire of morphemes in a
given language (or for a given language user).
The study of the meaningful sequencing of
words within phrases and sentences in a given
language is syntax. Larger units of language are
embraced by the study of discourse.
2. What are some of the processes involved in
language? In speech perception, listeners must
overcome the influence of coarticulation (overlapping) of phonemes on the acoustic structure
of the speech signal. Categorical perception is
the phenomenon in which listeners perceive
continuously varying speech sounds as distinct
categories. It lends support to the notion that
speech is perceived via specialized processes.
The motor theory of speech perception attempts to explain these processes in relation
to the processes involved in speech production.
Those who believe speech perception is
399
ordinary explain speech perception in terms of
feature-detection, prototype, and Gestalt theories of perception.
Syntax is the study of the linguistic structure
of sentences. Phrase-structure grammars analyze
sentences in terms of the hierarchical relationships among words in phrases and sentences.
Transformational grammars analyze sentences in terms of transformational rules that
describe interrelationships among the structures
of various sentences. Some linguists have suggested a mechanism for linking syntax to semantics. By this mechanism, grammatical sentences
contain particular slots for syntactical categories. These slots may be filled by words that
have particular thematic roles within the sentences. According to this view, each item in a
lexicon contains information regarding appropriate thematic roles, as well as appropriate syntactical categories.
3. How do perceptual processes interact with the
cognitive processes of reading? The reading
difficulties of people with dyslexia often relate
to problems with the perceptual aspects of
reading.
Reading comprises two basic kinds of processes: (1) lexical processes, which include sequences of eye fixations and lexical access; and
(2) comprehension processes.
4. How does discourse help us understand individual words? Obviously, we can understand
discourse only through analysis of words. But
sometimes we understand words through discourse. For one example, sometimes in a conversation or watching a movie, we miss a word. The
context of the discourse helps us figure out what
the word was likely to be. As a second example,
sometimes a word can have several meanings,
such as “well.” We use discourse to help us figure
out which meaning is intended. As a third example, sometimes we realize, through discourse,
that a word is intended to mean something different from its actual meaning, as in “Yeah,
right!” Here, “right” is likely to be intended to
mean “not really right at all.” So discourse helps
us understand individual words, just as the individual words help us understand discourse.
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CHAPTER 9 • Language
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe the six key properties of language.
2. In your opinion, why do some view speech
perception to be special, whereas others consider speech perception to be ordinary?
3. Compare and contrast the speech-is-ordinary
and speech-is-special views, particularly in reference to categorical perception and phonemic
restoration.
4. How do phrase-structure diagrams reveal the
alternative meanings of ambiguous sentences?
5. Write a noun phrase and a verb phrase. How are
they different?
6. In this chapter, we saw that passive-voice sentences can be transformed into active-voice
sentences using transformation rules. What are
some other kinds of sentence structures that are
related to one another? In your own words, state
the transformation rules that would govern the
changes from one form to another.
7. Based on the discussion of reading in this
chapter, what practical suggestion could you
recommend that might make reading easier for
someone who is having difficulty reading?
Key Terms
categorical perception, p. 372
coarticulation, p. 369
communication, p. 361
comprehension processes, p. 388
connotation, p. 375
content morphemes, p. 366
deep structure, p. 383
denotation, p. 374
discourse, p. 392
dyslexia, p. 386
function morphemes, p. 366
grammar, p. 377
language, p. 360
lexical access, p. 388
lexical processes, p. 388
lexicon, p. 367
morpheme, p. 365
noun phrase, p. 367
phoneme, p. 365
phonemic-restoration effect,
p. 371
phrase-structure grammar, p. 379
psycholinguistics, p. 361
referent, p. 362
semantics, p. 368
surface structure, p. 383
syntax, p. 367
thematic roles, p. 385
transformational grammar, p. 383
verb phrase, p. 367
word-superiority effect, p. 390
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Categorical Perception Identification
Discrimination
Suffix Effect
Lexical Decision
Word Superiority
10
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Language in Context
CHAPTER OUTLINE
Language and Thought
Differences among Languages
The Sapir-Whorf Hypothesis
Linguistic Relativity or Linguistic Universals?
Bilingualism and Dialects
Bilingualism—An Advantage or Disadvantage?
Factors That Influence Second Language
Acquisition
Bilingualism: One System or Two?
Language Mixtures and Change
Neuroscience and Bilingualism
Slips of the Tongue
Metaphorical Language
Language in a Social Context
Speech Acts
Direct Speech Acts
Indirect Speech Acts
Characteristics of Successful Conversations
Gender and Language
Do Animals Have Language?
Neuropsychology of Language
The Brain and Word Recognition
The Brain and Semantic Processing
The Brain and Syntax
The Brain and Language Acquisition
The Placisticity of the Brain
The Brain and Sex Differences
in Language Processing
The Brain and Sign Language
Aphasia
Wernicke’s Aphasia
Broca’s Aphasia
Global Aphasia
Anomic Aphasia
Autism
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
Brain Structures Involved in Language
401
402
CHAPTER 10 • Language in Context
Here are some of the questions we will explore in this chapter:
1. How does language affect the way we think?
2. How does our social context influence our use of language?
3. How can we find out about language by studying the human brain, and what do such studies reveal?
n BELIEVE IT OR NOT
IS IT POSSIBLE
TO
COUNT WITHOUT WORDS
FOR
NUMBERS?
Not all cultures in the world have developed words for
numbers. Even if they do have counting systems and words
for numbers, those systems and words may be quite different. The Piraha tribe, which lives along the banks of the
Amazon River in Brazil, has just three number words—one
for the number 1, one for the number 2, and one that
indicates “many.” Does this lack of number words interfere
with people’s ability to deal with larger numerical quantities? Peter Gordon conducted experiments with members
of the Piraha tribe and found that indeed, it does. He
presented them with matching tasks where he lined up
specific numbers of batteries and asked them to line up
an equal amount. Although the Piraha were able to complete this task well for numbers of up to three, their performance declined as the numbers increased. This finding
may indicate that we do not have an innate ability to count
beyond small numbers. A lack of words for larger numbers
may prevent people from thinking about those larger quantities (Gordon, 2004). In this chapter, we explore how
people use language in a social context, and how the
environment influences people’s language and cognition.
“My surgeon was a butcher.”
“His house is a rat’s nest.”
“Her sermons are sleeping pills.”
“He’s a real toad, and he always dates real dogs.”
“Abused children are walking time bombs.”
“My boss is a tiger in board meetings but a real pussycat with me.”
“Billboards are warts on the landscape.”
“My cousin is a vegetable.”
“John’s last girlfriend chewed him up and spit him out.”
Not one of the preceding statements is literally true. Yet fluent readers of English
have little difficulty comprehending these metaphors and other non-literal forms of
language. How do we comprehend them? One of the reasons that we can understand
non-literal uses of language is that we can interpret the words we hear within a
broader linguistic, cultural, social, and cognitive context.
In this chapter, we first focus on the cognitive context of language—we look at
how language and thought interact. Next, we discuss some uses of language in its
social context. Then we explore animal language because it puts human language
in perspective. Finally, we examine some neuropsychological insights into language.
Although the topics in this chapter are diverse, they all have one element in common: They address the issue of how language is used in the everyday contexts in
which we need it to communicate with others and to make our communications as
meaningful as we possibly can.
Language and Thought
403
Language and Thought
One of the most interesting areas in the study of language is the relationship between language and the thinking of the human mind (Harris, 2003). Many people
believe that language shapes thoughts. It is for this reason that the Publication Manual of the American Psychological Association places big value on political correctness in researchers’ writings. And for this reason politicians and media use labels like
“freedom fighters” versus “terrorists,” or “surgical strikes” versus “bombing raids”
(Stapel & Semin, 2007).
Many different questions have been asked about the relationship between language and thought. We consider only some of them here. Studies comparing and
contrasting users of differing languages and dialects form the basis of this section.
Differences among Languages
Why are there so many different languages around the world? And how does using
any language in general and using a particular language influence human thought?
As you know, different languages comprise different lexicons. They also use different
syntactical structures. These differences often reflect variations in the physical and
cultural environments in which the languages arose and developed. For example, in
terms of lexicon, the Garo of Burma distinguish among many kinds of rice, which is
understandable because they are a rice-growing culture. Nomadic Arabs have more
than 20 words for camels. These peoples clearly conceptualize rice and camels more
specifically and in more complex ways than do people outside their cultural groups.
As a result of these linguistic differences, do the Garo think about rice differently
than we do? And do the Arabs think about camels differently than we do? Consider
the way we discuss computers. We differentiate between many aspects of computers,
including whether the computer is a desktop or a laptop, a PC or a Mac, or uses
Linux or Windows as an operating system. A person from a culture that does not
have access to computers would not require so many words or distinctions to describe these machines. We expect, however, specific performance and features for a
given computer based on these distinctions. Clearly, we think about computers in a
way that is different than that of people who have never encountered a computer.
The syntactical structures of languages differ, too. Almost all languages permit
some way in which to communicate actions, agents of actions, and objects of actions
(Gerrig & Banaji, 1994). What differs across languages is the order of subject, verb,
and object in a typical declarative sentence. Also differing is the range of grammatical inflections and other markings that speakers are obliged to include as key elements of a sentence. For example, in describing past actions in English, we indicate
whether an action took place in the past by changing (inflecting) the verb form. For
example, walk changes to walked in the past tense. In Spanish and German, the verb
also must indicate whether the agent of action was singular or plural and whether it
is being referred to in the first, second, or third person. In Turkish, the verb form
must additionally indicate whether the action was witnessed or experienced directly
by the speaker or was noted only indirectly. Do these differences and other differences in obligatory syntactical structures influence—or perhaps even constrain—
the users of these languages to think about things differently because of the language
they use while thinking? We will have a closer look at these questions in the next
two sections, in which we explore the concepts of linguistic relativity and linguistic
universals.
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CHAPTER 10 • Language in Context
The Sapir-Whorf Hypothesis
The concept relevant to the question of whether language influences thinking is linguistic relativity. Linguistic relativity refers to the assertion that speakers of different
languages have differing cognitive systems and that these different cognitive systems
influence the ways in which people think about the world. Thus, according to the
relativity view, the Garo would think about rice differently than we do. For example, the Garo would develop more cognitive categories for rice than would an
English-speaking counterpart. What would happen when the Garo contemplated
rice? They purportedly would view it differently—and perhaps with greater complexity of thought—than would English speakers, who have only a few words for rice.
Thus, language would shape thought. There is some evidence that word learning
may occur, in part, as a result of infants’ mental differentiations among various kinds
of concepts (Carey, 1994; Xu & Carey, 1995, 1996). So it might make sense that
infants who encounter different kinds of objects might make different kinds of mental differentiations. These differentiations would be a function of the culture in
which the infants grew up.
The linguistic-relativity hypothesis is sometimes referred to as the Sapir-Whorf
hypothesis, named after the two men who were most forceful in propagating it. Edward Sapir (1941/1964) said that “we see and hear and otherwise experience very
largely as we do because the language habits of our community predispose certain
choices of interpretation” (p. 69). Benjamin Lee Whorf (1956) stated this view
even more strongly:
We dissect nature along lines laid down by our native languages. The categories and
types that we isolate from the world of phenomena we do not find there because they
stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds—and this means
largely by the linguistic systems in our minds. (p. 213)
The Sapir-Whorf hypothesis has been one of the most widely discussed ideas in
all of the social and behavioral sciences (Lonner, 1989). However, some of its implications appear to have reached mythical proportions. For example, “many social
scientists have warmly accepted and gladly propagated the notion that Eskimos
have multitudinous words for the single English word snow. Contrary to popular beliefs, Eskimos do not have numerous words for snow (Martin, 1986). “No one who
knows anything about Eskimo (or more accurately, about the Inuit and Yup’ik families of related languages spoken from Siberia to Greenland) has ever said they do”
(Pullum, 1991, p. 160). Laura Martin, who has done more than anyone else to debunk the myth, understands why her colleagues might consider the myth charming.
But she has been quite “disappointed” in the reaction of her colleagues when she
pointed out the fallacy. Most, she says, took the position that true or not ‘it’s still a
great example’” (Adler, 1991, p. 63). Apparently, we must exercise caution in our
interpretation of findings regarding linguistic relativity.
Consider a milder form of linguistic relativism—it is that language may not determine thought, but that language certainly may influence thought. Our thoughts
and our language interact in myriad ways, only some of which we now understand.
Clearly, language facilitates thought; it even affects perception and memory. For
some reason, we have limited means by which to manipulate non-linguistic images
(Hunt & Banaji, 1988). Such limitations make desirable the use of language to facilitate mental representation and manipulation. Even nonsense pictures (“droodles”)
Language and Thought
Figure 10.1
405
Labels Affect Perception (part 1).
How does your label for this image affect your perception, your mental representation,
and your memory of the image?
Source: From Psychology, Fifth Edition, by John Darley, et al. Copyright © 1998, Pearson Education. Reprinted
by permission of John Darley.
are recalled and redrawn differently, depending on the verbal label given to the picture (Bower, Karlin, & Dueck, 1975).
To see how this phenomenon might work, look at Figure 10.1. Suppose, instead
of being labeled “beaded necklace,” it had been titled “beaded curtain.” You might
have perceived it differently. However, once a particular label has been given, viewing the same figure from the alternative perspective is much harder (Glucksberg,
1988).
Psychologists have used other ambiguous figures (see Chapters 4 and 7) and
have found similar results. Figure 10.2 illustrates three other figures that can be
given alternative labels. When participants are given a particular label, they tend
to draw their recollection of the figure in a way more similar to the given label.
For example, after viewing a figure of two circles connected by a single line, they
will draw a figure differently as a function of whether it is labeled “eyeglasses” or
“dumbbells.” Specifically, the connecting line will either be lengthened or shortened, depending on the label.
Language also affects how we encode, store, and retrieve information in memory. Remember the examples in Chapter 6 regarding the label “Washing Clothes”?
That label enhanced people’s responses to recall and comprehension questions about
text passages (Bransford & Johnson, 1972, 1973). In a similar vein, eyewitness testimony is powerfully influenced by the distinctive phrasing of questions posed to
406
CHAPTER 10 • Language in Context
Reproduced
figure
Figure 10.2
Verbal label
Original
figure
Verbal list
Bottle
Stirrup
Crescent
moon
Letter C
Eyeglasses
Dumbbells
Reproduced
figure
Labels Affect Perception (part 2).
When the original figures (in the center) are redrawn from memory, the new drawings tend
to be distorted to be more like the labeled figures.
Source: From Psychology, Fifth Edition, by John Darley, et al. Copyright © 1998, Pearson Education. Reprinted
by permission of John Darley.
eyewitnesses (Loftus & Palmer, 1974; see also Chapter 6 for more information on
eyewitness testimony). In a famous study, participants viewed an accident (Loftus
& Palmer, 1974). Participants then were asked to describe the speeds of the cars before the accident. The word indicating impact was varied across participants. These
words included smashed, collided, bumped, and hit. When the word smashed was used,
the participants rated speed as significantly higher than when any of the other words
were used. The connotation of the word smash thereby seems to bias participants to
estimate a higher speed. Similarly, when participants were asked if they saw broken
glass (after a week’s delay), the participants who were questioned with the word
smashed said “yes” much more frequently than did any of the other participants (Loftus & Palmer, 1974). No other circumstances varied between participants, so the
difference in the description of the accident is presumably the result of the word
choice.
Even when participants generated their own descriptions, the subsequent accuracy of their eyewitness testimony declined (Schooler & Engstler-Schooler, 1990).
Accurate recall actually declined following an opportunity to write a description of
an observed event, a particular color, or a particular face. When given an opportunity to identify statements about an event—the actual color or a face—participants
were less able to do so accurately if they previously had described it. Paradoxically,
when participants were allowed to take their time in responding, their performance
was even less accurate than when they were forced to respond quickly. In other
words, given time to reflect on their answers, participants were more likely to respond in accord with what they had said or written than with what they had seen.
Is the Sapir-Whorf hypothesis relevant to everyday life? It almost certainly is. If
language constrains our thought, then we may fail to see solutions to problems because we do not have the right words to express these solutions. Consider the
Language and Thought
407
misunderstandings we have with people who speak other languages. For example,
one of the authors once was in Japan talking to a Japanese college student, who referred to the author as an “Aryan.” The author explained that this concept has no
basis in reality. It turned out that she meant to say “Alien,” but in Japanese, there is
not distinction between the “l” and “r” sounds. Even then, referring to him as an
“alien” was not particularly comforting to him. According to the Sapir-Whorf
view, the misunderstandings may result from the fact that other languages parse
words differently than ours does, and may use different phonemes as well. One
must be grateful that extreme versions of the Sapir-Whorf hypothesis do not appear
to be justified. Such versions would suggest that we are, figuratively, slaves to the
words available to us.
Linguistic Relativity or Linguistic Universals?
There has been some research that addresses linguistic universals—characteristic
patterns across all languages of various cultures—and relativity. Recall from Chapter
9 that linguists have identified hundreds of linguistic universals related to phonology
(the study of phonemes), morphology (the study of morphemes), semantics, and syntax. For example, Chomsky would argue that deep structure applies, in its own way,
to the syntaxes of all languages.
Colors An area that illustrates much of this research focuses on color names. These
words provide an especially convenient way of testing for universals. Why? Because
people in every culture can be expected to be exposed, at least potentially, to pretty
much the same range of colors.
In actuality, different languages name colors quite differently. But the languages
do not divide the color spectrum arbitrarily. A systematic pattern seems universally
to govern color naming across languages. Consider the results of investigations of
color terms across a large number of languages (Berlin & Kay, 1969; Kay, 1975).
Two apparent linguistic universals about color naming have emerged across languages. First, all the languages surveyed took their basic color terms from a set of
just 11 color names. These are black, white, red, yellow, green, blue, brown, purple,
pink, orange, and gray. Languages ranged from using all 11 color names, as in English, to using just two of the names, as in the Dani tribe of Western New Guinea
(Rosch Heider, 1972). Second, when only some of the color names are used, the
naming of colors falls into a hierarchy of five levels. The levels are (1) black, white;
(2) red; (3) yellow, green, blue; (4) brown; and (5) purple, pink, orange, gray. Thus,
if a language names only two colors, they will be black and white. If it names three
colors, they will be black, white, and red. A fourth color will be taken from the set
of yellow, green, and blue. The fifth and sixth will be taken from this set as well.
Selection will continue until all 11 colors have been labeled. The order of selection
within the categories may, however, vary between cultures (Jameson, 2005).
Another study had participants name various colors that were shown to them on
color plates. Participants also were asked to choose the best example for each color
(e.g., out of the many color plates presented, which is the best “red”?). This procedure was done for many languages, and the results showed that the “best” colors
tended to cluster around the colors that English speakers call red, yellow, green,
and blue (Regier et al., 2005). This result indicates that there are some universals
in color perception.
In contrast, several studies have shown that color categories vary, depending on
the speaker’s language. For example, Berinmo speakers from New Guinea tend to
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CHAPTER 10 • Language in Context
n BELIEVE IT OR NOT
DO YOU SEE COLORS TO YOUR LEFT DIFFERENTLY
COLORS TO YOUR RIGHT?
THAN
The language center of the brain is located mostly in the
left hemisphere. At the same time, light from objects on our
right falls onto the left side of our retina and is then transmitted to the left hemisphere of the brain (and vice versa;
for a graphical illustration of this, refer to Figure 2.8 in
Chapter 2). Could this circumstance influence our perception of colors? Participants were shown a circle consisting
of colored green squares. One of those squares was of a
different color—either blue or a different shade of green—
and it was located either in the lower right or lower left of
the circle. The time it took people to pick the square with
the different color was measured. If the square was located on the left (and the light therefore was transmitted
to the right hemisphere), it did not make a difference
whether its color was blue or a different shade of green.
If the square was on the right, the blue square was detected faster than the green square. This is because the
language center in the left hemisphere interacted with
color recognition. If participants’ language centers were
kept busy with a memory task, the effect disappeared,
making it indeed likely that the effect was a result of
language (Gilbert et al., 2006).
aggregate colors together in one name (nol) that we call green and blue (Roberson et
al., 2000, 2005). Other languages tend to see categorical differences where English
speakers do not see any. For example, Russian speakers discriminate between light
blue (goluboy) and dark blue (siniy) (Winawer et al., 2007). Various theories have
been proposed of why color names differ in different cultures. It has been proposed,
for example, that the sun’s ultraviolet rays causes people’s lenses to yellow, which
makes it harder to discriminate between green and blue. The large sun exposure,
then, in areas near the equator could be the reason for the relative scarcity of separate color terms for blue and green in some languages in this area (Lindsey & Brown,
2002). It also could be that color names are an evolutionary result of the most frequently occurring colors in the environment of members of a particular language
group (Yendrikhovskij, 2001). But so far, none of the theories are consistent with
each other.
So overall, while it seems that color naming is relatively universal in that it clusters worldwide around the same areas, color categories vary considerably and color
names can have an impact on perception and cognition (Kay & Regier, 2006;
Roberson & Hanley, 2007).
So, can we say that color perception is universal, or are there significant differences between cultures and languages? In the next section, we examine an interesting study that explored this question.
Verbs and Grammatical Gender Syntactical as well as semantic structural differences across languages may affect thought. For example, Spanish has two forms of
the verb “to be”—ser and estar. However, they are used in different contexts. One
investigator studied the uses of ser and estar in adults and in children (Sera, 1992).
When “to be” indicated the identity of something (e.g., in English, “This is
José.”) or the class membership of something (e.g., “José is a carpenter.”), both adults
and children used the verb form ser. Moreover, both adults and children used different verb forms when “to be” indicated attributes of things. Ser was used to indicate
permanent attributes (e.g., “Maria is tall.”). Estar was also used to indicate temporary
attributes (e.g., “Maria is busy.”). Finally, when using forms of “to be” to describe the
locations of objects, including people, animals, and other things, both adults and
children used estar (e.g., “Marie is on the chair.”). However, when using forms of
Language and Thought
409
“to be” to describe the locations of events (e.g., meetings or parties), adults used ser,
whereas children continued to use estar.
Sera (1992) interprets these findings as indicating two things. First, ser seems to
be used primarily for indicating permanent conditions, such as identity; class inclusion; and relatively permanent, stable attributes of things. Estar seems to be used primarily for indicating temporary conditions, such as short-term attributes of things
and the location of objects. These things often are subject to change from one place
to another. Moreover, children treat the location of events in the same way as the
location of objects. They view it as temporary and hence use estar. Adults, in contrast, differentiate between events and objects. In particular, adults consider the
locations of events to be unchanging. Because they are permanent, they require
the use of ser.
Other researchers have also suggested that young children have difficulty distinguishing between objects and events (e.g., Keil, 1979). Young children also find it
difficult to recognize the permanent status of many attributes (Marcus & Overton,
1978). Thus, the developmental differences regarding the use of ser to describe the
location of events may indicate developmental differences in cognition. Sera’s work
suggests that differences in language use may indeed indicate differences in thinking.
However, her work leaves open an important psychological question. Do native
Spanish speakers have a more differentiated sense of the temporary and the permanent than do native English speakers, who use the same verb form to express both
senses of “to be”? The answer is unclear.
Other languages also have been used in investigations of linguistic relativity.
Some studies explore the relevance of different languages using different prepositions. In English, people use the prepositions “in” and “on” to describe putting a
pear in a bowl or putting a cup on the table. “In” refers to containment of some
sort, whereas “on” refers to support. Korean speakers differentiate between “tight
fit” (kkita, like a DVD in its sleeve) and “loose fit” (nehta, like a pear in a bowl)
in their prepositions. In one experiment, participants were shown several spatial
actions and had to pick the one that seemed “odd” and not to fit the other actions.
The spatial actions were performed with objects of different texture and material
(e.g., wooden or made of sponge) and showed the objects either being put in a
tight-fitting setting or a loose container. In all, 80% of the Korean speakers picked
the odd scene on the basis of whether or not it involved tight/loose fit. In comparison, only 37% of English speakers did. The majority of English speakers picked
out a scene where the material or shape of the object differed (McDonough
et al., 2003).
Another experiment tested the effect of grammatical gender. The study was conducted in English, but participants were native German and Spanish speakers. They
were presented with 24 noun words that they had to describe in three adjectives
each. In all, 12 of the nouns were feminine in German and masculine in Spanish,
and the other 12 nouns were masculine in German and feminine in Spanish. There
were marked differences in how the objects were described, depending on their gender. For example, the word “key,” which is feminine in Spanish (la llave), was
described by the Spanish speakers as “golden, intricate, little, lovely.” In German,
the word “key” is masculine (der Schluessel) and was described as “hard, heavy, jagged,
metal.” The effect is especially impressive because the experiment was conducted in
English and did not involve the participants speaking German or Spanish (Boroditsky
et al., 2003).
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CHAPTER 10 • Language in Context
Also consider some more facts:
• Children who learn Mandarin Chinese tend to use more verbs than nouns. In
contrast, children acquiring English or Italian tend to use more nouns than
verbs (Tardif, 1996; Tardif, Shatz, & Naigles, 1997).
• Korean-speaking children use verbs earlier than do English-speaking children.
In contrast, English-speaking children have larger naming vocabularies earlier
than do Korean-speaking children (Gopnik & Choi, 1995; Gopnik, Choi, &
Baumberger, 1996).
What differences in thinking might such differences in acquisition imply? No
one knows for sure.
Concepts An intriguing experiment assessed the possible effects of linguistic relativity by studying people who speak more than one language (Hoffman, Lau, &
Johnson, 1986). In Chinese, a single term, shì gÈ, specifically describes a person
who is “worldly, experienced, socially skillful, devoted to his or her family, and
somewhat reserved” (p. 1098). English clearly has no comparable single term to embrace these diverse characteristics. Hoffman and his colleagues composed text passages in English and in Chinese describing various characters. They included the
shì gÈ stereotype, without, of course, specifically using the term shì gÈ in the descriptions. The researchers then asked participants who were fluent in both Chinese and
English to read the passages either in Chinese or in English. Then they rated various
statements about the characters, in terms of the likelihood that the statements would
be true of the characters. Some of these statements involved a stereotype of a shì gÈ
person.
Their results seemed to support the notion of linguistic relativity. The participants were more likely to rate the various statements in accord with the shì gÈ
stereotype when they had read the passages in Chinese than when they had read
the passages in English. Similarly, when participants were asked to write their own
impressions of the characters, their descriptions conformed more closely to the shì gÈ
stereotype if they previously read the passages in Chinese. These authors do not suggest that it would be impossible for English speakers to comprehend the shì gÈ stereotype. Rather, they suggest that having that stereotype readily accessible facilitates
its mental manipulation.
Research on linguistic relativity is a good example of the dialectic in action.
Before Sapir and Whorf, the issue of how language constrains thought was not salient in the minds of psychologists. Sapir and Whorf then presented a thesis that
language largely controls thought. After they presented their thesis, a number of
psychologists tried to show the antithesis. They argued that language does not control thought. Today, many psychologists believe in a synthesis: Language has some
influence on thought but not nearly so extreme an influence as Sapir and Whorf
believed.
The question of whether linguistic relativity exists, and if so, to what extent,
remains open. There may be a mild form of relativity in the sense that language
can influence thought. However, a stronger deterministic form of relativity is less
likely. Based on the available evidence, language does not seem to determine differences in thought among members of various cultures. Finally, it is probably the
case that language and thought interact with each other throughout the life span
(Vygotsky, 1986).
Language and Thought
411
IN THE LAB OF KEITH RAYNER
Eye Movements and Reading
target word when the saccade was
launched and the relationship between
Reading is a remarkable achievement of
the preview and the target.
the human brain/mind. How do we unA final type of gaze-contingent techderstand written language on a momentnique that we developed is the disappearto-moment basis? This is the primary quesing-text paradigm. Here, on each fixation,
tion that has driven my research for many
the word the reader is looking at disappears
years. We typically use eye movement
(or is masked) early in a fixation. One remeasures as a reflection of momentmarkable finding is that readers can read
to-moment processing. A considerable
normally if they get to see the fixated word
KEITH RAYNER
amount of research from my lab (and
for 50–60 milliseconds (this doesn’t mean
others) clearly documents that how long
that word recognition is completed in this
readers look at words in text is strongly influenced by
time, just that the information has been entered into the
cognitive processes and the ease or difficulty associprocessing system). Second, how long the eyes remain in
ated with processing a word. For example, readers
place is strongly influenced by the frequency of the fixated
look longer at low-frequency words (like “vituperative”)
word: If it is a low-frequency word, the eyes remain on it
than high-frequency words (like “house”).
longer than if it is a high-frequency word. This is very
There are a number of critical issues that needed
good evidence that cognitive processing drives eye
attention before one could safely assume that eye
movements during reading.
movements reflect moment-to-moment processing. In
Given these findings, eye movements can be
reading, our eyes pause on average for about
used to study moment-to-moment processing. In my
200–250 milliseconds. How much useful information
lab, we have taken advantage of the various types
do readers obtain on each fixation? To answer this
of ambiguity that exist in written English to strive to
question, George McConkie and I developed a gazeunderstand readers’ moment-to-moment comprehencontingent moving window paradigm in which we consion processes. Thus, we have studied how readers
trolled how much information readers had available on
parse sentences that contain temporary syntactic ambieach fixation. We found that the span of perception in
guities, as well as how they deal with lexically ambigreading extends about 3–4 letter spaces to the left of
uous words (words with two meanings, like bank and
fixation to about 14–15 letters spaces to the right of
straw) and phonologically ambiguous words (that are
fixation for readers of English.
spelled the same, but have two different pronunciaIn subsequent work, I developed a gaze-contingent
tions). We have also used eye movement data to
boundary paradigm to determine what kind of informastudy higher-level discourse processing, though the
tion readers obtain from the word to the right of fixation.
link between such processes and how long readers
This work documented that readers obtain a preview
look at parts of the text is much more tenuous than is
benefit from having valid information to the right of fixathe case with lexical processes. Finally, given that we
tion. In these types of experiments (which are quite pophave learned so much about the relationship between
ular these days), the type of information that is available
eye movements and reading, we (Erik Reichle, Sandy
in a target word location is manipulated (so for examPollatsek, Don Fisher, and myself) developed a model
ple, the preview might be the word chest), but during
of eye movement control in reading (called the E-Z
the eye movement to the word, the preview changes to
Reader model) that does a good job of predicting
the target word (chart). The amount of preview benefit
where readers fixate and how long they fixate on
depends on how far away the eyes were from the
words.
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CHAPTER 10 • Language in Context
Bilingualism and Dialects
Suppose a person can speak and think in two languages. Does the person think differently in each language? Do bilinguals—people who can speak two languages—
think differently from monolinguals—people who can speak only one language?
(Multilinguals speak at least two and possibly more languages.) What differences, if
any, emanate from the availability of two languages versus just one? Might bilingualism affect intelligence, positively or negatively?
Bilingualism—An Advantage or Disadvantage?
Does bilingualism make thinking in any one language more difficult, or does it enhance thought processes? The data are somewhat contradictory. Different participant
populations, different methodologies, different language groups, and different experimenter biases may have contributed to the inconsistency in the literature. Consider
what happens when bilinguals are balanced bilinguals, who are roughly equally fluent in both languages, and when they come from middle-class backgrounds. In these
instances, positive effects of bilingualism tend to be found. Executive functions,
which are located primarily in the prefrontal cortex and include abilities such as to
shift between tasks or ignore distracters, are enhanced in bilingual individuals. Even
the onset of dementia in bilinguals may be delayed by as much as four years (Andreou & Karapetsas, 2004; Bialystok & Craik, 2010; Bialystok et al., 2007). But negative effects may result as well. Bilingual speakers tend to have smaller vocabularies
and their access to lexical items in memory is slower (Bialystok, 2001b; Bialystok &
Craik, 2010). What might be the causes of this difference?
Let us distinguish between what might be called additive versus subtractive bilingualism (Cummins, 1976). In additive bilingualism, a second language is acquired in addition to a relatively well-developed first language. In subtractive bilingualism, elements of a
second language replace elements of the first language. It appears that the additive form
results in increased thinking ability. In contrast, the subtractive form results in decreased
thinking ability (Cummins, 1976). In particular, there may be something of a threshold
effect. Individuals may need to be at a certain relatively high level of competence in
both languages for a positive effect of bilingualism. Classroom teachers often discourage
bilingualism in children (Sook Lee & Oxelson, 2006). Either through letters requesting
only English be spoken at home, or through subtle attitudes and methods, many teachers actually encourage subtractive bilingualism (Sook Lee & Oxelson, 2006). Additionally, children from backgrounds with lower socioeconomic status (SES) may be
more likely to be subtractive bilinguals than are children from the middle SES. Their
SES may be a factor in their being hurt rather than helped by their bilingualism.
Researchers also distinguish between simultaneous bilingualism, which occurs
when a child learns two languages from birth, and sequential bilingualism, which occurs when an individual first learns one language and then another (Bhatia &
Ritchie, 1999). Either form of language learning can contribute to fluency. It depends on the particular circumstances in which the languages are learned (Pearson
et al., 1997). It is known, however, that infants begin babbling at roughly the same
age. This happens regardless of whether they consistently are exposed to one or two
languages (Oller et al., 1997). In the United States, many people make a big deal of
bilingualism, perhaps because relatively few Americans born in the United States of
non-immigrant parents learn a second language to a high degree of fluency. In other
cultures, however, the learning of multiple languages is taken for granted. For example, in parts of India, people routinely may learn as many as four languages
Language and Thought
413
(Khubchandani, 1997). In Flemish-speaking Belgium, many people learn at least
some French, English, and/or German. Often, they learn one or more of these other
languages to a high degree of fluency.
Factors That Influence Second Language Acquisition
A significant factor believed to contribute to acquisition of a language is age. Some
researchers have suggested that native-like mastery of some aspects of a second language is rarely acquired after adolescence. Other researchers disagree with this view
(Bahrick et al., 1994; Herschensohn, 2007). They found that some aspects of a second
language, such as vocabulary comprehension and fluency, seem to be acquired just as
well after adolescence as before. Furthermore, these researchers found that even some
aspects of syntax seem to be acquired readily after adolescence. These results are contrary to prior findings. The mastery of native-like pronunciation often seems to depend
on early acquisition. But individual differences are great and some learners attain
native-like language abilities even at a later age (Birdsong, 2009). It may seem surprising that learning completely novel phonemes in a second language may be easier than
learning phonemes that are highly similar to the phonemes of the first language (Flege,
1991). In any case, there do not appear to be critical periods for second-language acquisition (Birdsong, 1999, 2009). Adults may appear to have a harder time learning second languages because they can retain their native language as their dominant
language. Young children, in contrast, who typically need to attend school in the new
language, may have to switch their dominant language. So, they learn the new language to a higher level of mastery (Jia & Aaronson, 1999). A study on second language
acquisition found that age and proficiency in a language are negatively correlated (Mechelli et al., 2004). This finding has been well documented (Birdsong, 2006). This does
not mean that we cannot learn a new Flanguage later in life, but rather, that the earlier
we learn it, the more likely we will become highly proficient in its use.
What kinds of learning experiences facilitate second-language acquisition?
There is no single correct answer to that question (Bialystock & Hakuta, 1994).
One reason is that each individual language learner brings distinctive cognitive abilities and knowledge to the language-learning experience. In addition, the kinds of
learning experiences that facilitate second-language acquisition should match the
context and uses for the second language once it is acquired.
For example, consider these individuals:
• Caitlin, a young child, may not need to master a wealth of vocabulary and complex syntax to get along well with other children. If she can master the phonology, some simple syntactical rules, and some basic vocabulary, she may be
considered fluent.
• Similarly, José needs only to get by in a few everyday situations, such as shopping, handling routine family business transactions, and getting around town.
He may be considered proficient after mastering some simple vocabulary and
syntax, as well as some pragmatic knowledge regarding context-appropriate manners of communicating.
• Kim Yee must be able to communicate regarding her specialized technical field.
She may be considered proficient if she masters the technical vocabulary, a
primitive basic vocabulary, and the rudiments of syntax.
• Sumesh is a student who studies a second language in an academic setting. Sumesh may be expected to have a firm grasp of syntax and a rather broad, if shallow, vocabulary.
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CHAPTER 10 • Language in Context
Each of these language learners may require different kinds of language experiences to gain the proficiency being sought. Different kinds of experiences may be
needed to enhance their competence in the phonology, vocabulary, syntax, and
pragmatics of the second language.
When speakers of one language learn other languages, they find the languages
differentially difficult. For example, it is much easier, on average, for a native speaker
of English to acquire Spanish as a second language than it is to acquire Russian. One
reason is that English and Spanish share more roots than do English and Russian.
Moreover, Russian is much more highly inflected than are English and Spanish. English and Spanish are more highly dependent on word order. The difficulty of learning a language as a second language, however, does not appear to have much to do
with its difficulty as a first language. Russian infants probably learn Russian about as
easily as U.S. infants learn English (Maratsos, 1998).
Bilingualism: One System or Two?
One way of approaching the study of bilingualism is to apply what we have learned
from cognitive-psychological research to practical concerns regarding how to help
with acquisition of a second language. Another approach is to study bilingual individuals to see how bilingualism may offer insight into the human mind. Some cognitive psychologists have been interested in finding out how the two languages are
represented in the bilingual’s mind. The single-system hypothesis suggests that two
languages are represented in just one system or brain region (see Hernandez et al.,
2001, for evidence supporting this hypothesis in early bilinguals). Alternatively, the
dual-system hypothesis suggests that two languages are represented somehow in separate systems of the mind (De Houwer, 1995; Paradis, 1981). For instance, might
German language information be stored in a physically different part of the brain
than English language information? Figure 10.3 shows schematically the difference
in the two points of view.
One way to address this question is through the study of bilinguals who have
experienced brain damage. Suppose a bilingual person has brain damage in a particular part of the brain. According to the dual-system hypothesis, the individual would
show different degrees of impairment in the two languages. The single-system view
would suggest roughly equal impairment in the two languages. The logic of this kind
of investigation is compelling, but the results are not. When recovery of language
after trauma is studied, sometimes the first language recovers first; sometimes the second language recovers first. And sometimes recovery is about equal for the two languages (Albert & Obler, 1978; Marrero et al., 2002; Paradis, 1977). Recovery of one
or both languages seems contingent on age of acquisition of the second language and
on pre-incident language proficiency, among other factors (Marrero, Golden, & Espe
Pfeifer, 2002).
A 32-year-old French-German bilingual who suffered from a stroke and subsequent aphasia was trained in German but was given no training in French. The
researchers found significant recovery of German, but his German language abilities
did not transfer to his French abilities (Meinzer et al., 2007).
The conclusions that can be drawn from all this research are ambiguous. Nevertheless, the results seem to suggest at least some duality of structure. A different
method of study has led to an alternative perspective on bilingualism. Two investigators mapped the region of the cerebral cortex relevant to language use in two of
their bilingual patients being treated for epilepsy (Ojemann & Whitaker, 1978).
Language and Thought
415
Single system
English
German
Table
Bread
Tisch
Brot
Butter
Butter
Dual system
English
German
Table
Bread
Tisch
Brot
Butter
Butter
Figure 10.3
Single-System and Dual-System Hypotheses.
The single-system conceptualization hypothesizes that both languages are represented in a
unified cognitive system. The dual-system conceptualization of bilingualism hypothesizes that
each language is represented in a separate cognitive system.
Mild electrical stimulation was applied to the cortex of each patient. Electrical stimulation tends to inhibit activity where it is applied. It leads to a reduced ability to
name objects for which the memories are stored at the location being stimulated.
The results for both patients were the same. They may help explain the contradictions in the literature. Some areas of the brain showed equal impairments for object
naming in both languages. But other areas of the brain showed differential impairment in one or the other language. The results also suggested that the weaker
language was more diffusely represented across the cortex than was the stronger language. In other words, asking the question of whether two languages are represented
singly or separately may be asking the wrong question. The results of this study suggest that some aspects of the two languages may be represented singly; other aspects
may be represented separately.
To summarize, two languages seem to share some, but not all, aspects of mental
representation. Learning a second language is often a plus, but it is probably most
useful if the individual learning the second language is in an environment in which
the learning of the second language adds to rather than subtracts from the learning
of the first language. For beneficial effects to appear, the second language must be
learned well. In the approach usually taken in schools, students may receive as little
as two or three years of second-language instruction spread out over a few class periods a week. This approach probably will not be sufficient for the beneficial effects of
bilingualism to appear. However, schooling does seem to yield beneficial effects on
acquisition of syntax. This is particularly so when a second language is acquired after
adolescence. Furthermore, whenever possible, individual learners should choose specific kinds of language-acquisition techniques that best fit their needs, abilities, preferences, and personal goals for using the second language.
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CHAPTER 10 • Language in Context
Language Mixtures and Change
Bilingualism is not a certain outcome of linguistic contact between different language groups. Here are some scenarios of what can happen when different language
groups come into contact with each other:
• Sometimes when people of two different language groups are in prolonged contact with one another, the language users of the two groups begin to share some
vocabulary that is superimposed onto each group’s language use. This superimposition results in what is known as a pidgin. It is a language that has no native
speakers (Wang, 2009).
• Over time, this admixture can develop into a distinct linguistic form. It has its
own grammar and hence becomes a creole. An example of a creole is the Haitian
Creole language, spoken in Haiti. The Haitian Creole language is a combination
of French and a number of West African languages.
• Modern creoles may resemble an evolutionarily early form of language, termed
protolanguage (Bickerton, 1990).
The existence of pidgins and creoles, and possibly a protolanguage, supports the
universality notion discussed earlier. That is, linguistic ability is so natural and universal that, given the opportunity, humans actually invent new languages quite rapidly.
Creoles and pidgins arise when two linguistically distinctive groups meet. The
counterpart—a dialect—occurs when a single linguistic group gradually diverges toward somewhat distinctive variations. A dialect is a regional variety of a language
distinguished by features such as vocabulary, syntax, and pronunciation. The study
of dialects provides insights into such diverse phenomena as auditory discrimination
and social discrimination. Many of the words we choose are a result of the dialect we
use. The most well-known example is the word choice for a soft drink. Depending
on the dialect you use, you may order a “soda,” “pop,” or a “Coke” (see Figure 10.4).
Pop vs. Soda data as of October 3, 2002
“Pop”
“Soda”
“Coke”
Other
Figure 10.4 The Pop vs. Soda Controversy.
This map shows the distribution of different words used for “soft drink” across the United States. What word people use
depends on the dialect they speak.
Source: http://popvssoda.com:2998/
Language and Thought
417
Dialectical differences often represent harmless regional variations. They create few serious communication difficulties, but these difficulties can lead to some
confusion. In the United States, for example, when national advertisers give tollfree numbers to call, they sometimes route the calls to the Midwest. They do so
because they have learned that the Midwestern form of speech seems to be the
most universally understood form within the country. Other forms, such as southern and northeastern ones, may be harder for people from diverse parts of the
country to understand. And when calls are routed to other countries, such as India, there may be serious difficulties in achieving effective communication because
of differences in dialect as well as accent. Many radio announcers try to learn
something close to a standard form of English, often called “network English.”
In this way, they can maximize their comprehensibility to as many listeners as
possible.
Sometimes, differing dialects are assigned different social statuses, such as standard forms having higher status than non-standard ones. The distinction between
standard and non-standard forms of a language can become unfortunate when speakers of one dialect start to view themselves as speakers of a superior dialect. The view
that one dialect is superior to another may lead one to make judgments about the
speaker that are biased. This linguicism, or stereotype based on dialect, may be quite
widespread and can cause many interpersonal problems (Phillipson, 2010; Zuidema,
2005). For example, we frequently make judgments about people’s intelligence, competence, and morality based on the dialect they use. Specifically, a person who uses a
non-standard form may be judged to be less educated or less trustworthy than a person who uses a more standard form. Usually, the standard dialect is that of the class
in society that has the most political or economic power. Virtually any thought can
be expressed in any dialect.
Neuroscience and Bilingualism
Learning a second language increases the gray matter in the left inferior parietal
cortex (Mechelli et al., 2004). This density is positively correlated with proficiency.
Thus, the more proficient a person is in a second language, the denser this area of
the brain will be. Finally, a negative correlation exists between age of acquisition
and the density in the left inferior parietal cortex (Mechelli et al., 2004)—the
higher the age of acquisition, the less the density. These findings suggest that this
area of the brain benefits from the learning of a second language and that the earlier this learning occurs, the better it is both for brain density and for overall
proficiency.
Studies with aphasic patients suggest that first and second languages may be distributed in different anatomic regions of the brain. This assumption comes from the
observation of a bilingual patient who suffered a stroke and subsequently had impaired language skills in his native language. His second language, however, was unaffected (Garcia et al., 2010). Other studies, however, suggest, that the brain regions
activated by two languages may actually overlap (Gandour et al., 2007; Yokohama et
al., 2006). Whether or not the same brain areas are involved likely depends on other
factors, like the age of acquisition of the second language.
One study had bilingual persons complete a sentence-generation task (i.e., participants were asked to create sentences). The study showed that the centers of activation in the left inferior frontal gyrus are overlapping for early bilinguals. Late
bilinguals, however, show separate centers of activation (Kim et al., 1997).
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CHAPTER 10 • Language in Context
Slips of the Tongue
An area of particular interest to cognitive psychologists is how people use language
incorrectly. Studying speech errors helps cognitive psychologists better understand
normal language processing. One way of using language incorrectly is through slips
of the tongue—inadvertent linguistic errors in what we say. They may occur at any
level of linguistic analysis: phonemes, morphemes, or larger units of language (Crystal, 1987; McArthur, 1992). In such cases, what we think and what we mean to say
do not correspond to what we actually do say. Freudian psychoanalysts have suggested that in Freudian slips, the verbal slips reflect some kind of unconscious processing that has psychological significance. The slips are alleged often to indicate
repressed emotions. For example, a business competitor may say, “I’m glad to beat
you,” when what was overtly intended was, “I’m glad to meet you.”
Most cognitive psychologists see things differently from the psychoanalytic view.
They are intrigued by slips of the tongue because of what the lack of correspondence
between what is thought and what is said may tell us about how language is produced. In speaking, we have a mental plan for what we are going to say. Sometimes,
however, this plan is disrupted when our mechanism for speech production does not
cooperate with our cognitive one. Often, such errors result from intrusions by other
thoughts or by stimuli in the environment, such as a background noise from radio
talk show or a neighboring conversation (Garrett, 1980; Saito & Baddeley, 2004).
Slips of the tongue may be taken to indicate that the language of thought differs
somewhat from the language through which we express our thoughts (Fodor, 1975).
Often we have the idea right, but its expression comes out wrong. Sometimes we are
not even aware of the slip until it is pointed out to us. In the language of the mind,
whatever it may be, the idea is right, although the expression represented by the slip
is inadvertently wrong. This fact can be seen in the occasional slips of the tongue
even in preplanned and practiced speech (Kawachi, 2002).
People tend to make various kinds of slips in their conversations (Fromkin,
1973; Fromkin & Rodman, 1988):
• In anticipation, the speaker uses a language element before it is appropriate in the
sentence because it corresponds to an element that will be needed later in the
utterance. For example, instead of saying, “an inspiring expression,” a speaker
might say, “an expiring expression.”
• In perseveration, the speaker uses a language element that was appropriate earlier
in the sentence but that is not appropriate later on. For example, a speaker
might say, “We sat down to a bounteous beast” instead of a “bounteous feast.”
• In substitution, the speaker substitutes one language element for another. For example, you may have warned someone to do something “after it is too late,”
when you meant “before it is too late.”
• In reversal (also called “transposition”), the speaker switches the positions of two
language elements. An example is the reversal that reportedly led “flutterby” to
become “butterfly.” This reversal captivated language users so much that it is
now the preferred form. Sometimes, reversals can be fortuitously opportune.
• In spoonerisms, the initial sounds of two words are reversed and make two entirely different words. The term is named after the Reverend William Spooner,
who was famous for them. Some of his choicest slips include, “You have hissed
all my mystery lectures,” [missed all my history lectures] and “Easier for a camel
to go through the knee of an idol” [the eye of a needle] (Clark & Clark, 1977).
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419
• In malapropism, one word is replaced by another that is similar in sound but different in meaning (e.g., furniture dealers selling “naughty pine” instead of
“knotty pine”).
• Additionally, slips may occur because of insertions of sounds (e.g., “mischievious”
instead of “mischievous” or “drownded” instead of “drowned”) or other linguistic
elements. The opposite kind of slip involves deletions (e.g., sound deletions
such as “prossing” instead of “processing”). Such deletions often involve blends
(e.g., “blounds” for “blended sounds”).
Each kind of slip of the tongue may occur at different hierarchical levels of linguistic processing (Dell, 1986). That is, it may occur at the acoustical level of phonemes, as in “bounteous beast” instead of “bounteous feast.” It may occur at the
semantic level of morphemes, as in “after it’s too late” instead of “before it’s too
late.” Or it may occur at even higher levels, as in “bought the bucket” instead of
“kicked the bucket” or “bought the farm.” The patterns of errors (e.g., reversals, substitutions) at each hierarchical level tend to be parallel (Dell, 1986). For example, in
phonemic errors, initial consonants tend to interact with initial consonants, as in
“tasting wime” instead of “wasting time.” Final consonants tend to interact with final
consonants, as in “bing his tut” instead of “bit his tongue.” Prefixes often interact
with prefixes, as in “expiring expression,” and so on.
Also, errors at each level of linguistic analysis suggest particular kinds of insights
into how we produce speech. Consider, for example, phonemic errors. A stressed
word, which is emphasized through speech rhythm and tone, is more likely to influence other words than is an unstressed word (Crystal, 1987). Furthermore, even
when sounds are switched, the basic rhythmic and tonal patterns usually are preserved. An example is the emphasis on “hissed” and the first syllable of “mystery”
in the first spoonerism quoted here.
Even at the level of words, the same parts of speech tend to be involved in the
errors we produce (e.g., nouns interfere with other nouns, and verbs with verbs;
Bock, 1990; Bock, Loebell, & Morey, 1992). In the second spoonerism quoted
here, Spooner managed to preserve the syntactical categories, the nouns knee and
idol. He also preserved the grammaticality of the sentence by changing the articles
from “a needle” to “an idol.” Even in the case of word substitutions, syntactic categories are preserved. In speech errors, semantic categories, too, may be preserved. An
example would be naming a category when intending to name a member of the category, such as “fruit” for “apple.” Another example would be naming the wrong
member of the category, such as “peach” for “apple.” A last example would be naming a member of a category when intending to name the category as a whole, as in
“peach” for “fruit” (Garrett, 1992).
People who are fluent in sign language and mouth at the same time they sign
have slips of the tongue (or hand) occurring independently of each other, indicating
that oral words and sign words are not stored together in that person’s lexicon (Vinson et al., 2010).
Another aspect of language that offers us a distinctive view is the study of metaphorical language.
Metaphorical Language
Until now, we have discussed primarily the literal uses of language. At least as interesting to poets and to many others is the non-literal, figurative use of language.
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A notable example is the use of metaphors as a way of expressing thoughts. Metaphors juxtapose two nouns in a way that positively asserts their similarities, while
not disconfirming their dissimilarities (e.g., The house was a pigsty). Related to metaphors are similes. Similes introduce the words like or as into a comparison between
items (e.g., The child was as quiet as a mouse).
Metaphors contain four key elements: Two are the items being compared, a
tenor and a vehicle. And two are ways in which the items are related. The tenor is
the topic of the metaphor (e.g., house). The vehicle is what the tenor is described in
terms of (e.g., pigsty). For example, consider the metaphor, “Billboards are warts on
the landscape.” The tenor is “billboards.” The vehicle is “warts.” The ground of the
metaphor is the set of similarities between the tenor and the vehicle (e.g., both are
messy). The tension of the metaphor is the set of dissimilarities between the two
(e.g., people do not live in pigsties but do live in houses). We may conjecture that
a key similarity (ground) between billboards and warts is that they are both considered unattractive. The dissimilarities (tension) between the two are many, including
that billboards appear on buildings, highways, and other impersonal public locations.
But warts appear on diverse personal locations on an individual.
Various theories have been proposed to explain how metaphors work. The traditional views have highlighted either the ways in which the tenor and the vehicle
are similar or the ways in which they differ.
• The traditional comparison view highlights the importance of the comparison. It
underscores the comparative similarities and analogical relationship between the
tenor and the vehicle (Malgady & Johnson, 1976; Miller, 1979; cf. also Sternberg & Nigro, 1983). As applied to the metaphor, “Abused children are walking
time bombs,” the comparison view underscores the similarity between the elements: their potential for explosion.
• In contrast, the anomaly view of metaphor emphasizes the dissimilarity between
the tenor and the vehicle (Beardsley, 1962; Gerrig & Healy, 1983; Searle,
1979). The anomaly view would highlight the dissimilarities between abused
children and time bombs.
• The domain-interaction view integrates aspects of each of the preceding views. It
suggests that a metaphor is more than a comparison and more than an anomaly.
According to this view, a metaphor involves an interaction of some kind between the domain (area of knowledge, such as animals, machines, plants) of
the tenor and the domain of the vehicle (Black, 1962; Hesse, 1966). The exact
form of this interaction differs somewhat from one theory to another. The metaphor often is more effective when two circumstances occur. First, the tenor and
the vehicle share many similar characteristics (e.g., the potential explosiveness
of abused children and time bombs). Second, the domains of the tenor and the
vehicle are highly dissimilar (e.g., the domain of humans and the domain of
weapons) (Tourangeau & Sternberg, 1981, 1982).
• Another view is that that metaphors are essentially a non-literal form of classinclusion statements (Glucksberg & Keysar, 1990). According to this view, the
tenor of each metaphor is a member of the class characterized by the vehicle of
the given metaphor. That is, we understand metaphors not as statements of
comparison but as statements of category membership, in which the vehicle is
a prototypical member of the category. Suppose I say, “My colleague’s partner
is an iceberg.” I am thereby saying that the partner belongs to the category of
things that are characterized by an utter lack of personal warmth, extreme
Language in a Social Context
421
rigidity, and the ability to produce a massively chilling effect on anyone in the
surrounding environment. For a metaphor to work well, the reader should find
the salient features of the vehicle (“iceberg”) to be unexpectedly relevant as features of the tenor (“my colleague’s partner”). That is, the reader should be at
least mildly surprised that prominent features of the vehicle may characterize
the tenor. But after consideration, the reader should agree that those features
do describe the tenor.
Metaphors enrich our language in ways that literal statements cannot match.
Our understanding of metaphors seems to require not only some kind of comparison.
It also requires that the domains of the vehicle and of the tenor interact in some
way. Reading a metaphor can change our perception of both domains. It therefore
can educate us in a way that is perhaps more difficult to transmit through literal
speech.
A very prominent metaphor in cognitive psychology is that of humans as information processors. This metaphor highlights certain aspects of humans, such as our
limited capacity for information processing. This limited capacity leads us to be selective in terms of what information to attend to in our environment (Newell &
Broeder, 2008). Metaphors such as that of the human information processor guide
scientific thinking and research.
Metaphors can enrich our speech in social contexts. For example, suppose we
say to someone, “You are a prince.” Chances are that we do not mean that the person is literally a prince. Rather, we mean that the person has characteristics of a
prince. How, in general, do we use language to negotiate social contexts? We explore the social contexts of language in the next section.
CONCEPT CHECK
1. What is linguistic relativity?
2. What impact can language have on the perception of color?
3. What is additive bilingualism?
4. Does age influence our ability to learn languages?
5. What are the single-system and dual-system hypotheses?
6. Name some kinds of slips of the tongue people make when they speak.
7. What are the key elements of metaphors?
Language in a Social Context
The study of the social context of language is a relatively new area of linguistic research. One aspect of context is the investigation of pragmatics, the study of how
people use language. It includes sociolinguistics and other aspects of the social context of language.
Under most circumstances, you change your use of language in response to contextual cues without giving these changes much thought. Similarly, you usually unselfconsciously change your language patterns to fit different contexts.
For example, in speaking with a conversational partner, you seek to establish
common ground, or a shared basis for engaging in a conversation (Clark & Brennan,
1991). When we are with people who share background, knowledge, motives, or
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INVESTIGATING COGNITIVE PSYCHOLOGY
Language in Different Contexts
To get an idea of how you change your use of language in different contexts, suppose
that you and your friend are going to meet right after work. Something comes up and
you must call your friend to change the time or place for your meeting. When you call
your friend at work, your friend’s supervisor answers and offers to take a message. Exactly what will you say to your friend’s supervisor to ensure that your friend will know
about the change in time or location? Suppose, instead, that the 4-year-old son of your
friend’s supervisor answers. Exactly what will you say in this situation? Finally, suppose
that your friend answers directly. How will you have modified your language for each
context, even when your purpose (underlying message) in all three contexts was the
same?
goals, establishing common ground is likely to be easy and scarcely noticeable.
When little is shared, however, such common ground may be hard to find.
Gestures and vocal inflections, which are forms of nonverbal communication,
can help establish common ground. One aspect of nonverbal communication is personal space—the distance between people in a conversation or other interaction that
is considered comfortable for members of a given culture. Proxemics is the study of
interpersonal distance or its opposite, proximity. It concerns itself with relative distancing and the positioning of you and your fellow conversants. In the United
States, 2.45 feet to 2.72 feet are considered about right. In Mexico, the adequate
distance ranges from 1.65 to 2.14 feet, whereas in Costa Rica it is between 1.22 and
1.32 feet (Baxter, 1970). Scandinavians expect more distance. Middle Easterners,
southern Europeans, and South Americans expect less (Sommer, 1969; Watson,
1970).
When on our own familiar turf, we take our cultural views of personal space
for granted. Only when we come into contact with people from other cultures do
we notice these differences. For example, when the author was visiting Venezuela,
he noticed his cultural expectations coming into conflict with the expectations of
those around him. He often found himself in a comical dance: He would back off
from the person with whom he was speaking; meanwhile, that person was trying to
move closer. Within a given culture, greater proximity generally indicates one or
more of three things. First, the people see themselves in a close relationship. Second, the people are participating in a social situation that permits violation of the
bubble of personal space, such as close dancing. Third, the “violator” of the bubble
is dominating the interaction.
Even within our own culture, there are differences in the amount of personal
space that is expected. For instance, when two colleagues are interacting, the personal space is much smaller than when an employee and supervisor are interacting.
When two women are talking, they stand closer together than when two men are
talking (Dean, Willis, & Hewitt, 1975; Hall, 1966).
Does interpersonal distance also play a role in virtual-reality environments?
When virtual worlds are created, a lot of factors matter in determining how believable the virtual worlds are. How people dress, how the streets look, and what sounds
are in the background all facilitate or make it harder for people to immerse
Language in a Social Context
423
themselves in that environment. For example, when you visit a virtual place located
in Latin America, you expect to see people who look Latin American. To create
lifelike simulations, it also matters how people behave during interpersonal interactions. How close do they stand together, how often do they look at each other, and
how long do they keep that gaze? Computational models are being developed to simulate the behavior of people from different cultures (Jan et al., 2007).
Violations of personal space, even in virtual environments, cause discomfort
(Wilcox et al., 2006). When given the option, people whose personal space is violated in a virtual environment will move away (Bailenson et al., 2003). Physical
space is also maintained in video conferencing (Grayson & Coventry, 1998).
These findings on proxemics indicate the importance of interpersonal space in
all interactions. They also indicate that proxemics is important, even when one or
more of the people are not physically present.
Speech Acts
When we communicate with others, we can use either direct or indirect speech. We
will examine both kinds of speech acts in the next two sections.
Direct Speech Acts
When you speak, what kinds of things can you accomplish? Speech acts address the
question of what you can accomplish with speech and fall into five basic categories,
based on the purpose of the acts (Searle, 1975a; see also Harnish, 2003). There are
essentially five things you can accomplish with speech. Table 10.1 identifies these
categories and gives examples of each.
The appealing thing about Searle’s taxonomy is that it classifies almost any
statement that might be made. It shows the different kinds of things speech can
accomplish. It also shows the close relationship between language structure and
language function.
Indirect Speech Acts
Sometimes speech acts are indirect, meaning that we accomplish our goals in speaking in an oblique fashion. One way of communicating obliquely is through indirect
requests, through which we make a request without doing so straightforwardly
(Gordon & Lakoff, 1971; Searle, 1975b), for example, “Won’t you please take out
the garbage?”
Types of Indirect Speech Acts There are four basic ways of making indirect
requests:
•
•
•
•
asking or making statements about abilities;
stating a desire;
stating a future action; and
citing reasons.
Examples of these forms of indirect requests are illustrated in Table 10.2. In
each case, the indirect request is aimed at having a waitress tell the speaker where
to find the restroom in a restaurant.
When are indirect speech acts interpreted literally, and when is the indirect
meaning understood by the listener? When an indirect speech act, such as “Must
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Table 10.1
Searle’s Taxonomy of Speech Acts
The five basic categories of speech acts encompass the various tasks that can be accomplished through speech (or
other modes of using language).
Speech Act
Description
Example
Representative
A speech act by which a person
conveys a belief that a given
proposition is true. The speaker
can use various sources of information to support the given belief.
But the statement is nothing more,
nor less, than a statement of belief.
Qualifiers can be added to show
the speaker’s degree of certainty.
Mr. Smith has a son named Jack and a daughter named Jill. If
Mr. Smith says, “It’s important for Jack to learn responsibility.
Asking him to help shovel the driveway is one way he can learn
about responsibility,” he is conveying that he believes it is important to teach children responsibility, and that having them
participate in household tasks is one way to achieve this goal.
He can use various sources of information to support his belief.
Nonetheless, the statement is nothing more or less than
a statement of belief. Similarly, he can make a statement that is
more directly verifiable, such as, “As you can see on this thermometer, the temperature outside is 31 degrees Fahrenheit.”
Directive
An attempt by a speaker to get a
listener to do something, such as
supplying the answer to a question. Sometimes a directive is quite
indirect. For example, almost any
sentence structured as a question
probably is serving a directive
function. Any attempt to elicit assistance of any kind, however
indirect, falls into this category.
Mr. Smith wants Jack to help him shovel snow. He can request
this in various ways, some of which are more direct than others,
such as, “Please help me shovel the snow,” or “It sure would be
nice if you were to help me shovel the snow,” or “Would you
help me shovel the snow?” The different surface forms are all
attempts to get Jack’s help. Some directives are quite indirect. If
Mr. Smith asks, “Has it stopped raining yet?” he is still uttering
a directive, in this case seeking information rather than physical
assistance. In fact, almost any sentence structured as a question
probably serves a directive function.
Commissive
A commitment by the speaker to
engage in some future course of
action. Promises, pledges, contracts, guarantees, assurances,
and the like all constitute
commissives.
If Jack responds, “I’m busy now, but I’ll help you shovel the snow
later,” he is uttering a commissive, in that he is pledging his future
help. If Jill then says, “I’ll help you,” she too is uttering a commissive, because she is pledging her assistance now. Promises,
pledges, contracts, guarantees, assurances, and the like all
constitute commissives.
Expressive
A statement regarding the speaker’s psychological state.
If Mr. Smith tells Jack later, “I’m really upset that you didn’t come
through in helping me shovel the snow,” that would be an expressive. If Jack says, “I’m sorry I didn’t get around to helping you
out,” he would be uttering an expressive. If Jill says, “Daddy, I’m
glad I was able to help out,” she is uttering an expressive.
Declaration
(also termed
performative)
A speech act by which the very act
of making a statement brings
about an intended new state of
affairs. Declarations also are
termed performatives (Clark &
Clark, 1977).
Suppose that you are called into your boss’s office and told that
you are responsible for the company losing $50,000. Then your
boss says, “You’re fired.” The speech act results in your being in
a new state—that is, unemployed. You might then tell your boss,
“That’s fine, because I wrote you a letter yesterday saying that
the money was lost because of your glaring incompetence, not
mine, and I resign.” You are making a declaration.
you open the window?” is presented in isolation, it usually first is interpreted literally, for example, as “Do you need to open the window?” (Gibbs, 1979). When the
same speech act is presented in a story context that makes the indirect meaning
clear, the sentence first is interpreted in terms of the indirect meaning. For instance,
Language in a Social Context
Table 10.2
Type of Indirect
Speech Act
425
Indirect Speech Acts
Example of an Indirect Request For Information
Abilities
If you say, “Can you tell me where the restroom is?” to a waitress at a restaurant, and she says, “Yes,
of course I can,” the chances are she missed the point. The question about her ability to tell you the
location of the restroom was an indirect request for her to tell you exactly where it is.
Desire
“I would be grateful if you told me where the restroom is.” Your statements of thanks in advance are
really ways of getting someone to do what you want.
Future action
“Would you tell me where the restroom is?” Your inquiry into another person’s future actions is
another way to state an indirect request.
Reasons
You need not spell out the reasons to imply that there are good reasons to comply with the request.
For example, you might imply that you have such reasons for the waitress to tell you where the
restroom is by saying, “I need to know where the restroom is.”
suppose a character in a story had a cold and asked, “Must you open the window?” It
would be interpreted as an indirect request: “Do not open the window.”
Subsequent work showed that indirect speech acts often anticipate what potential obstacles the respondent might pose. These obstacles are specifically addressed
through the indirect speech act (Gibbs, 1986). For example:
• “May I have … ?” addresses potential obstacles of permission.
• “Would you mind … ?” addresses potential obstacles regarding a possible imposition on the respondent.
• “Do you have … ?” addresses potential obstacles regarding availability.
Indirect requests that ask permission are judged to be the most polite (Clark &
Schunk, 1980). Similarly, indirect requests that speak to an obligation (i.e.,
“Shouldn’t you…?”) are judged as the most impolite (Clark & Schunk, 1980). The
responses to these requests typically match the requests in terms of politeness (Clark
& Schunk, 1980).
Pinker’s Theory of Indirect Speech Steven Pinker and his colleagues (2007) recently developed a three-part theory of indirect speech. Its basic assumption is that
communication is always a mixture of cooperation and conflict. Indirect speech
gives the speaker the chance to voice an ambiguous request that the listener can
accept or decline without reacting adversely to it. According to the three-part theory, indirect speech can serve three purposes:
1. Plausible deniability. Imagine a policeman pulls you over when you are driving
and wants to give you a traffic ticket. By saying, “Maybe the best thing is to take
care of this right here,” you can imply that you might be willing to pay a bribe
to get off the ticket. If the policeman is inclined to accept, he can do so. If he is
not interested in the bribe, he cannot arrest you for the attempted bribe (you
hope!) because you never made an explicit offer. You purposely were indirect
in order to ensure, to the extent possible, plausible deniability (in this case, of
your attempt to bribe). Similarly, sexual overtures are often made in an indirect
way in order to ensure deniability should the object of the overtures react
negatively.
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2. Relationship negotiation. This occurs when a person uses indirect language because the nature of a relationship is ambiguous. For example, one purpose of an
indirect sexual overture may be plausible deniability (the first purpose). But the
overture also may be indirect to avoid offending the targeted individual if he or
she is not interested in a sexual relationship (relationship negotiation). In this
case, indirectness is a way of helping two people mutually resolve the nature of
their relationship.
3. Language as a digital medium of indirect as well as direct communication. Language can serve purposes other than direct communication. For example, suppose
the emperor believes he is wearing fine robes when he is in fact naked. A boy
shouts out, “The emperor has no clothes.” The boy is not telling the others anything they do not know—they can see the emperor has no clothes. What he is
telling them is that it is not just they as individuals who see no clothes—everyone sees the emperor wearing no clothes. The boy has communicated something
digitally—that all know the emperor is naked—that before was ambiguous.
Both direct and indirect communication are part of what makes a conversation
successful. What else leads to a successful conversation?
Characteristics of Successful Conversations
In speaking to each other, we implicitly set up a cooperative enterprise. Indeed, if we
do not cooperate with each other when we speak, we often end up talking past
rather than to each other. In other words, we fail to communicate what we intended. Conversations thrive on the basis of a cooperative principle, by which we
seek to communicate in ways that make it easy for our listener to understand what
we mean (Grice, 1967; Mooney, 2004). According to Grice, successful conversations
follow four maxims: the maxim of quantity, the maxim of quality, the maxim of relation, and the maxim of manner. These are also called conversational postulates.
Examples of these maxims are provided in Table 10.3.
To these four maxims noted by Grice, we might add an additional maxim: Only
one person speaks at a time (Sacks, Schegloff, & Jefferson, 1974). Given that maxim,
the situational context and the relative social positions of the speakers affect turntaking (Keller, 1976). Sociolinguists have noted many ways in which speakers signal
to one another when and how to take turns. Sometimes people flaunt the conversational postulates to make a point. For example, suppose one says, “My parents are
wardens.” One is not providing full information (what, exactly, does it mean for one’s
parents to be wardens?). But the ambiguity is intentional. Or sometimes when a conversation on a topic is becoming heated, one purposely may switch topics and bring up
an irrelevant issue. One’s purpose in doing so is to get the conversation to another,
safer topic. When we flaunt the postulates, we are sending an explicit message by doing
so: The postulates retain their importance because their absence is so notable.
People with autism have difficulty with both language and emotion. It is therefore not surprising that they have particular difficulty in detecting violations of the
Gricean maxims (Eales, 1993; Surian, 1996). Further discussion of language impairments in people with autism are discussed later in the chapter.
Gender and Language
Within our own culture, do men and women speak a different language? Gender
differences have been found in the content of what we say. Young girls are more
Language in a Social Context
Table 10.3
427
Conversational Postulates
To maximize the communication that occurs during conversation, speakers generally follow four maxims.
Postulate
Maxim
Example
Maxim of quantity
Make your contribution to a conversation as
informative as required but no more informative than is appropriate.
If someone asks you the temperature outside and
you reply, “It’s 31.297868086298 degrees out
there,” you are violating the maxim of quantity
because you are giving more information than
was probably wanted.
Maxim of quality
Your contribution to a conversation should be
truthful; you are expected to say what you
believe to be the case. Irony, sarcasm, and
jokes might seem to be exceptions to the
maxim of quality, but they are not. The listener
is expected to recognize the irony or sarcasm
and to infer the speaker’s true state of mind
from what is said. Similarly, a joke often is
expected to accomplish a particular purpose.
It usefully contributes to a conversation when
that purpose is clear to everyone.
Clearly, there are awkward circumstances in
which each of us is unsure of just how much honesty is being requested. Under most circumstances, however, communication depends on an
assumption that both parties to the communication
are being truthful.
Maxim of relation
You should make your contributions to a
conversation relevant to the aims of the
conversation.
Almost any large meeting we attend seems to
have someone who violates this maxim. This
someone inevitably goes into long digressions that
have nothing to do with the purpose of the meeting and that hold up the meeting. “That reminds
me of a story a friend once told me about a
meeting he once attended, where …”
Maxim of manner
You should try to avoid obscure expressions,
vague utterances, and purposeful obfuscation
of your point.
Nobel Prize–winning physicist Richard Feynman
(1997) described how he once read a paper by a
well-known scholar, and he found that he could
not make heads or tails of it. One sentence went
something like this: “The individual member of the
social community often receives information via
visual, symbolic channels” (p. 281). Feynman
concluded, in essence, that the scholar was violating the maxim of manner when Feynman realized that the sentence meant, “People read.”
likely to ask for help than are young boys (Thompson, 1999). Older adolescent and
young adult males prefer to talk about political views, sources of personal pride, and
what they like about the other person. In contrast, females in this age group prefer to
talk about feelings toward parents, close friends, classes, and their fears (Rubin et al.,
1980). Also, in general, women seem to disclose more about themselves than do
men (Morton, 1978).
Conversations between men and women are sometimes regarded as crosscultural communication (Tannen, 1986, 1990, 1994). Young girls and boys learn
conversational communication in essentially separate cultural environments through
their same-sex friendships. As men and women, we then carry over the conversational styles we have learned in childhood into our adult conversations.
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Tannen has suggested that male–female differences in conversational style
largely center on differing understandings of the goals of conversation. These cultural differences result in contrasting styles of communication. These in turn can
lead to misunderstandings and even break-ups as each partner somewhat unsuccessfully tries to understand the other. Men see the world as a hierarchical social order
in which the purpose of communication is to negotiate for the upper hand, to preserve independence, and to avoid failure (Tannen, 1990, 1994). Each man strives to
one-up the other and to “win” the contest. Women, in contrast, seek to establish a
connection between the two participants, to give support and confirmation to
others, and to reach consensus through communication.
To reach their conversational goals, women use conversational strategies that
minimize differences, establish equity, and avoid any appearances of superiority on
the part of one or another conversant. Women also affirm the importance of and
the commitment to the relationship. They handle differences of opinion by negotiating to reach a consensus that promotes the connection and ensures that both parties at least feel that their wishes have been considered. They do so even if they are
not entirely satisfied with the consensual decision.
Men enjoy connections and rapport. But because men have been raised in a
gender culture in which status plays an important role, other goals take precedence
in conversations. Tannen has suggested that men seek to assert their independence
from their conversational partners. In this way, they indicate clearly their lack of
acquiescence to the demands of others, which would indicate lack of power. Men
also prefer to inform (thereby indicating the higher status conferred by authority)
rather than to consult (indicating subordinate status) with their conversational partners. The male partner in a close relationship thus may end up informing his partner
of their plans. In contrast, the female partner expects to be consulted on their plans.
When men and women engage in cross-gender communications, their crossed purposes often result in miscommunication because each partner misinterprets the
other’s intentions.
Tannen has suggested that men and women need to become more aware of their
cross-cultural styles and traditions. In this way, they may at least be less likely to
misinterpret one another’s conversational interactions. They are also both more
likely to achieve their individual aims, the aims of the relationship, and the aims
of the other people and institutions affected by their relationship. Such awareness
is important not only in conversations between men and women. It is also important
in conversations among family members in general (Tannen, 2001).
Tannen may be right. But at present, converging operations are needed, in addition to Tannen’s sociolinguistic case-based approach, to pin down the validity and
generality of her interesting findings.
Gender differences in the written use of language have also been observed (Argamon et al., 2003). For example, a study that analyzed more than 14,000 text files
from 70 separate studies found that women used more words that were related to
psychological and social processes, whereas men related more to object properties
and impersonal topics (Newman et al., 2008).
These findings are not conclusive. A study examining blogs noted that the type
of blog, more than the gender of the author, dictated the writing style (Herring &
Paolillo, 2006).
Thus far we have discussed the social and cognitive contexts for language. Language use interacts with, but does not completely determine, the nature of thought.
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PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
IMPROVING YOUR COMMUNICATION WITH OTHERS
Think about how your gender influences your conversational style. Construct some ways to
communicate more effectively with people of the opposite sex. How might your speech
acts and conversational postulates differ? If you are a man, do you tend to use and prefer
directives and declarations over expressives and commissives? If you are a woman, do
you use and prefer expressives and commissives over directives and declarations? If so,
speaking to people of the opposite sex can lead to misinterpretations of meaning based
on differences in style. For example, when you want to get another person to do something, it may be best to use the style that more directly reflects the other person’s style. In
this case, you might use a directive with men (“Would you go to the store?”) and an expressive with women (“I really enjoy going shopping.”). Also, remember that your responses should match the other person’s expectations regarding how much information to
provide, honesty, relevance, and directness. The art of effective communication really involves listening carefully to another person, observing body language, and interpreting
the person’s goals accurately. This can be accomplished only with time, effort, and
sensitivity.
Have you recently been in a situation where you felt communication was not ideal? Write
down the communication and identify what you would do differently. How could you prevent such a situation, or at least improve it?
Social interactions influence the ways in which language is used and comprehended
in discourse and reading. Next, we highlight some of the insights we have gained by
studying the physiological context for language. Specifically, how do our brains process language? And do nonhuman animals have language?
CONCEPT CHECK
1. What are the different categories of speech acts?
2. Name some advantages of indirect speech.
3. What are some maxims of successful conversations?
4. How does gender have an impact on language?
Do Animals Have Language?
Some cognitive psychologists specialize in the study of nonhuman animals. Why
would they study such animals, when humans are so readily available? There are several reasons.
First, nonhuman animals often are presumed to have somewhat simpler cognitive systems. It is therefore easier to model their behavior. These models can then
be bootstrapped to the study of humans, as has happened most notably in the study
of learning. For example, a model of conditioning that originally was proposed for
nonhuman animals such as white rats has proven to be extremely useful in understanding human learning (Rescorla & Wagner, 1972). The model, when first proposed,
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was unique in suggesting that nonhuman animal cognition is more complex than
had previously been thought. Robert Rescorla and Allan Wagner showed that classical conditioning depends not just on simple contiguity of an unconditioned and
conditioned stimulus, but rather on the contingency involved in the situation. In
other words, classical conditioning occurs when animals reduce uncertainty in a
learning situation—when they learn the relation between occurrences of two kinds
of stimuli. In sum, research on simpler animals often leads to important insights
about human learning.
Second, nonhuman animals can be subject to procedures that would not be
possible for human ones. For example, a rat may be sacrificed at the end of a learning experiment to study changes that have occurred in the brain as a result of learning. A rat may also be injected with drugs to examine a compound’s effects on
functioning. Such experimentation clearly cannot be completed on humans. All
such studies, of course, must be subject to institutional approval for the ethics of experimentation before they are conducted.
Third, nonhuman animals that are not in the wild can serve as full-time subjects, or at least, regularly available subjects. They are typically there when the experimenter needs them. In contrast, college students and other humans have many
other obligations, such as classes, homework, jobs, and personal commitments.
Moreover, sometimes, even when they sign up for research, they fail to show up.
Fourth, an understanding of the comparative and evolutionary as well as developmental bases of human behavior requires studies of nonhuman animals of various
kinds (Rumbaugh & Beran, 2003). If cognitive psychologists want to understand the
origins of human cognition in the distant past, they need to study other kinds of
animals besides humans.
The philosopher René Descartes suggested that language is what qualitatively distinguishes human beings from other species. Was he right? Before we get into the
particulars of language in nonhuman species, we should emphasize the distinction
between communication and language. Few would doubt that nonhuman animals
communicate in one way or another. What is at issue is whether they do so through
what reasonably can be called a language. Whereas language is an organized means of
combining words to communicate, communication more broadly encompasses not only
the exchange of thoughts and feelings through language but also nonverbal expression. Examples include gestures, glances, distancing, and other contextual cues.
Primates—especially chimpanzees—offer our most promising insights into nonhuman language. Jane Goodall, the well-known investigator of chimpanzees in the
wild, has studied diverse aspects of chimp behavior. One is vocalizations. Goodall
considers many of them to be clearly communicative, although not necessarily indicative of language. For example, chimps have a specific cry indicating that they are
about to be attacked. They have another for calling their fellow chimps together.
Nonetheless, their repertoire of communicative vocalizations seems to be small, nonproductive (new utterances are not produced), limited in structure, lacking in structural complexity, and relatively non-arbitrary. It also is not spontaneously acquired.
The chimps’ communications thus do not satisfy our criteria for a language.
But can chimps be taught to use language by humans? Several researchers have
had chimps and tried to teach them language skills. The vocal tract of chimpanzees
is different from the one of humans, so by their very nature they are not able to
reproduce the majority of human sounds. Instead, researchers have reverted to teaching them sign language.
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431
Savage-Rumbaugh and her colleagues (Savage-Rumbaugh et al., 1986, 1993)
have found the best evidence yet in favor of language use among chimpanzees. Their
pygmy chimpanzees spontaneously combined the visual symbols (such as red triangles and blue squares) of an artificial language the researchers taught them. They
even appear to have understood some of the language spoken to them. One pygmy
chimp in particular (Greenfield & Savage-Rumbaugh, 1990) seemed to possess remarkable skill, even possibly demonstrating a primitive grasp of language structure.
It may be that the difference in results across groups of investigators is due to the
particular kind of chimp tested or to the procedures used. The chimp’s language
may not meet all the constraints posed by the properties of language described at
the beginning of the chapter. For example, the language used by the chimps is not
spontaneously acquired. Rather, they learn it only through very deliberate and systematic programs of instruction.
Another famous exploration of language in a nonhuman can be seen in the
gorilla Koko. Koko can use approximately 1,000 signs and can communicate quite
effectively with humans, expressing both desires and thoughts. Evidence also suggests
that Koko is able to understand and use humor (Gamble, 2001). Koko also seems to
be able to use language in a novel way, both combining signs in new ways and by
forming entirely new signs. One of the most famous examples of this behavior was
exhibited when Koko developed a new sign for “ring” by combining “finger” and
“bracelet” (Hill, 1978).
A neuroanatomical study of chimpanzees found that when chimps use tools, the
brain regions that were especially active corresponded to Broca’s and Wernicke’s
areas in humans. Both of those areas are associated with language comprehension
and production, and it has been hypothesized that the use of tools in early humans
actually facilitated the development of language (Hopkins et al., 2007).
A less positive view of the linguistic capabilities of chimpanzees was taken by
Herbert Terrace (1987), who raised a chimp named Nim Chimpsky, a takeoff on
Noam Chomsky, the eminent linguist. Over the course of several years, Nim made
more than 19,000 multiple-sign utterances in a slightly modified version of ASL.
Most of his utterances consisted of two-word combinations. Terrace’s careful analysis
of these utterances, however, revealed that most of them were repetitions of what
Nim had seen. Terrace concluded that, despite what appeared to be impressive accomplishments, Nim did not show even the rudiments of syntactical expression. The
chimp could produce single- or even multiple-word utterances, but not in a syntactically organized way. For example, Nim would alternate signing, “Give Nim banana,”
“Banana give Nim,” and “Banana Nim give,” showing no preference for the grammatically correct form. Moreover, Terrace also studied films showing other chimpanzees supposedly producing language. He came to the same conclusion for them that
he had reached for Nim. His position, then, is that although chimpanzees can understand and produce utterances, they do not have linguistic competence in the same
sense that even very young humans do. Their communications lack structure, and
particularly multiplicity of structure. At this point, we just cannot be sure if the
chimps truly show the full range of language abilities.
Chimpanzees are not the only ones that can learn language to a certain extent—
other species can as well. Take the example of Alex, an African Grey Parrot who
died in 2007. Alex could produce more than 200 words and express a variety of
complex concepts, including present and absent and a zero-like concept. Recent evidence also suggests that Alex was capable of novel combinations of words to form
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new ways of expressing concepts (Pepperberg, 1999, 2007; Pepperberg & Gordon,
2005).
Whether nonhuman species can use language, it seems almost certain that the
language facility of humans far exceeds that of other species psychologists have studied. Noam Chomsky (1991) has stated the key question regarding nonhuman language quite eloquently: “If an animal had a capacity as biologically advantageous as
language but somehow hadn’t used it until now, it would be an evolutionary miracle,
like finding an island of humans who could be taught to fly.”
CONCEPT CHECK
1. Why do psychologists conduct research with animals?
2. Do animals have the same potential for language as humans? Explain.
Neuropsychology of Language
In this part of the chapter, we will first explore which parts of the brain are involved
in language production and comprehension. Afterwards, we will turn our attention
to specific instances of language impairment. Recall from Chapter 2 that some of our
earliest insights into brain localization related to an association between specific language deficits and specific organic damages to the brain, as first discovered by Marc
Dax, Paul Broca, and Carl Wernicke (see also Brown & Hagoort, 1999; Garrett,
2003). Broca’s aphasia and Wernicke’s aphasia are particularly well-documented instances in which brain lesions affect linguistic functions.
Brain Structures Involved in Language
Through studies of patients with brain lesions, researchers have learned a great deal
about the relations between particular areas of the brain (the areas of lesions observed in patients) and particular linguistic functions (the observed deficits in the
brain-injured patients). For example, we can broadly generalize that many linguistic
functions are located primarily in the areas identified by Broca and Wernicke. Damage to Wernicke’s area, in the posterior of the cortex, is now believed to entail more
grim consequences for linguistic function than does damage to Broca’s area, closer to
the front of the brain (Kolb & Whishaw, 1990). Also, lesion studies have shown
that linguistic function is governed by a much larger area of the posterior cortex
than just the area identified by Wernicke. In addition, other areas of the cortex
also play a role. Examples are association-cortex areas in the left hemisphere and a
portion of the left temporal cortex.
The Brain and Word Recognition
One avenue of research involves the study of the metabolic activity of the brain and
the flow of blood in the brain during the performance of various verbal tasks. fMRI
studies have found that the middle part of the superior temporal sulcus (STS) responds more strongly to speech sounds than to non-speech sounds. The response
takes place in both sides of the STS, although it is usually stronger in the left hemisphere. Interestingly, it does not matter whether words or pseudo-words are presented. This means it is unlikely that processing of semantic information takes
place here (Binder, 2009; Binder et al., 1996, 2000; Desai et al., 2005).
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The Brain and Semantic Processing
Where does semantic processing take place then? Research shows a relatively consistent picture. The evidence comes from studies involving patients with Alzheimer’s
disease, aphasia, autism, and many other disorders.
There are five brain regions that are involved in the storage and retrieval of
meaning (Binder, 2009):
• the ventral temporal lobes, including middle and inferior temporal, anterior fusiform, and anterior parahippocampal gyri;
• the angular gyrus;
• the anterior aspect (pars orbitalis) of the inferior frontal gyrus;
• the dorsal prefrontal cortex; and
• the posterior cingulate gyrus.
The activation of these areas takes place mostly in the left hemisphere, although
there is some activation in the right hemisphere. It is suspected, however, that the
right hemisphere does not play a significant role in word recognition (Binder, 2009;
see also Binder et al., 2005, 2009; Ischebeck et al., 2004; Sabsewitz et al., 2005;
Vandenbulcke, 2006).
Finally, some other subcortical structures (e.g., the basal ganglia and the posterior thalamus) also are involved in linguistic function. These structures remain
poorly understood, however. Surgeons sometimes conduct brain surgery while
patients are awake to map the language pathways and try to preserve the language
capabilities of their patients after surgery (Duffau et al., 2008).
The Brain and Syntax
Event-related potentials, or ERPs (see Chapter 2), also can be used to study the processing of language in the brain. For one thing, a certain ERP called N400 (a negative potential 400 milliseconds after stimulus onset) typically occurs when
individuals hear an anomalous sentence (Dambacher & Kliegl, 2007; Kutas & Hillyard, 1980). Thus, if people are presented a sequence of normal sentences but also
anomalous sentences (such as “The leopard is a very good napkin”), the anomalous
sentences will elicit the N400 potential. Moreover, the more anomalous a sentence
is, the greater the response shown in another ERP, P600 (a positive potential 600
milliseconds after the stimulus onset; Kutas & Van Patten, 1994). The P600 effect
seems to be more related to syntactic violations, whereas the N400 effect is more
related to semantic violations (Friederici et al., 2004).
The Brain and Language Acquisition
There is some evidence that the brain mechanisms responsible for language learning are different from those responsible for the use of language by adults (Stiles
et al., 1998). In general, the left hemisphere seems to be better at processing
well-practiced routines. The right hemisphere is better at dealing with novel stimuli. A possibly related finding is that individuals who have learned language later
in life show more right-hemisphere involvement (Neville, 1995; PolkczynskaFiszer, 2008). Perhaps the reason is that language remains somewhat more novel
for them than for others. These findings suggest that one cannot precisely map
linguistic or other kinds of functioning to hemispheres in a way that works for
all people. Rather, the mappings differ somewhat from one person to another
(Zurif, 1995).
CHAPTER 10 • Language in Context
The Plasticity of the Brain
Recent imaging studies of the post-traumatic recovery of linguistic functioning find
that neurological language functioning appears to redistribute to other areas of the
brain. Thus, damage to the major left hemisphere areas responsible for language
functioning sometimes can lead to enhanced involvement of other areas as language
functioning recovers. It is as if previously dormant or overshadowed areas take over
the duties left vacant (Rosenberg et a., 2008; Cappa, et al., 1997).
The Brain and Sex Differences in Language Processing
Another method used to examine brain functioning is fMRI. Through these methods, dominance of the left hemisphere is observed for most language users (Anderson
et al., 2006; Gaillard et al., 2004). Men and women appear to process language differently, at least at the phonological level (Shaywitz, 2005). An fMRI study of men
and women asked participants to perform one of four tasks:
1.
2.
3.
4.
indicate whether a pair of letters was identical;
indicate whether two words have the same meaning;
indicate whether a pair of words rhymes; and
compare the lengths of two lines (a control task).
The researchers found that when both male and female participants were performing the letter-recognition and word-meaning tasks, they showed activation in the left
temporal lobe of the brain. When they were performing the rhyming task, however,
different areas were activated for men versus women. Only the inferior (lower) frontal
region of the left hemisphere was activated for men. The inferior frontal region of both
the left and right hemispheres was activated in women. These results suggested that
men localized their phonological processing more than did women.
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Some intriguing sex differences emerge in the ways that linguistic function appears to be localized in the brain (Kimura, 1987). Men seem to show more lefthemisphere dominance for linguistic function than the women show. Women show
more bilateral, symmetrical patterns of linguistic function. Furthermore, the brain
locations associated with aphasia seemed to differ for men and women. Most aphasic
women showed lesions in the anterior region, although some aphasic women showed
lesions in the temporal region. In contrast, aphasic men showed a more varied pattern of lesions. Aphasic men were more likely to show lesions in posterior regions
rather than in anterior regions. One interpretation of Kimura’s findings is that the
role of the posterior region in linguistic function may be different for women than
it is for men.
Another interpretation relates to the fact that women show less lateralization of
linguistic function. Women may be better able to compensate for any possible loss of
function due to lesions in the left posterior hemisphere through functional offsets in
the right posterior hemisphere. The possibility that there also may be subcortical sex
differences in linguistic function further complicates the ease of interpreting
Kimura’s findings. (Recall also the earlier discussion of communication differences
between men and women.) A recent meta-analysis, however, could not verify any
sex differences in asymmetries of the Planum Temporale (which is at the center of
Wernicke’s area) or in functional imaging findings during language tasks (Sommer
et al., 2008).
Despite the many findings that have resulted from studies of brain-injured patients, there are two key difficulties in drawing conclusions based only on studies of
patients with lesions:
1. Naturally occurring lesions are often not easily localized to a discrete region of
the brain, with no effects on other regions. For example, when hemorrhaging or
insufficient blood flow (such as impairment due to clotting) causes lesions, the
lesions also may affect other areas of the brain. Thus, many patients who show
cortical damage also have suffered some damage in subcortical structures. This
may confound the findings of cortical damage.
2. Researchers are able to study the linguistic function of patients only after the
lesions have caused damage. Typically they are unable to document the linguistic function of patients prior to the damage.
Because it would be unethical to create lesions merely to observe their effects on
patients, researchers are able to study the effects of lesions only in those areas where
lesions happen to have occurred naturally. Other areas therefore are not studied.
Researchers also investigate brain localization of linguistic functions via electrical stimulation of the brain. Gender differences have been investigated this way as
well (Ojemann, 1982; Spring et al., 2008). Through stimulation studies, researchers
have found that stimulation of particular points in the brain seems to yield discrete
effects on particular linguistic functions (such as the naming of objects) across repeated, successive trials. For example, in a given person, repeated stimulation of
one particular point might lead to difficulties in recalling the names of objects on
every trial. In contrast, stimulation of another point might lead to incorrect naming
of objects. In addition, information regarding brain locations in a specific individual
may not apply across individuals. Thus, for a given individual, a discrete point of
stimulation may seem to affect only one particular linguistic function. But across individuals, these particular localizations of function vary widely.
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The effects of electrical stimulation are transitory. Linguistic function returns to
normal soon after the stimulation has ceased. These brain-stimulation studies also
show that many more areas of the cortex are involved in linguistic function than
was thought previously. One study examined electrical stimulation of the brains of
bilingual speakers. The researchers found different areas of the brain were active
when using the primary versus the secondary language to name items. There was,
however, some overlap of active areas with the two languages (Lucas, McKhann, &
Ojemann, 2004).
Using electrical-stimulation techniques, sex differences in linguistic function can
be identified. There is a somewhat paradoxical interaction of language and the brain
(Ojemann, 1982). Although females generally have superior verbal skills to males,
males have a proportionately larger (more diffusely dispersed) language area in their
brains than do females. Counterintuitively, therefore, the size of the language area in
the brain may be inversely related to the ability to use language.
The Brain and Sign Language
Kimura (1981) also has studied hemispheric processing of language in people who
use sign language rather than speech to communicate. She found that the locations
of lesions that would be expected to disrupt speech also disrupt signing. Further, the
hemispheric pattern of lesions associated with signing deficits is the same pattern
shown with speech deficits. That is, all right-handers with signing deficits show
left-hemisphere lesions, as do most left-handers. But some left-handers with signing
deficits show right-hemisphere lesions (see also Newman et al., 2010; Pickell et al.,
2005). This finding supports the view that the brain processes both signing and speech
similarly in terms of their linguistic function. It refutes the view that signing involves
spatial processing or some other non-linguistic form of cognitive processing.
Aphasia
Aphasia is an impairment of language functioning caused by damage to the brain
(Caramazza & Shapiro, 2001; Garrett, 2003; Hillis & Caramazza, 2003). There are
several types of aphasias (Figure 10.5).
Wernicke’s Aphasia
Wernicke’s aphasia is caused by damage to Wernicke’s area of the brain (see Chapter
2). It is characterized by notable impairment in the understanding of spoken words
and sentences. It also typically involves the production of sentences that have the
basic structure of the language spoken but that make no sense. They are sentences
that are empty of meaning. Two examples are “Yeah, that was the pumpkin furthest
from my thoughts” and “the scroolish prastimer ate my spanstakes” (Hillis & Caramazza, 2003, p. 176). In the first case, the words make sense, but not in the context
they are presented. In the second case, the words themselves are neologisms, or
newly created words. Treatment for patients with this type of aphasia frequently
involves supporting and encouraging non-language communication (Altschuler
et al., 2006).
Broca’s Aphasia
Broca’s aphasia is caused by damage to Broca’s area of the brain (see Chapter 2). It is
characterized by the production of agrammatical speech at the same time that verbal
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Neuropsychology of Language
Images not available due to copyright restrictions
Figure 10.5
Healthy and Aphasic Brains.
Brain scans comparing the brain of (a) a normal patient with brains of patients with
comprehension ability is largely preserved. It thus differs from Wernicke’s aphasia in
two key respects. First is that speech is agrammatical rather than grammatical (as in
Wernicke’s). Second is that verbal comprehension is largely preserved. An example
of a production by a patient with Broca’s aphasia is “Stroke … Sunday … arm, talking—bad” (Hillis & Caramazza, 2003, p. 176). The gist of the intended sentence is
maintained, but the expression of it is badly distorted. Broca’s area is important for
speech production, regardless of the format of the speech. In particular, Broca’s area
is activated during imagined or actual sign production (Campbell, MacSweeney, &
Waters, 2007; Horwitz et al., 2003).
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Global Aphasia
Global aphasia is the combination of highly impaired comprehension and production
of speech. It is caused by lesions to both Broca’s and Wernicke’s areas. Aphasia following a stroke frequently involves damage to both Broca’s and Wernicke’s areas.
In one study, researchers found 32% of aphasias immediately following a stroke
involved both Broca’s and Wernicke’s areas (Pedersen, Vinter, & Olsen, 2004).
Anomic Aphasia
Anomic aphasia involves difficulties in naming objects or in retrieving words. The patient may look at an object and simply be unable to retrieve the word that corresponds
to the object. Sometimes, specific categories of things cannot be recalled, such as
names of living things (Jonkers & Bastiaanse, 2007; Warrington & Shallice, 1984).
Autism
Autism is a developmental disorder characterized by abnormalities in social behavior,
language, and cognition (Heinrichs et al., 2009; Pierce & Courchesne, 2003). It is
biological in its origins, and researchers have already identified some of the genes
associated with it (Wall et al., 2009). Children with autism show abnormalities in
many areas of the brain, including the frontal and parietal lobes, as well as the cerebellum, brainstem, corpus callosum, basal ganglia, amygdala, and hippocampus. The
disease was first identified in the middle of the 20th century (Kanner, 1943). It is
five times more common in males than in females. The incidence of diagnosed
autism has increased rapidly over recent years. Between the years of 2000
and 2004, the frequency of diagnosis of autism increased 14% (Chen et al., 2007).
Autism has been diagnosed in recent years in approximately 60 out of every 10,000
children (Fombonne, 2003). This rate corresponds to about 1 out of every 165 children being diagnosed with an autism-spectrum disorder. The increase in recent times
may be a result of a number of causes, including changes in diagnosing strategies or
environmental pollution (Jick & Kaye, 2003; Windham et al., 2006).
Children with autism usually are identified by around 14 months of age, when
they fail to show expected normal patterns of interactions with others. Children with
autism display repetitive movements and stereotyped patterns of interests and activities
(Pierce & Courchesne, 2003). Often they repeat the same motion, over and over
again, with no obvious purpose to the movement. When they interact with someone,
they are more likely to view their lips than their eyes. About half of children with
autism fail to develop functional speech. What speech they do develop tends to be
characterized by echolalia, meaning they repeat, over and over again, speech they
have heard. Sometimes the repetition occurs several hours after the original use of
the words by someone else (Pierce & Courchesne, 2003). People with autism also
may have problems with the semantic encoding of language (Binder, 2009).
There are a variety of theories of autism. One recent theory suggests that autism
can be understood in terms of sex differences in the wiring of the human brain. According to this theory (Baron-Cohen, 2003), male brains are, on average, stronger
than female ones at understanding and building systems. These systems can be concrete ones, such as those involved in building machinery, or they can be abstract
ones, such as those in politics or writing or music.
Females’ brains, in contrast, are stronger at empathizing and communicating.
According to Baron-Cohen, autism results from an extreme male brain. This brain
is almost totally inept in empathy and communication but very strong in systematizing. As a result, individuals with autism sometimes can perform tasks that require a
Key Themes
439
great deal of systematization, such as figuring out the day corresponding to a date
well in the future. As it happens, autism is also much more common among males
than among females. Although this theory has not been conclusively proven, it is
intriguing and currently undergoing further investigation.
Another theory of autism is that of executive dysfunction (Chan et al., 2009;
Ozonoff et al., 1994). Executive functions include abilities to control and regulate
other abilities and behaviors. For example, when you initiate or terminate an action,
or monitor your behavior to see if it helps you in achieving your goals, you are using
executive functions. This theory describes the repetitive motion observed in autism,
as well as difficulties in planning, mental flexibility, and self-monitoring (Hill,
2004). The executive dysfunction theory views autism as associated with dysfunction
in the frontal lobes.
Much of this chapter has revealed the many ways in which language and
thought interact. The following chapter focuses on problem solving and creativity.
But it also further reveals the interconnectedness of the ways in which we use language and the ways in which we think.
CONCEPT CHECK
1. Which parts of the brain are involved in semantic processing?
2. What does “plasticity” refer to with respect to the brain?
3. What are some difficulties when drawing conclusions from lesion studies?
4. What is the difference between Wernicke’s aphasia and Broca’s aphasia?
Key Themes
This chapter deals with several of the themes highlighted in Chapter 1.
Validity of causal inference versus ecological validity. Some researchers study
language comprehension and production in controlled laboratory settings. For example, studies of phonology are likely to occur in a laboratory where it is possible to
gain precise experimental control of stimuli. But work on language and thought
often is done in remote parts of the world where tight experimental controls
are only a dream. Studies of language usage in remote African villages, for example,
typically cannot be done with tight controls, although some control is possible. As
always, a combination of methodologies best enables cognitive psychologists to
understand psychological phenomena to their fullest.
Biological versus behavioral methods. Lesion studies are a particularly good example of a combination of the two methodologies. On the one hand, they require a
deep understanding of the nature of the brain and the parts of the brain affected by
particular lesions. On the other hand, researchers examine behavior to understand
how the particular lesions, and by inference, parts of the brain, are related to behavioral functioning.
Structure versus process. To understand any linguistic phenomena, one must
analyze thoroughly the structure of the language under investigation. One can then
investigate the processes that are used to comprehend and produce this language.
Without an understanding of both structure and process, it would be impossible to
fully understand language and thought.
Suppose you are on a camping trip and are sitting around the campfire at night,
admiring the numerous stars in the sky. Imagine asking someone the following
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metaphorical question, “Would you like to see the sun paint a picture across the
morning sky?” What does this question mean? Some people might say that it means
that you are asking if they would like to wake up early to see how beautiful the sunrise will be the next morning. Others might say it means that it is getting late and
that you should go to sleep to wake up early to see the beautiful sunrise. Now, suppose you ask this same question not on a camping trip but in a sleazy bar. What do
you think the utterance will mean in that context?
Summary
1. How does language affect the way we think?
According to the linguistic-relativity view, cognitive differences that result from using different languages cause people speaking the various
languages to perceive the world differently.
However, the linguistic-universals view stresses
cognitive commonalities across different language users. No single interpretation explains
all the available evidence regarding the interaction of language and thought.
Research on bilinguals seems to show that environmental considerations also affect the interaction of language and thought. For example,
additive bilinguals have established a welldeveloped primary language. The second language adds to their linguistic and perhaps even
their cognitive skills. In contrast, subtractive bilinguals have not yet firmly established their primary language when portions of a second
language partially displace the primary language.
This displacement may lead to difficulties in verbal skills. Theorists differ in their views as to
whether bilinguals store two or more languages
separately (dual-system hypothesis) or together
(single-system hypothesis). Some aspects of multiple languages possibly could be stored separately and others unitarily. Creoles and pidgins
arise when two or more distinct linguistic groups
come into contact. A dialect appears when a
regional variety of a language becomes distinguished by features such as distinctive vocabulary, grammar, and pronunciation.
Slips of the tongue may involve inadvertent verbal errors in phonemes, morphemes,
or larger units of language. Slips of the tongue
include anticipations, perseverations, reversals (including spoonerisms), substitutions,
insertions, and deletions.
Alternative views of metaphor include the
comparison view, the anomaly view, the domaininteraction view, and the class-inclusion view.
2. How does our social context influence our use
of language? Psychologists, sociolinguists, and
others who study pragmatics are interested in
how language is used within a social context.
Their research looks into various aspects of
nonverbal as well as verbal communication.
Speech acts comprise representatives, directives,
commissives, expressives, and declarations. Indirect requests, ways of asking for something
without doing so straightforwardly, may refer
to abilities, desires, future actions, and reasons.
Conversational postulates provide a means for
establishing language as a cooperative enterprise. They comprise several maxims, including
the maxims of quantity, quality, relation, and
manner. Sociolinguists have observed that people engage in various strategies to signal turntaking in conversations.
Sociolinguistic research suggests that male–
female differences in conversational style
center largely on men’s and women’s differing
understandings of the goals of conversation. It
has been suggested that men tend to see the
world as a hierarchical social order in which
their communication aims involve the need
to maintain a high rank in the social order.
In contrast, women tend to see communication as a means for establishing and maintaining their connection to their communication
partners. To do so, they seek ways to demonstrate equity and support and to reach consensual agreement.
In discourse and reading comprehension, we
use the surrounding context to infer the
reference of pronouns and ambiguous phrases.
The discourse context also can influence the
semantic interpretation of unknown words in
passages and aid in acquiring new vocabulary.
Propositional representations of information in
passages can be organized into mental models
for text comprehension. Finally, a person’s
Media Resources
point of view likewise influences what will be
remembered.
3. How can we find out about language by studying the human brain, and what do such studies
reveal? Neuropsychologists, cognitive psychologists, and other researchers have managed to link
quite a few language functions with specific areas
or structures in the brain. They observe what
happens when a particular area of the brain is
injured, is electrically stimulated, or is studied
in terms of its metabolic activity. For most
441
people, the left hemisphere of the brain is vital
to speech. It affects many syntactical aspects and
some semantic aspects of linguistic processing.
For most people, the right hemisphere handles
a more limited number of linguistic functions.
They include auditory comprehension of semantic information, as well as comprehension and
expression of some non-literal aspects of language
use. These aspects involve vocal inflection, gesture, metaphors, sarcasm, irony, and jokes.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Why are researchers interested in the number of
color words used by different cultures?
2. Describe the five basic kinds of speech acts
proposed by Searle.
3. How should cognitive psychologists interpret
evidence of linguistic universals when considering the linguistic-relativity hypothesis?
4. Compare and contrast the kinds of understandings that can be gained by studying speech errors made by healthy people with those that can
be gained by studying the language produced by
people who have particular brain lesions.
5. Write an example of a pidgin conversation between two people and a creole conversation,
focusing on the differences between pidgins and
creoles.
6. Draft an example of a brief dialogue between a
male and a female in which each may misunderstand the other, based on their differing
beliefs regarding the goals of communication.
7. Suppose that you are an instructor of English as a
second language. What kinds of things will you
want to know about your students to determine
how much to emphasize phonology, vocabulary,
syntax, or pragmatics in your instruction?
8. Give an example of a humorous violation of one
of Grice’s four maxims of successful
conversation.
Key Terms
aphasia, p. 436
bilinguals, p. 412
cooperative principle, p. 426
dialect, p. 416
dual-system hypothesis, p. 414
indirect requests, p. 423
linguistic relativity, p. 404
linguistic universals, p. 407
metaphors, p. 420
monolinguals, p. 412
pragmatics, p. 421
similes, p. 420
single-system hypothesis, p. 414
slips of the tongue, p. 418
speech acts, p. 423
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines,
and more.
11
C
H
A
P
T
E
R
Problem Solving and Creativity
CHAPTER OUTLINE
The Problem-Solving Cycle
Types of Problems
Well-Structured Problems
Isomorphic Problems
Problem Representation Does Matter!
Ill-Structured Problems and the Role of Insight
Early Gestaltist Views
The Neo-Gestaltist View
Insights into Insight
Neuroscience and Insight
Obstacles and Aids to Problem Solving
Mental Sets, Entrenchment, and Fixation
Negative and Positive Transfer
Transfer of Analogies
Intentional Transfer: Searching for Analogies
Incubation
Neuroscience and Planning during Problem Solving
Intelligence and Complex Problem Solving
Expertise: Knowledge and Problem Solving
Organization of Knowledge
442
Elaboration of Knowledge
Reflections on Problem Solving
Automatic Expert Processes
Innate Talent and Acquired Skill
Artificial Intelligence and Expertise
Can a Computer Be Intelligent?
The Turing Test
Expert Systems
Creativity
What Are the Characteristics of Creative People?
Neuroscience and Creativity
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
CHAPTER 11 • Problem Solving and Creativity
443
Here are some of the questions we will explore in this chapter:
1. What are some key steps involved in solving problems?
2. What are the differences between problems that have a clear path to a solution versus problems that
do not?
3. What are some of the obstacles and aids to problem solving?
4. How does expertise affect problem solving?
5. What is creativity, and how can it be fostered?
n BELIEVE IT OR NOT
CAN NOVICES HAVE AN ADVANTAGE OVER EXPERTS?
An expert has invested countless hours into his field of
study—be it playing a musical instrument, doing academic research, or playing chess. Does having this expertise always pay off? Research suggests that sometimes
having less knowledge—being a novice—actually gives
you an edge! In one experiment, researchers had expert
and novice chess players briefly view a display of a chessboard with the chess pieces on it, and the players then
had to recall the positions of the chess pieces on the
board. As you might expect, the experts performed quite
a bit better than the novices. However, the setup of the
chess pieces on the board was then changed in a way
that it did not make sense in terms of the actual game of
chess. Suddenly, experts lost their advantage and performed no better, or even worse, than did the novices
(Chase & Simon, 1973; see also Brockmole et al.,
2008). We will explore possible reasons for this effect
later in this chapter in the section on expertise.
Frensch and Sternberg (1989) found that when a
strategic change was made in the rules for bridge, experts
were hurt more than novices, presumably because the experts had become entrenched and somewhat stuck with
the conventional set of rules.
How do you solve problems that arise in your relationships with other people? How do
you solve the “two-string” problem illustrated in Figure 11.1? How does anyone solve
any problem, for that matter? This chapter considers the process of solving problems, as
well as some of the hindrances and aids to problem solving, an effort to overcome obstacles obstructing the path to a solution (Reed, 2000). At the conclusion of this chapter, we discuss creativity and its role in problem solving. Throughout the chapter, we
discuss how people make the “mental leaps” that lead them from having a set of givens
to having a solution to a problem (Holyoak & Thagard, 1995).
The focus of this chapter is on individual problem solving. It is worth remembering, however, that working in groups often facilitates problem solving. The solutions reached by groups often are better than those reached by individuals (Williams
& Sternberg, 1988). This benefit is seen most notably when the group members represent a variety of ability levels (Hong & Page, 2004). We engage in problem solving when we need to overcome obstacles to answer a question or to achieve a goal.
If we quickly can retrieve an answer from memory, we do not have a problem. If we
cannot retrieve an immediate answer, then we have a problem to be solved.
How people solve problems depends partly on how they understand the problem
(Whitten & Graesser, 2003). Consider an example of how understanding the nature
of the problem matters.
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CHAPTER 11 • Problem Solving and Creativity
Figure 11.1 The String Problem.
Imagine that you are the person standing in the middle of this room, in which two strings are hanging down from the ceiling.
Your goal is to tie together the two strings, but neither string is long enough so that you can reach out and grab the other
string while holding either of the two strings. You have available a few clean paintbrushes, a can of paint, and a heavy
canvas tarpaulin. How will you tie together the two strings? If you have trouble finding a solution, look at Figure 11.7.
Source: From Richard E. Mayer, “The Search for Insight: Grappling with Gestalt Psychology’s Unanswered Questions,” in The Nature of
Insight, edited by R. J. Sternberg and J. E. Davidson. © 1995 MIT Press. Reprinted with permission from MIT Press.
People are told the following about a drug (Stanovich, 2003; Stanovich &
West, 1999):
•
•
•
•
150 people received the drug and were not cured.
150 people received the drug and were cured.
75 people did not receive the drug and were not cured.
300 people did not receive the drug and were cured.
Will they understand exactly what they were told? Many people believe that the
drug in this instance is helpful. In fact, the drug described is not helpful at all. On
the contrary, it is harmful. Only 50% of the people who received the drug were
cured (i.e., 150 of 300). In contrast, 80% of the people who did not receive the
drug were cured (300 of 375).
The Problem-Solving Cycle
The problem-solving cycle includes: problem identification, problem definition, strategy formulation, organization of information, allocation of resources, monitoring, and
evaluation (shown in Figure 11.2; see Bransford & Stein, 1993; Pretz, Naples, &
Sternberg, 2003; Sternberg, 1986).
The Problem-Solving Cycle
7
Evaluating problem
solving
1
Problem identification
6
Monitoring problem
solving
5
Allocation of resources
Figure 11.2
445
2
Definition of problem
3
Constructing a strategy
for problem solving
4
Organizing information
about a problem
The Problem-Solving Cycle.
The steps of the problem-solving cycle include problem identification, problem definition, strategy formulation, organization of information, allocation of resources, monitoring, and evaluation.
In considering the steps, remember also the importance of flexibility in following the various steps of the cycle. Successful problem solving may involve occasionally tolerating some ambiguity regarding how best to proceed. Rarely can we solve
problems by following any one optimal sequence of problem-solving steps. We may
go back and forth through the steps. We can change their order, or even skip or add
steps when it seems appropriate. Following is a description of each part of the
problem-solving cycle.
1. Problem identification: Do we actually have a problem?
2. Problem definition and representation: What exactly is our problem?
3. Strategy formulation: How can we solve the problem? The strategy may involve
analysis—breaking down the whole of a complex problem into manageable elements. Instead, or perhaps in addition, it may involve the complementary process of synthesis—putting together various elements to arrange them into
something useful.
Another pair of complementary strategies involves divergent and convergent thinking. In divergent thinking, you try to generate a diverse assortment
of possible alternative solutions to a problem. Once you have considered a variety of possibilities, however, you must engage in convergent thinking to narrow
down the multiple possibilities to converge on a single best answer.
4. Organization of information: How do the various pieces of information in the
problem fit together?
5. Resource allocation: How much time, effort, money, etc., should I put into this
problem?
CHAPTER 11 • Problem Solving and Creativity
Published in The New Yorker 4/19/1993 by Robert Mankoff/www.Cartoonbank.com
446
Sometimes we don’t recognize an important problem that confronts us.
Studies show that expert problem solvers (and better students) tend to devote
more of their mental resources to global (big-picture) planning than do novice
problem solvers. Novices (and poorer students) tend to allocate more time to local (detail-oriented) planning than do experts (Larkin et al., 1980; Sternberg,
1981). For example, better students are more likely than poorer students to spend
more time in the initial phase, deciding how to solve a problem, and less time
actually solving it (Bloom & Broder, 1950). By spending more time in advance
deciding what to do, effective students are less likely to fall prey to false starts,
winding paths, and all kinds of errors. When a person allocates more mental resources to planning on a large scale, he or she is able to save time and energy and
to avoid frustration later on.
6. Monitoring: Am I on track as I proceed to solve the problem?
7. Evaluation: Did I solve the problem correctly?
Our emotions can influence how we implement the problem-solving cycle
(Schwarz & Skurnik, 2003). In groups with participants with high measured emotional intelligence—that is, the ability to identify emotions in others and regulate
emotions in oneself—emotional processing can positively influence problem solving
(Jordan & Troth, 2004). In mathematicians, the ability to regulate their emotional
state (among other factors) is related to higher problem-solving ability (Carlson &
Bloom, 2005).
CONCEPT CHECK
1. Why is the process of solving problems described as a cycle?
2. What are the different steps of the problem-solving cycle?
Types of Problems
447
Types of Problems
Problems can be categorized according to whether they have clear paths to a solution (Davidson & Sternberg, 1984). Well-structured problems have clear paths to
solutions. These problems also are termed well-defined problems. An example would
be, “How do you find the area of a parallelogram?” Ill-structured problems lack
clear paths to solutions (Shin et al., 2003). These problems are also termed illdefined problems. An example is shown in Figure 11.1: “How do you tie together
two suspended strings, when neither string is long enough to allow you to reach
the other string while holding either of the strings?” Or how do you decide on which
house to buy if each of the potential houses in which you are interested has advantages and disadvantages? Of course, in the real world of problems, these two categories may represent a continuum of clarity in problem solving rather than two discrete
classes with a clear boundary between the two. Nonetheless, the categories are useful
in understanding how people solve problems. Next, we consider each of these kinds
of problems in more detail.
Well-Structured Problems
On tests in school, your teachers have asked you to tackle countless well-structured
problems in specific content areas (e.g., math, history, geography). These problems
had clear paths, if not necessarily easy paths, to their solutions—in particular, the
application of a formula. In psychological research, cognitive psychologists might
ask you to solve less content-specific kinds of well-structured problems. For example,
cognitive psychologists often have studied a particular type of well-structured problem: the class of move problems, so termed because such problems require a series of
moves to reach a final goal state. Perhaps the most well known of the move problems is one involving two antagonistic parties, whom we call “hobbits” and “orcs,”
in the Investigating Cognitive Psychology: Move Problems box.
INVESTIGATING COGNITIVE PSYCHOLOGY
Move Problems
Three hobbits and three orcs are on a river bank. The hobbits and orcs need to cross over
to the other side of the river. They have for this purpose a small rowboat that will hold just
two people. There is one problem, however. If the number of orcs on either river bank exceeds the number of hobbits on that bank, the orcs will eat the hobbits on that bank. How
can all six creatures get across to the other side of the river in a way that guarantees that
they all arrive there with the forest intact? Try to solve the problem before reading on.
The solution to the problem is shown in Figure 11.3. The solution contains several
features worth noting. First, the problem can be solved in a minimum of eleven steps,
including the first and last steps. Second, the solution is essentially linear in nature. There
is just one valid move (connecting two points with a line segment) at most steps of
the problem solution. At all but two steps along the solution path, only one error can
be made without violating the rules of the move problem: to go directly backward in
the solution. At two steps, there are two possible forward-moving responses. But both
of these lead toward the correct answer. Thus, again, the most likely error is to return to
a previous state in the solution of the problem.
448
CHAPTER 11 • Problem Solving and Creativity
Shore
River
Shore
1. HHH
OOO
2. HH
OO
H or HHH
O O
O
O
H O
O
O
H
3. HHH
OO
4. HHH
O
O O
5. HHH
O
6. H
O
7. HH
OO
8. OO
OOO
O
OO
H H
HH
OO
H O
H
O
H H
HHH
O
HHH
O
9. OOO
10. O
OO
HHH
OO
O O
or
HH
OO
11. H
O
H
12.
H O or O O
H = Hobbits
Figure 11.3
OO
O
HHH
O
12. HHH
OOO
O = Orcs
Solution to the Problem of the Hobbits and Orcs.
How can you get both the hobbits and the orcs to the other side of the river without any hobbits getting eaten? (For a more detailed description of the problem and its solution, refer to
Investigating Cognitive Psychology: Move Problems.) What can you learn about your own
methods of solving problems by seeing how you approached this particular problem?
Source: From In Search of the Human Mind, by Robert J. Sternberg. Copyright © 1995 by Harcourt Brace &
Company. Reproduced by permission of the publisher.
Types of Problems
449
People seem to make three main kinds of errors when trying to solve well-structured
problems (Greeno, 1974; Simon & Reed, 1976; Thomas, 1974). These errors are:
(1) Inadvertently moving backward: They revert to a state that is further from the end
goal, for instance, moving all of the “orcs” and “hobbits” back to the first side of
the river.
(2) Making illegal moves: They make an illegal move—that is, a move that is not
permitted according to the terms of the problem. For example, a move that resulted in having more than two individuals in the boat would be illegal.
(3) Not realizing the nature of the next legal move: They become “stuck”—they do not
know what to do next, given the current stage of the problem. An example
would be realizing that you must bring one “orc” or “hobbit” back across the
river to its starting point before you can move any of the remaining characters.
One method for studying how to solve well-defined problems is to develop
computer simulations. Here, the researcher’s task is to create a computer program
that can solve these problems. By developing the instructions a computer must execute to solve problems, the researcher may better understand how humans solve
similar kinds of problems. According to one model of problem solving, the problem
solver (which may be using human or artificial intelligence) must view the initial
problem state and the goal state within a problem space (Wenke & Frensch, 2003).
A problem space is the universe of all possible actions that can be applied to solving a problem, given any constraints that apply to the solution of the problem.
Algorithms are sequences of operations (in a problem space) that may be repeated over and over again and that, in theory, guarantee the solution to a problem
(Hunt, 1975; Sternberg, 2000). Generally, an algorithm continues until it satisfies a
condition determined by a program. Suppose a computer is provided with a welldefined problem and an appropriate hierarchy (program) of operations organized
into procedural algorithms. The computer can readily calculate all possible operations and combinations of operations within the problem space. It also can determine the best possible sequence of steps to take to solve the problem.
Unlike computers, however, the human mind does not specialize in high-speed
computations of numerous possible combinations. The limits of our working memory
prohibit us from considering more than just a few possible operations at one time
(Hambrick & Engle, 2003; Kintsch et al., 1999; see also Chapter 5). Newell and
Simon recognized these limits and observed that humans must use mental shortcuts
for solving problems. These mental shortcuts are termed heuristics—informal, intuitive, speculative strategies that sometimes lead to an effective solution and sometimes do not (see Chapter 12 for more on heuristics; Gilovich et al., 2002;
Stanovich, 2003; Sternberg, 2000). Suppose we store, in long-term memory, several
simple heuristics that we can apply to a variety of problems. We thereby can lessen
the burden on our limited-capacity working memory. Studies suggest that when
problem solvers are confronted with a problem for which they cannot immediately
see an answer, effective problem solvers use the heuristic of means–ends analysis. In
this strategy, the problem solver continually compares the current state and the goal
state and takes steps to minimize the differences between the two states. Various
other problem-solving heuristics include working forward, working backward, and generate and test. Table 11.1 illustrates how a problem solver might apply these heuristics to the aforementioned move problem (Greeno & Simon, 1988) and to a more
common everyday problem (Hunt, 1994).
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CHAPTER 11 • Problem Solving and Creativity
Table 11.1
Four Heuristics
These four heuristics may be used in solving the move problem illustrated in Figure 11.3.
Example of Heuristic Applied to
the Move Problem (Greeno &
Simon, 1988)
Example of Heuristic Applied to an
Everyday Problem: How to Travel
by Air from Your Home to Another
Location Using the Most Direct
Route Possible (Hunt, 1994)
Heuristic
Definition of Heuristic
Means–ends
analysis
The problem solver analyzes
the problem by viewing the
end—the goal being sought
—and then tries to decrease
the distance between the current position in the problem
space and the end goal in that
space.
Try to get as many individuals
on the far bank and as few
people on the near bank as
possible.
Try to minimize the distance between home and the destination.
Working
forward
The problem solver starts at
the beginning and tries to
solve the problem from the
start to the finish.
Evaluate the situation carefully
with the six people on one
bank and then try to move
them step by step to the
opposite bank.
Find the possible air routes leading
from home toward the destination,
and take the routes that seem most
directly to lead to the destination.
Working
backward
The problem solver starts at
the end and tries to work
backward from there.
Start with the final state—
having all hobbits and all
orcs on the far bank—and try
to work back to the beginning state.
Find the possible air routes that
reach the destination, and work
backward to trace which of these
routes can be most directly traced
to originate at home.
Generate
and test
The problem solver simply
generates alternative courses
of action, not necessarily in a
systematic way, and then notices in turn whether each
course of action will work.
This method works fairly well
for the move problem because
at most steps in the process,
there is only one allowable
forward move, and there are
never more than two possibilities, both of which eventually
will lead to the solution.
Find the various possible alternative
routes leading from home, then see
which of these routes might be used
to end up at the destination. Choose
the most direct route. Unfortunately,
given the number of possible combinations of routes for air travel, this
heuristic may not be very helpful.
Figure 11.4 shows a rudimentary problem space for the move problem. It illustrates that there may be any number of possible strategies for solving it.
Isomorphic Problems
Sometimes, two problems are isomorphic; that is, their formal structure is the same,
and only their content differs. Sometimes, as in the case of the hobbits and orcs
problem and a similar missionaries and cannibals problem, in which cannibals eat
missionaries when they outnumber them, the isomorphism is obvious. Similarly,
you can readily detect the isomorphism of many games that involve constructing
words from jumbled or scrambled letters. Figure 11.5 also shows a different set of
isomorphic problems. They illustrate some of the puzzles associated with isomorphic
problems.
It often is extremely difficult to observe the underlying structural isomorphism of
problems. It is also difficult to be able to apply problem-solving strategies from one
Types of Problems
451
Problem Space
(All possible strategies)
Working forward
Working backward
minimize
Means–ends analysis
minimize
SOLUTION
PROBLEM
Test: won't work
Test: won't work
Generate
Test: won't work
Test: will work
Figure 11.4 Problem Space.
A problem space contains all the possible strategies leading from the initial problem state to the solution (the goal
state). This problem space, for example, shows four of the heuristics that might be used in solving the move problem
illustrated in Figure 11.3.
Source: From In Search of the Human Mind, by Robert J. Sternberg. Copyright © 1995 by Harcourt Brace & Company. Reproduced by
permission of the publisher.
Figure 11.5 Isomorphic Problems.
Compare the problems illustrated in the games of (a) number scrabble, (b) tic-tac-toe, and (c) magic square. Number
scrabble is based on equations. Which triples of numbers satisfy the equation X þ Y þ Z ¼ 15? Tic-tac-toe requires
one to produce three Xs or three Os in a row, column, or diagonal. The magic square requires one to place numbers in
the tic-tac-toe board so that every row, column, and major diagonal adds up to 15. In what ways are these problems
isomorphic? How do their differences in presentation affect the ease of representing and solving these problems? Although these problems seem different on their surface, they all require the same mental operations for their solution.
452
CHAPTER 11 • Problem Solving and Creativity
problem to another. For example, it may not be clear how an example from a textbook applies to another problem (e.g., one on a test). Problem solvers are particularly unlikely to detect isomorphisms when two problems are similar but not
identical in structure. Furthermore, when the content or the surface characteristics
of the problems differ sharply, detecting the isomorphism of the structure of problems is harder. For example, school-aged children may find it difficult to see the
structural similarity between various word problems that are framed within different
story situations. Similarly, physics students may have difficulty seeing the structural
similarities among various physics problems when different kinds of materials are
used. The problem of recognizing isomorphisms across varying contexts returns us
to the recurring difficulties in problem representation.
Problem Representation Does Matter!
What is the key reason that some problems are easier to solve than others that are
isomorphic to them? Consider the various versions of a problem known as the
Tower of Hanoi. In this problem, the problem solver must use a series of moves
to transfer a set of rings (usually three) from the first of three pegs to the third of
the three pegs, using as few moves as possible (Figure 11.6). There are several
electronic versions of the Tower of Hanoi on-line. You can find them by entering
the search words “tower of Hanoi game” in a search engine. Try it out yourself!
Researchers presented this same basic problem in many different isomorphic
forms, for example, as dots that have to be transferred between boxes (Kotovsky,
Hayes, & Simon, 1985). They found that some forms of the problem took up to 16
times as long to solve as other forms. Although many factors influenced these findings, a major determinant of the relative ease of solving the problem was how the
problem was represented. For example, in the form shown in Figure 11.6, the
Figure 11.6 The Tower of Hanoi.
There are three discs of unequal sizes, positioned on the far-left side of three pegs so that the
largest disc is at the bottom, the middle-sized disc is in the middle, and the smallest disc is on
the top. Your task is to transfer all three discs to the peg on the far right, using the middle peg
as a stationing area as needed. You may move only one disc at a time, and you may never
move a larger disc on top of a smaller disc.
Source: From Intelligence Applied: Understanding and Increasing Your Intellectual Skills, by Robert J. Sternberg.
Copyright © 1986 by Harcourt Brace & Company. Reproduced by permission of the publisher.
Types of Problems
453
physically different sizes of the discs facilitated the mental representation of the restriction against moving larger discs onto smaller discs. Other forms of the problem
did not. There are many variations of this task, involving differing rules and restrictions (Chen, Tian, & Wang, 2007).
Problems such as the Tower of Hanoi challenge problem-solving skills, in
part through their demands on working memory. One study found that there is a
relationship between working-memory capacity and the ability to solve analytic
problems (Fleck, 2007). Other researchers had experimental participants do what
they called the “Tower of London” task, which is very similar to the Tower of
Hanoi (Welsh, Satterlee-Cartmell, & Stine, 1999). In this task, the goal was to
move a set of colored balls across different-sized pegs in order to match a target
configuration. As in the Tower of Hanoi, there were constraints on which balls
could be moved at a given time. The researchers also gave participants two tests
of working-memory capacity. They found that the measures of working-memory
capacity accounted for between 25% and 36% of the variance in how successful
participants were in solving the problem. Interestingly, mental-processing speed,
sometimes touted as a key to intelligence, showed no correlation with success in
solution. The brain areas that seem most involved in the Tower of Hanoi task are
Figure 11.7
Solution to the String Problem.
Many people assume that they must find a way to move themselves toward each string and then bring the two strings
together. They fail to consider the possibility of finding a way to get one of the strings to move toward them, such as by
tying something to one of the strings, then swinging the object as a pendulum, and grabbing the object when it swings
close to the other string. There is nothing in the problem that suggests that the person must move, rather than that the
string may move. Nevertheless, most people presuppose that the constraint exists. By placing an unnecessary and unwarranted constraint on themselves, people make the problem insoluble.
Source: From Richard E. Mayer, “The Search for Insight: Grappling with Gestalt Psychology’s Unanswered Questions,” in The Nature of
Insight, edited by R. J. Sternberg and J. E. Davidson. Copyright © 1995 by MIT Press. Reprinted by permission.
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CHAPTER 11 • Problem Solving and Creativity
the prefrontal cortex, bilateral parietal cortex, and bilateral premotor cortex
(Fincham et al., 2002).
Recall the two-string problem, posed at the outset of this chapter. The solution
to the two-string problem is shown in Figure 11.7. Many people find it extremely
difficult to arrive at the solution. Many never do, no matter how hard they try. People who find the problem insoluble often err at Step 2 of the problem-solving cycle,
after which they never recover. That is, by defining the problem as being one in
which they must be able to move toward one string while holding another, they impose on themselves a constraint that makes the problem virtually insoluble.
Ill-Structured Problems and the Role of Insight
The two-string problem is an example of an ill-structured problem. In fact, although
we occasionally may misrepresent well-structured problems, we are much more likely
to have difficulty representing ill-structured problems. Before we explain the nature
of ill-structured problems, try to solve a few more such problems. The following problems illustrate some of the difficulties created by the representation of ill-structured
problems (after Sternberg, 1986). Be sure to try all three problems before you read
about their solutions.
1. Haughty Harry has been asked to build a hat rack with a few given materials
(see Figure 11.8). Can you help him construct the hat rack?
1'
8'
"
'3
12
1" x 2" x 60"
1" x 2" x 43"
2"
13' 5"
Figure 11.8 Haughty Harry’s Problem.
Haughty Harry and several other job seekers were looking for work as carpenters. The site supervisor handed each ap00
00
00
00
00
00
00
plicant two sticks (a 1 2 60 stick and a 1 2 43 stick) and a 2 C-clamp. This situation is represented in
Figure 11.8. The opening of the clamp is wide enough so that both sticks can be inserted and held together securely
0
00
0
00
0
when the clamp is tightened. The supervisor ushered the job applicants into a room 12 3 13 5 with an 8 ceiling.
0
0
Mounted on the ceiling were two 1 1 beams, dividing the ceiling into thirds lengthwise. She told the applicants that
she would hire the first applicant who could build a hat rack capable of supporting her hard hat, using just the two
sticks and the C-clamp. She could hire only one person. So she recommended that the applicants not try to help one
another. What should Harry do?
Source: From Richard E. Mayer, “The Search for Insight: Grappling with Gestalt Psychology’s Unanswered Questions,” in The Nature of
Insight, edited by R. J. Sternberg and J. E. Davidson. Copyright © 1995 MIT Press. Reprinted with permission from MIT Press.
Types of Problems
455
2. A woman who lived in a small town married 20 different men in that same town.
All of them are still living, and she never divorced any of them. Yet she broke no
laws. How could she do this?
3. You have loose black and brown socks in a drawer, mixed in a ratio of five black
socks for every brown one. How many socks do you have to take out of that
drawer to be assured of having a pair of the same color?
Both the two-string problem and each of the three preceding problems are
ill-structured problems. There are no clear, readily available paths to solution. By
definition, ill-structured problems do not have well-defined problem spaces. Problem
solvers have difficulty constructing appropriate mental representations for modeling
these problems and their solutions. For such problems, much of the difficulty is in
constructing a plan for sequentially following a series of steps that inch ever closer
to their solution. In one study, both domain knowledge and justification skills
proved to be important for solving both ill- and well-structured problems. Justification skills are important because ill-structured problems can be represented in different ways and often have alternative solutions. Thus, problem solvers need to choose
and justify their selection of a particular representation and solution. Additional cognitive and affective factors, including attitudes toward science and regulation of cognition, are also important for the solving of ill-structured problems (Shin, Jonassen, &
McGee, 2003).
The preceding ill-structured problems are insight problems because you need to
see the problem in a novel way. In particular, you need to see it differently from
how you would probably see the problem at first, and differently from how you
would probably solve problems in general. That is, you must restructure your representation of the problem to solve it.
Insight is a distinctive and sometimes seemingly sudden understanding of a
problem or of a strategy that aids in solving the problem. Often, an insight involves
reconceptualizing a problem or a strategy in a totally new way. Insight often involves detecting and combining relevant old and new information to gain a novel
view of the problem or of its solution. Although insights may feel as though they are
sudden, they are often the result of much prior thought and hard work. Without this
work, the insight would never have occurred. Insight can be involved in solving wellstructured problems, but it more often is associated with the rocky and twisting path
to solution that characterizes ill-structured problems. For many years, psychologists
interested in problem solving have been trying to figure out the true nature of
insight.
What are the solutions to the insight problems we presented? Consider first the
hat-rack problem. Harry was unable to solve the problem before Sally quickly
whipped together a hat rack like the one shown in Figure 11.9. To solve the problem, Sally had to redefine her view of the materials in a way that allowed her to
conceive of a C-clamp as a hat holder.
The woman who was involved in multiple marriages is a minister. The critical
element for solving this problem is to recognize that the word married may be used to
describe the performance of the marriage ceremony. So the minister married the
20 men but did not herself become wedded to any of them. To solve this problem,
you had to redefine your interpretation of the term married. Others have suggested
yet additional possibilities. For example, perhaps the woman was an actress and only
married the men in her role as an actress. Or perhaps the woman’s multiple marriages were annulled so she never technically divorced any of the men.
456
CHAPTER 11 • Problem Solving and Creativity
Figure 11.9 Solution to Haughty Harry’s Problem.
Were you able to modify your definition of the materials available in a way that helped you solve the problem?
Source: From Intelligence Applied: Understanding and Increasing Your Intellectual Skills, by Robert J. Sternberg. Copyright © 1986 by
Harcourt Brace & Company. Reproduced by permission of the publisher.
As for the socks, you need only to take out three socks to be assured of having a
pair of the same color. The ratio information is irrelevant. Whether the first two
socks you withdraw match in color, the third certainly will match at least one of
the first two.
Early Gestaltist Views
Gestalt psychologists emphasized the importance of the whole as more than a collection
of parts. In regard to problem solving, Gestalt psychologists held that insight problems
require problem solvers to perceive the problem as a whole. Gestalt psychologist Max
Wertheimer (1945/1959) wrote about productive thinking, which involves insights
that go beyond the bounds of existing associations. He distinguished it from reproductive
thinking, which is based on existing associations involving what is already known.
According to Wertheimer, insightful (productive) thinking differs fundamentally from
reproductive thinking. In solving the insight problems given in this chapter, you had to
break away from your existing associations and see each problem in an entirely new light.
Productive thinking also can be applied to well-structured problems.
Wertheimer’s colleague Wolfgang Köhler (1927) studied insight in non-human
primates, particularly a caged chimpanzee named Sultan. In Köhler’s view, the ape’s
behavior illustrated insight (see Figure 11.10). To Köhler and other Gestaltists, insight is a special process. It involves thinking that differs from normal, linear information processing.
The Neo-Gestaltist View
Some researchers have found that insightful problem solving can be distinguished
from non-insightful problem solving in two ways (Metcalfe, 1986; Metcalfe &
Wiebe, 1987). For one thing, when given routine problems to solve, problem solvers
show remarkable accuracy in their ability to predict their own success in solving a
457
© SuperStock/SuperStock
Types of Problems
Figure 11.10
Insight Demonstrated by Chimpanzee.
Gestalt psychologist Wolfgang Köhler placed an ape in an enclosure with a few boxes. At the top of the cage, just out
of reach, was a bunch of bananas. After the ape unsuccessfully tried to jump and to stretch to reach the bananas, the
ape showed sudden insight: The ape realized that the boxes could be stacked on top of one another to make a
structure tall enough to reach the bunch of bananas.
problem prior to any attempt to solve it. In contrast, when given insight problems,
problem solvers show poor ability to predict their own success prior to trying to solve
the problems. Not only were successful problem solvers pessimistic about their ability
to solve insight problems, but unsuccessful problem solvers were often optimistic
about their ability to solve them.
In addition, the investigators used a clever methodology to observe the problemsolving process while participants were solving routine versus insight problems.
Routine problems included algebra problems, such as “(3x2 þ 2x þ 10)(3x) ¼ .” Insight problems included problems such as “A prisoner was attempting escape from
a tower. He found in his cell a rope which was half long enough to permit him to
reach the ground safely. He divided the rope in half and tied the two parts
together and escaped. How could he have done this?” At 15-second intervals, participants paused briefly to rate how close (“warm”) versus far (“cold”) they felt they
were to reaching a solution. Consider first what happened for routine problems,
such as algebra, or the Tower of Hanoi. Participants showed increases in their
feelings of warmth as they drew closer to reaching a correct solution. For insight
problems, however, participants showed no such increases. Figure 11.11 shows a
comparison of participants’ reported feelings of warmth for solving algebra problems
versus insight problems. In solving insight problems, participants showed no increasing feelings of warmth until moments before abruptly realizing the solution and
correctly solving the problem. Metcalfe’s findings certainly seem to support the
Gestaltist view that there is something special about insightful problem solving, as
distinct from non-insightful, routine problem solving. The specific nature and underlying mechanisms of insightful problem solving have yet to be addressed by this
research, however.
458
CHAPTER 11 • Problem Solving and Creativity
Insight
Algebra
30
30
20
20
10
10
0
30
0
30
–15 seconds
Frequency
20
20
10
10
0
0
30
30
–30 seconds
20
20
10
10
0
30
0
30
20
–45 seconds
20
10
10
0
30
0
30
20
20
–60 seconds
10
10
0
1
Figure 11.11
2
3
4
5
Warmth rating
6
7
1
2
3
4
5
Warmth rating
6
7
0
Feelings of Warmth in Insightful Problem Solving.
When Janet Metcalfe presented participants with routine problems and insight problems, they showed clear differences
in their feelings of warmth as they approached a solution to the problems. These frequency histograms (bar graphs in
which the area of each bar indicates the frequency for the given interval of time) show comparative feelings of warmth
during the four 15-second intervals prior to solving the problems. When solving insight problems, participants showed
no incremental increases in feelings of warmth, whereas when solving routine problems, participants showed distinct
incremental increases in feelings of warmth. (From Metcalfe & Wiebe, 1987, pp. 242, 245.)
Types of Problems
459
Insights into Insight
According to Smith (1995a), insights need not be sudden “a-ha” experiences. They
may and often do occur gradually and incrementally over time. When an insightful
solution is needed but not forthcoming, sleep may help produce a solution. In both
mathematical problem solving and solution of a task that requires understanding
underlying rules, sleep has been shown to increase the likelihood that an insight
will be produced (Stickgold & Walker, 2004; Wagner et al., 2004).
Unfortunately, insights—like many other aspects of human thinking—can be
both startlingly brilliant and dead wrong. How do we fall into mental traps that
lead us down false paths as we try to reach solutions?
Neuroscience and Insight
Neuroimaging studies suggest that the activity of our brain during rest can be divided up
into several different networks. Some of these networks are also active when we engage in
problem solving. This indicates that at least portions of the thought processes are the same
when we are problem solving and when we have thoughts during rest (Andreasen et al.,
1995; Christoff et al., 2004; Damoiseaux et al., 2006; Kounios et al., 2008). fMRI studies
show that activity in the right anterior superior-temporal gyrus increases when a person
experiences an insight. Furthermore, EEGs also record a burst of high-frequency activity
during insight (Jung-Beeman et al., 2004). In fact, before insights even become conscious,
activity in the right hemisphere can be observed. It is therefore generally assumed that
the right hemisphere has a special role in insight processes (Bowden et al., 2005).
The right hippocampus is critical in the formation of an insightful solution (Luo
& Niki, 2003). (As you may remember from Chapters 2 and 5, the hippocampus is
integral to the formation of new memories. Therefore it makes sense that the hippocampus would be involved in the formation of an insightful solution, as this process
involves combining relevant information stored in memory.) Another study demonstrated a spike of activity in the right anterior temporal area immediately before an
insight is formed. This area is active during all types of problem solving, as it involves making connections among distantly related items (Jung-Beeman et al.,
2004). This spike in activity, however, suggests a sudden understanding of relationships within a problem that leads to a solution.
Neural correlates measured even before an individual sees a problem can predict
if insight will occur. In one study, during the preparation prior to viewing of a problem, participants who would later generate an insightful solution had substantial
activation in the frontal lobes, whereas those who would not generate an insightful
solution had comparable activation in the occipital lobes (Kounios et al., 2006).
These findings suggest, first, that certain problem solvers are more likely to use insight than others. Second, they suggest that insight involves some advanced planning that occurs before a problem is even presented.
CONCEPT CHECK
1. What is the difference between well-structured and ill-structured problems?
2. When are two problems isomorphic?
3. What is insight?
4. According to Neo-Gestaltism, how can insightful problem solving and non-insightful
problem solving be distinguished?
5. Are insights always sudden?
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CHAPTER 11 • Problem Solving and Creativity
Obstacles and Aids to Problem Solving
Several factors can hinder or enhance problem solving. Among them are mental sets
as well as positive and negative transfer. Incubation plays a role in problem solving
as well. In the next sections, we will explore these factors in more detail.
Mental Sets, Entrenchment, and Fixation
One factor that can hinder problem solving is mental set—a frame of mind involving an existing model for representing a problem, a problem context, or a procedure
for problem solving. Another term for mental set is entrenchment. When problem
solvers have an entrenched mental set, they fixate on a strategy that normally works
well in solving many problems but that does not work well in solving this particular
problem. For example, in the two-string problem, you may fixate on strategies that
involve moving yourself toward the string, rather than moving the string toward
you. In the oft-marrying minister problem, you may fixate on the notion that to
marry someone is to become wedded to the person.
Mental sets also can influence the solution of rather routine problems. For example, consider “water-jar” problems (Luchins, 1942). In water-jar problems, participants are asked how to measure out a certain amount of water using three different
jars. Each jar holds a different amount of water. Investigating Cognitive Psychology:
Luchin’s Water-Jar Problems shows the problems used by Luchins. Look at the box
and try to solve the problems yourself before you read on.
Problems 7 through 11 can be solved in a much simpler way. One need use just
two of the jars. Problem 7 can be solved by A C. Problem 8 can be solved by A þ C,
and so on. People who are given Problems 1 through 6 to solve generally continue to
use the B A 2C formula in solving Problems 7 through 11. Consider, in Luchins’s
original experiment, those participants who solved the first set of problems. Between
64% and 83% of them went on to solve the last set of problems by using the less simple
strategy. What happened to the control participants who were not given the first set of
problems? Only 1% to 5% failed to apply the simpler solutions to the last set of problems. They had no established mental set that interfered with their seeing things in
a new and simpler way.
Another type of mental set involves fixation on a particular use (function) for
an object. Specifically, functional fixedness is the inability to realize that something
known to have a particular use may also be used for performing other functions
(German & Barrett, 2005; Rakoczy et al., 2009). Functional fixedness prevents us
from solving new problems by using old tools in novel ways. Becoming free of functional fixedness is what first allowed people to use a reshaped coat hanger to get into
a locked car. It is also what first allowed thieves to pick simple spring door locks
with a credit card.
Another type of mental set is considered an aspect of social cognition. Stereotypes
are beliefs that members of a social group tend more or less uniformly to have particular types of characteristics. We seem to learn many stereotypes during childhood. For
example, cross-cultural studies of children show their increasing knowledge about—
and use of—gender stereotypes across the childhood years (Neto, Williams, & Widner,
1991; Seguino, 2007). Stereotype awareness, for a variety of groups, develops in most
children between the ages of 6 and 10 (McKown & Weinstein, 2003). Stereotypes
often arise in the same way that other kinds of mental sets develop. We observe a
particular instance or set of instances of some pattern. We then may overgeneralize
Obstacles and Aids to Problem Solving
461
INVESTIGATING COGNITIVE PSYCHOLOGY
Luchins’s Water-Jar Problems
How do you measure out the right amount of water using Jars A, B, and C? You need to
use up to three jars to obtain the required amounts of water (measured in numbers of
cups) in the last column. Columns A, B, and C show the capacity of each jar. The first
problem, for example, requires you to get 20 cups of water from just two of the jars,
a 29-cup one (Jar A) and a 3-cup one (Jar B). Easy: Just fill Jar A, and then empty out
9 cups from this jar by taking out 3 cups three times, using Jar B. Problem 2 isn’t too
hard, either. Fill Jar B with 127 cups, then empty out 21 cups using Jar A, and then
empty out 6 cups, using Jar C twice. Now try the rest of the problems yourself. (After
Luchins, 1942.)
Jars Available for Use
Problem Number
A
B
C
Required Amount
(CUPS)
1
29
3
0
20
2
21
127
3
100
3
14
163
25
99
4
18
43
10
5
5
9
42
6
21
6
20
59
4
31
7
23
49
3
20
8
15
39
3
18
9
28
76
3
25
10
18
48
4
22
11
14
36
8
6
Luchins, Abraham S. (1942). Mechanization in Problem Solving: The Effect of Einstellung, Psychological
Monographs, 54(6), 248. © 1942, by Dr. Abraham S. Luchins. Reprinted by permission.
If you are like many people solving these problems, you will have found a formula
that works for all the remaining problems. You fill up Jar B. Then you pour out of it the
amount of water you can put into Jar A. Then you twice pour out of it the amount of
water you can put into Jar C. The formula, therefore, is B A 2C.
from those limited observations. We may assume that all future instances similarly will
demonstrate that pattern. For example, we may observe that some African Americans
can run very fast. If we then conclude that every African American is a fast runner, we
do have a stereotype because not every African American is a fast runner. Of course,
when the stereotypes are used to target particular scapegoats for societal mistreatment,
grave social consequences result for the targets of stereotypes. The targets are not the
only ones to suffer from stereotypes, however. Like other kinds of mental sets, stereotypes hinder the problem-solving abilities of the individuals who used them. These
people limit their thinking by using set stereotypes.
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CHAPTER 11 • Problem Solving and Creativity
Negative and Positive Transfer
Often, people have particular mental sets that prompt them to fixate on one aspect of a
problem or one strategy for problem solving to the exclusion of other possible relevant
ones. They are carrying knowledge and strategies for solving one kind of problem to a
different kind of problem. Transfer is any carryover of knowledge or skills from one
problem situation to another (Detterman & Sternberg, 1993; Gentile, 2000). Transfer
can be either negative or positive. Negative transfer occurs when solving an earlier problem makes it harder to solve a later one. Sometimes an early problem gets an individual
on a wrong track. For example, police may have difficulty solving a political crime because such a crime differs so much from the kinds of crime that they typically deal with.
Or when presented with a new tool, a person may operate it in a way similar to the way
in which he or she operated a tool with which he or she was already familiar (Besnard &
Cacitti, 2005). Positive transfer occurs when the solution of an earlier problem makes it
easier to solve a new problem. That is, sometimes the transfer of a mental set can be an
aid to problem solving. For instance, one may transfer early math skills, such as addition,
to advanced math problems of the kinds found in algebra or physics (Bassok & Holyoak,
1989; Chen & Daehler, 1989; see also Campbell & Robert, 2008).
Transfer of Analogies
Researchers designed some elegant studies of positive transfer involving analogies (Gick
& Holyoak, 1980, 1983). To appreciate their results, you need to become familiar with a
problem first used by Karl Duncker (1945), often called the “radiation problem.” It is
described in the Investigating Cognitive Psychology: Problems Involving Transfer.
INVESTIGATING COGNITIVE PSYCHOLOGY
Problems Involving Transfer
The Radiation Problem
Imagine that you are a doctor treating a patient with a malignant stomach tumor. You
cannot operate on the patient because of the severity of the cancer. But unless you destroy the tumor somehow, the patient will die. You could use high-intensity X-rays to destroy the tumor. Unfortunately, the intensity of X-rays needed to destroy the tumor also will
destroy healthy tissue through which the rays must pass. X-rays of lesser intensity will
spare the healthy tissue, but they will be insufficiently powerful to destroy the tumor.
What kind of procedure could you employ that will destroy the tumor without also destroying the healthy tissue surrounding the tumor?
Duncker had in mind a particular insightful solution as the optimal one for this problem. Figure 11.12
shows the solution pictorially.
Prior to presenting Duncker’s radiation problem, participants received another, easier problem. This particular
problem was called the “military problem” (Holyoak, 1984, p. 205).
The Military Problem
A general wishes to capture a fortress located in the center of a country. There are many
roads radiating outward from the fortress. All have been mined. Although small groups
of men can pass over the roads safely, any large force will detonate the mines. A fullscale direct attack is therefore impossible. What should the general do?
Think about this: What are the commonalities between the two problems, and what is an elemental
strategy that can be derived by comparing the two problems?
Obstacles and Aids to Problem Solving
Table 11.2
463
Correspondence between the Radiation and the Military Problems
What are the commonalities between the two problems, and what is an elemental strategy
that can be derived by comparing the two problems? (After Gick & Holyoak, 1983.)
Military Problem
Initial State Goal: Use army to capture fortress
Resources: Sufficiently large army
Constraint: Unable to send entire army along one road
Solution Plan: Send small groups along multiple roads simultaneously
Outcome: Fortress captured by army
Radiation Problem
Initial State Goal: Use rays to destroy tumor
Resources: Sufficiently powerful rays
Constraint: Unable to administer high-intensity rays from one direction only
Solution Plan: Administer low-intensity rays from multiple directions simultaneously
Outcome: Tumor destroyed by rays
Convergence Schema
Initial State Goal: Use force to overcome a central target
Resources: Sufficiently great force
Constraint: Unable to apply full force along one path alone
Solution Plan: Apply weak forces along multiple paths simultaneously
Outcome: Central target overcome by force
M. L. Gick and K. J. Holyoak (1983), “Schema Induction and Analogical Transfer,” Cognitive Psychology,
Vol. 15, pp. 1–38. Reprinted by permission of Elsevier.
The correspondence between the radiation and military problems is actually
quite close, although not perfect (see Table 11.2). The question is whether producing a group-convergence solution to the military problem helped participants in
solving the radiation problem. Consider participants who received the military problem with the convergence solution and then were given a hint to apply it in some
way to the radiation problem. About 75% of the participants reached the correct
solution to the radiation problem. This figure compared with less than 10% of the
participants who did not receive the military story first but instead received no prior
story or only an irrelevant one.
In another experiment, participants were not given the convergence solution to
the military problem. They had to figure it out for themselves. About 50% of the
participants generated the convergence solution to the military problem. Of these,
41% went on to generate a parallel solution to the radiation problem. That is, positive transfer was weaker when participants produced the original solution themselves
than when the solution to the first problem was given to them (41%, as compared
with 75%).
The investigators found that the usefulness of the military problem as an
analog to the radiation problem depended on the induced mental set with which
the problem solver approached the problems. Consider what happened when participants were asked to memorize the military story under the guise that it was a
story-recall experiment and then were given the radiation problem to solve.
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CHAPTER 11 • Problem Solving and Creativity
Figure 11.12
The Radiation Problem.
The solution to the X-ray problem involving the treatment of a patient with a tumor involves
dispersion. The idea is to direct weak X-radiation toward the tumor from a number of different
points outside the body. No single set of rays would be strong enough to destroy either the
healthy tissue or the tumor. However, the rays would be aimed so that they all converged at
one spot within the body—the spot that houses the tumor. This solution actually is used today
in some X-ray treatments, except that a rotating source of X-rays is used for dispersing rays.
Source: From In Search of the Human Mind, by Robert J. Sternberg. Copyright © 1995 by Harcourt Brace &
Company. Reproduced by permission of the publisher.
Only 30% of participants produced the convergence solution to the radiation
problem. The investigators also found that positive transfer improved if two,
rather than just one, analogous problems were given in advance of the radiation
problem.
Researchers have expanded these findings to encompass problems other than
the radiation problem. They found that when the domains or the contexts for the
two problems were more similar, participants were more likely to see and apply the
analogy (see Holyoak, 1990). Similar patterns of data were found with various types
of problems involving electricity and mathematical insight (Davidson & Sternberg,
1984; Gentner & Gentner, 1983; Novick & Holyoak, 1991).
Perhaps the most crucial aspect of these studies is that people have trouble noticing analogies unless they explicitly are told to look for them. Consider studies involving physics problems. Positive transfer from solved examples to unsolved
problems was more likely among students who specifically tried to understand why
particular examples were solved as they were, as compared with students who sought
only to understand how particular problems were solved as they were (Chi et al.,
1989). Based on these findings, we generally need to be looking for analogies to
find them. We often will not find them unless we explicitly seek them.
Obstacles and Aids to Problem Solving
465
People sometimes do not recognize the surface similarities of problems (Bassok,
2003). Other times they are fooled by surface similarities into believing two different
kinds of problems are the same (Bassok, Wu, & Olseth, 1995; Gentner, 2000).
Sometimes even experienced problem solvers are led astray. They believe that similar surface structures indicate comparable deep structures. For example, problem solvers may use the verbal content rather than the mathematical operations required in
a mathematical problem to classify the problem as being of a certain kind (Blessing
& Ross, 1996).
Intentional Transfer: Searching for Analogies
In order to find analogies between two problems, one must perceive the relationships
between them (Gentner, 1983, 2000). The actual content attributes of the problems
are irrelevant. In other words, what matters in analogies is not the similarity of the
content but how closely their structural systems of relationships match. Because we
are accustomed to considering the importance of the content, we find it difficult to
push the content to the background. It also is difficult to bring form (structural relationships) to the foreground. For example, the differing content makes the analogy
between the military problem and the radiation problem hard to recognize and impedes positive transfer from one problem to the other.
The opposite phenomenon is transparency, in which people see analogies
where they do not exist because of similarity of content. In making analogies, we
need to be sure we are focusing on the relationships between the two terms being
compared, not just their surface content attributes. For example, in studying for final
exams in two psychology courses, you may need different strategies when studying
for a closed-book essay exam than for an open-book, multiple-choice exam. Transparency of content may lead to negative transfer between non-isomorphic problems
if care is not taken to avoid such transfer.
Incubation
For solving many problems, the chief obstacle is not the need to find a suitable strategy for positive transfer. Rather, it is to avoid obstacles resulting from negative transfer. Incubation—putting the problem aside for a while without consciously thinking
about it—offers one way in which to minimize negative transfer. It involves taking a
pause from the stages of problem solving. For example, suppose you find that you are
unable to solve a problem. None of the strategies you can think of seem to work.
Try setting the problem aside for a while to let it incubate. During incubation, you
must not consciously think about the problem. You do, however, allow for the possibility that the problem will be processed subconsciously. Some investigators of
problem solving have even asserted that incubation is an essential stage of the
problem-solving process (e.g., Cattell, 1971; von Helmholtz, 1896). Others have
failed to find experimental support for the phenomenon of incubation (e.g., Baron,
1988).
A recent meta–analysis (Sio & Ormerod, 2009) found that, as most of the time
in psychological research, the state of affairs is complex. When people have more
time to prepare for the solving of a problem, incubation periods are usually more
fruitful. Likewise, being occupied with tasks that are highly cognitively demanding
is detrimental to the effect of an incubation period. The effect of incubation furthermore depends on the kind of task, with performance on divergent-thinking tasks
(where something has to be produced) benefiting more than performance on
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linguistic tasks, for example. Incubation seems to help because people continue to
process, below consciousness, information about a problem on which they are incubating at the same time that they are attending to other matters.
Neuroscience and Planning during Problem Solving
One way to invest enough initial time in a problem is through the formation of a
plan of action for the problem. As previously discussed, planning saves time and improves performance. In one study employing variants of the Tower of Hanoi, when
participants became more familiar with this type of problem, they showed increased
planning times, which resulted in a decrease in the total number of moves (Gunzelmann & Anderson, 2003). These results highlight the importance of planning for
efficient problem solving.
Recall from Chapter 2 that the frontal lobes are involved in high-level cognitive
processes. It is therefore not surprising that the frontal lobes and in particular the
prefrontal cortex are essential for planning for complex problem-solving tasks
(Unterrainer & Owen, 2006). A number of studies using a variety of neuropsychological methods, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), have highlighted activation in this region of the
brain during problem solving (Unterrainer & Owen, 2006). Additionally, both the
left and right prefrontal areas are active during the planning stage of complex problem solving (Newman et al., 2003). When a participant gives an incorrect response
in a problem-solving task and therefore has to continue working on the problem, he
or she reveals greater bilateral prefrontal activation than is associated with a correct
response (Unterrainer et al., 2004). This finding would suggest that if the initial
plan fails, problem solvers must devise a new plan, thereby activating the prefrontal
cortex.
Further evidence for the importance of the prefrontal regions in problem solving
can be seen in cases of traumatic brain injury. Both problem solving and planning
ability decline following traumatic brain injury (Catroppa & Anderson, 2006). In
fact, with regard to the problem-solving ability of patients with traumatic brain injury, those patients who performed best were ones with limited damage to the left
prefrontal regions (Cazalis et al., 2006).
In the Tower of London task, other areas, including the premotor cortex and
the parietal regions, were also activated (Newman et al., 2003; Unterrainer &
Owen, 2006). This additional activation is likely the result of the need for attention
and planning for movement. In addition to the prefrontal regions, the same areas
active during use of visual spatial working memory are also active during solution
of the Tower of London (Baker et al., 1996).
Intelligence and Complex Problem Solving
Cognitive approaches for studying information processing can be applied to more
complex problem-solving tasks, such as analogies, series problems (e.g., completing
a numerical or figural series), and syllogisms (Sternberg, 1977, 1983, 1984;
see Chapter 12). The idea is to take the kinds of tasks used on conventional intelligence tests and to isolate components of intelligence. Components are the mental
processes used in performing these tasks, such as translating a sensory input into a
mental representation, transforming one conceptual representation into another, or
translating a conceptual representation into a motor output (Sternberg, 1982). Many
Obstacles and Aids to Problem Solving
467
investigators have elaborated on and expanded this basic approach (Lohman, 2000,
2005; Wenke, Frensch, & Funke, 2005). For example, in processing the analogy
DOG : BOXER :: CAT : SIAMESE, one needs to encode the terms of the problem,
infer the relation between DOG and BOXER, and then apply that relation from
CAT to SIAMESE (see also Figure 11.13).
There are significant correlations between speed in executing these processes
and performance on other, traditional intelligence tests. However, a more intriguing discovery is that participants who score higher on traditional intelligence tests
take longer to encode the terms of the problem than do less intelligent participants. But they make up for the extra time by taking less time to perform the remaining components of the task. In general, more intelligent participants take
longer during global planning—encoding the problem and formulating a general
strategy for attacking the problem (or set of problems). But they take less time for
local planning—forming and implementing strategies for the details of the task
(Sternberg, 1981).
The advantage of spending more time on global planning is the increased likelihood that the overall strategy will be correct. Thus, when taking more time is advantageous, brighter people may take longer to do something than will less bright
people. For example, the brighter person might spend more time researching and
planning for writing a term paper but less time in the actual writing of it. This
same differential in time allocation has been shown in other tasks as well. An example would be in solving physics problems (Larkin et al., 1980; see Sternberg, 1979,
1985a). That is, more intelligent people seem to spend more time planning for and
encoding the problems they face. But they spend less time engaging in the other
components of task performance. This may relate to the previously mentioned metacognitive attribute many include in their notions of intelligence.
Encoding
Preparation
A
B
Inference
D
C
Response
Application
Mapping
Figure 11.13
Mental Processes in Solving Analogies.
In the solution of an analogy problem, the problem solver must first encode the problem A is to B as C is to D. The
problem solver then must infer the relationship between A and B. Next, the problem solver must map the relationship
between A and B to the relationship between C and each of the possible solutions to the analogy. Finally, the problem
solver must apply the relationship to choose which of the possible solutions is the correct solution to the problem.
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CHAPTER 11 • Problem Solving and Creativity
Researchers have also studied information processing of people engaged in complex problem-solving situations, such as playing chess and performing logical derivations (Bilalic et al., 2008; Kiesel et al., 2009; Simon, 1976). For example, a simple,
brief task might require the participants first to view an arithmetic or geometric series. Then they must figure out the rule underlying the progression. And finally they
must guess what numeral or geometric figure might come next. More complex tasks
might include some of the tasks mentioned before, like the water-jar problem.
CONCEPT CHECK
1. How can mental sets impair our problem-solving ability?
2. What is negative transfer?
3. Are analogies always useful for problem solving?
4. What is the role of incubation in problem solving?
Expertise: Knowledge and Problem Solving
Even people who do not have expertise in cognitive psychology recognize that
knowledge, particularly expert knowledge, greatly enhances problem solving. Expertise is superior skills or achievement reflecting a well-developed and well-organized
knowledge base. What interests cognitive psychologists is the reason that expertise
enhances problem solving. Why can experts solve problems in their field more successfully than can novices? Do experts know more problem-solving algorithms, heuristics, and other strategies? Do experts know better strategies? Or do they just use
these strategies more often? What do experts know that makes the problem-solving
process more effective for them than for novices in a field? Is it all talent or just
acquired skill?
Organization of Knowledge
Do you think one can distinguish beers by their flavor? In one study, beer experts
and beer novices experienced tasting a series of beers (Valentin et al., 2007). Both
groups could sort the beers equally well. However, the beer experts performed
better on subsequent recognition tasks (Valentin et al., 2007). These findings
suggest that there was no difference in perceptual abilities between the experts
and the novices, but there was a difference in memory between these two groups
(Valentin et al., 2007). The researchers concluded that the beer experts had a
superior framework for encoding and retrieving the new beer information
(Valentin et al., 2007).
Knowledge can interact with understanding in problem solving as well (Whitten &
Graesser, 2003). Consider a study investigating how knowledge interacts with coherence of a text. Investigators presented children with biology texts (McNamara
et al., 1996). Half the children in the study had high levels of domain knowledge
about biology and half had low levels. In addition, half the texts were highly coherent, meaning that they made clear how the various concepts in the text related
to each other. The other half of the texts were of low coherence, meaning that
they were more difficult to read because the ideas did not flow smoothly. Readers
then had to do a variety of problem-solving tasks based on what they had read.
Expertise: Knowledge and Problem Solving
469
As the authors predicted, participants with low domain knowledge performed better when the texts were highly coherent. This finding suggests that, in general,
learners do better when they are presented new material in a coherent way.
Surprisingly, however, the high-knowledge group performed better when the
texts were of low rather than high coherence. The authors of the study suggested
that high-knowledge readers may have been, essentially, on automatic pilot when
reading the high-coherence texts, not paying much attention because they thought
they knew what was in the texts. The low-coherence texts forced them to pay attention. These results point out the importance of attentional processes when people
solve problems. This is particularly relevant in domains in which they are expert
and in which they therefore may not feel they have to pay attention.
Elaboration of Knowledge
Do you remember the study with chess experts and novices described at the very
beginning of this chapter in Believe It or Not? What differentiated the experts from
the novices was the amount, organization, and use of knowledge. There were two
tasks in the chess study: One involved a random array of pieces and the other a
meaningful arrangement of pieces (Figure 11.14). For both chess tasks, the experts
used heuristics for storing and retrieving information about the positions of the
pieces on the chess-board. The novices, to the contrary, had not stored significant
knowledge about positions. The key difference, therefore, was that chess experts had
stored and organized in memory tens of thousands of particular board positions.
When they saw sensible board positions, they could use the knowledge they had in
memory to help them. They were able to remember the various board positions as
integrated, organized chunks of information. As you may recall from Chapter 5, the
ability to chunk information into meaningful units allows for superior memory and
capacity. For random scatterings of pieces on the board, however, the knowledge of
the experts was of no use. The experts had no advantage over the novices. Like the
novices, they had to try to memorize the distinctive interrelations among many discrete pieces and positions. This memorization requires the storage of many more
items, thus taxing one’s memory abilities.
Retrieval processes involving recognition of board arrangements are instrumental in grand master–level chess players’ success when compared with novices’ play
(Gobet & Simon, 1996a, 1996b, 1996c). Even when grand masters are timeconstrained so that look-ahead processes are curtailed, their constrained performance
does not differ substantially from their unconstrained playing. Thus, an organized
knowledge system is relatively more important to experts’ performance in chess
than even the processes involved in predicting future moves.
Other studies have examined experts in other domains like radiology (Lesgold et al.,
1988), physics (Larkin et al., 1980), and meditation (Brefczynski-Lewis et al., 2007).
These studies revealed the same thing again and again. What differentiated experts
from novices were their schemas for solving problems within their own domains of expertise (Glaser & Chi, 1988). The schemas of experts involve large, highly interconnected
units of knowledge. They are organized according to underlying structural similarities
among knowledge units. In contrast, the schemas of novices involve relatively small
and disconnected units of knowledge. They are organized according to superficial similarities (Bryson et al., 1991).
Experts and novices also differ in how they classify various problems, describe
the essential nature of problems, and how they determine and describe solutions (Chi,
Glaser, & Rees, 1982; Larkin et al., 1980). One study exploring problem-solving
strategies in both expert and novice mathematicians noted a difference in the use of
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CHAPTER 11 • Problem Solving and Creativity
M = Master B = Beginner
Black
Actual board positions
24
Number of correct pieces
20
16
M
12
8
4
B
1
White
(a)
Black
2
3
4
Trials
5
6
7
(b)
Random board positions
Number of correct pieces
24
B
20
16
M
12
8
4
1
2
3
4
5
6
7
Trials
White
(c)
Figure 11.14
(d)
Experts Versus Novices in Playing Chess.
When experts and novices were asked to recall realistic patterns of chess pieces, as in panel (a), experts demonstrated
much better performance, as shown in panel (b). However, when experts and novices were asked to recall random arrangements of chess pieces, as shown in panel (c), experts performed no better than novices, as shown in panel (d).
Source: From William G. Chase and Herbert A. Simon (1973), Copyright “The Mind’s Eye in Chess,” in Visual Information Processing, edited
by William G. Chase. Reprinted by permission of Elsevier.
Expertise: Knowledge and Problem Solving
471
visual depictions. The researchers observed that novice problem solvers use a visual representation to solve problems that have an obvious spatial component, such as geometry
problems. However, expert problem solvers used visual representations to solve a wide
range of mathematical problems (Stylianou & Silver, 2004), whether or not they had an
obvious spatial component. The ability to apply a visual representation to a variety of problems allows greater flexibility and an increased likelihood that a solution will be found.
An interesting study looked at the role of knowledge in understanding and interpreting a news broadcast regarding a baseball game (Hambrick & Engle, 2002). A
total of 181 adults having a wide range of knowledge about baseball listened to radio
broadcasts recorded by a professional baseball announcer. The announcements
sounded like a real game. After each broadcast, memory for changes in the status
of the game were measured. For example, participants would be asked questions
about which bases were occupied after each player’s turn at bat and about the numbers of outs and of runs scored during the inning. Baseball knowledge accounted for
more than half the reliable variation in participants’ performance. Working memory
capacity also mattered, but not nearly so much as knowledge. Thus, people can remember things better and solve problems with what they remember better if they
have a solid knowledge base with which to work.
Reflections on Problem Solving
Another difference between experts and novices can be observed by asking problem solvers to report aloud what they are thinking as they are attempting to solve
various problems (Bilalic, 2008; Dew et al., 2009). Statements made by problem
solvers are called verbal protocols. An interesting effect of verbal protocols is that
they can lead to increased problem-solving ability. In one study, when participants
spoke aloud or wrote about their problem-solving strategy in a way that centered
on the objects of the problem, an improvement in quality of solutions was observed (Steif et al., 2006). In another study, problem-solving ability was enhanced
when participants wrote a description of their problem-solving strategy as compared with when they spoke about their strategy (Pugalee, 2004). Thus, it seems
that, for novice problem solvers, communicating problem-solving strategies improves performance.
Another difference between expert and novice problem solvers is the time spent
on various aspects of problems, and the relationship between problem-solving strategies and the solutions reached. Experts appear to spend proportionately more time
determining how to represent a problem than do novices (Lesgold, 1988; Lesgold
et al., 1988), but they spend much less time than do novices actually implementing
the strategy for solution.
The differences between experts and novices in their expenditure of time can be
viewed in terms of the focus and direction of their problem solving. Experts seem to
spend relatively more time than do novices figuring out how to match the given information in the problem with their existing schemas. In other words, they try to
compare what they know about the problem with how the information they have
matches what they already know, based on their expertise. Once experts find a correct match, they quickly can retrieve and implement a problem strategy. Thus, experts seem able to work forward from the given information (“What do I know?”) to
find the unknown information (“What do I need to find out?”). They implement
the correct sequence of steps, based on the strategies they have retrieved from their
schemas in long-term memory (Chi et al., 1982).
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CHAPTER 11 • Problem Solving and Creativity
Image not available due to copyright restrictions
Expertise: Knowledge and Problem Solving
473
Consider the ways an expert doctor and a novice medical student might handle
a patient with a set of symptoms. The novice is not sure what to make of the symptoms. He somewhat haphazardly orders a long and expensive series of medical tests.
He is hoping that with a more nearly complete set of symptomatic information, he
may be able to make a correct diagnosis. The more experienced doctor, however, is
more likely to immediately recognize the symptoms as fitting into a diagnostic pattern or one of a small number of patterns. This doctor orders only a small number of
highly targeted tests. She is able to choose the correct diagnosis from among the
limited number of possibilities. She then moves on to treat the diagnosed illness.
In contrast, novices seem to spend relatively little time trying to represent the
problem. Instead, they choose to work backward from the unknown information to
the given information. That is, they go from asking what they need to find out to
asking what information is offered and what strategies do they know that can help
them find the missing information. Often, novices use means–ends analysis (see
Hunt, 1994). Thus, novices often consider more possible strategies than experts consider (see Holyoak, 1990). For experts, means–ends analysis of problems serves only
as a backup strategy. They turn to it only if they are unable to retrieve an appropriate strategy, based on their existing schemas.
Thus, experts have not only more knowledge but also better-organized knowledge. They use their knowledge more effectively. Furthermore, the schemas of experts involve not only greater declarative knowledge about a problem domain.
They also involve more procedural knowledge about strategies relevant to that
domain. Perhaps because of their better grasp of the strategies required, experts
more accurately predict the difficulty of solving problems than do novices. Experts
also monitor their problem-solving strategies more carefully than do novices
(Schoenfeld, 1981).
Automatic Expert Processes
Through practice in applying strategies, experts may automatize various operations.
They can retrieve and execute these operations easily while working forward (see
VanLehn, 1989). They use two important processes: One is schematization, which
involves developing rich, highly organized schemas; the other is automatization,
which involves consolidating sequences of steps into unified routines that require
little or no conscious control. Through these two processes, experts may shift the
burden of solving problems from limited-capacity working memory to infinitecapacity long-term memory. They thereby become increasingly efficient and accurate
in solving problems. The freeing of their working-memory capacity may better enable them to monitor their progress and their accuracy during problem solving. Novices, in contrast, must use their working memory for trying to hold multiple features
of a problem and various possible alternative strategies. This effort may leave novices
with less working memory available for monitoring their accuracy and their progress
toward solving the problem.
Automaticity can be seen in mathematics, for example, where low-level skills,
such as counting and adding, become automatic (Tronsky, 2005). These skills reduce the working-memory load and allow for higher-level mathematical procedures
to be complete.
However, the automaticity of experts actually may hinder problem solving by
making them less flexible. This can occur when experts are tackling problems that
differ structurally from the problems they normally encounter (Frensch & Sternberg,
1989). Initially, novices may perform better than experts when the problems appear
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CHAPTER 11 • Problem Solving and Creativity
structurally different from the norm. Eventually, however, the performance of experts generally catches up to and surpasses that of novices (Frensch & Sternberg,
1989; Lesgold, 1988). Perhaps this difference results from the experts’ richly developed schemas and their enhanced self-monitoring skills. The highest level experts,
however, are less vulnerable to falling prey to their own expertise (Bilalic et al.,
2008). They have the wisdom to realize their own susceptibility to becoming entrenched and take this susceptibility into account.
Table 11.3 summarizes the various characteristics of expert problem solving.
Innate Talent and Acquired Skill
Although a richly elaborated knowledge base is crucial to expertise in a domain,
there remain differences in performance that are not explainable in terms of knowledge level alone. There is considerable debate as to whether differences between novices and experts and among different experts themselves are due either to innate
talent or to the quantity and quality of practice in a domain. Many espouse the
“practice makes perfect” point of view (see for example Ericsson, 2003). The practice should be deliberate, or focused. It should emphasize acquisition of new skills
and applications rather than mindless repetition of what the developing expert already knows how to do.
However, some take an alternative approach. This approach acknowledges the
importance of practice in building a knowledge and skill base. It also underscores the
importance of something like talent. Indeed, the interaction between innate abilities
modified by experience is widely accepted in the domain of language acquisition as
well as other domains. Certainly, some skill domains are heavily dependent on nurture. For example, wisdom is partly knowledge based. The knowledge one uses to
make wise judgments is necessarily a result of experience (Baltes & Smith, 1990).
Experts in some domains perform at superior levels by virtue of prediction skills.
For example, expert typists move their fingers toward keys corresponding to the letters they will need to type more quickly than do novice typists (Norman & Rumelhart, 1983). Indeed, the single best predictor of typing speed is how far ahead in the
text a typist looks when typing (Ericsson, 2003). The farther ahead he or she looks,
the better the typist is able to have fingers in position as needed. When typists are
not allowed to look ahead in their typing, the advantage of expert typists is largely
eliminated (Salthouse, 1984). Expert sign-language users show variations in sign
production in preparation for the next sign (Yang & Sarkar, 2006). Rather than produce one sign in isolation, these signers are looking ahead. Looking ahead allows
experts to produce signs more quickly than do novices. Expert musicians, too, are
better able to sight-read than novices by virtue of their looking farther ahead in
the music so they can anticipate what notes will be coming up (Sloboda, 1984).
Even in sports, such as tennis, experts are superior to novices in part by virtue of
their being able to predict the trajectory of an approaching ball more rapidly and
accurately than novices (Abernethy, 1991).
Another characteristic of experts is that they tend to use a more systematic approach to difficult problems within their domain of expertise than do novices. For example, one study compared strategies used by problem solvers in a simulated biology
laboratory (Vollmeyer, Burns, & Holyoak, 1996). The investigators found that better
problem solvers were more systematic in their approach to the lab than were poorer
problem solvers. For example, in seeking an explanation of a biological phenomenon,
they were more likely to hold one variable constant while varying other variables.
Expertise: Knowledge and Problem Solving
Table 11.3
475
What Characterizes Expertise?
Experts
Novices
Have large, rich schemas containing a
great deal of declarative knowledge about
domain
Have relatively impoverished schemas containing relatively less declarative knowledge
about domain
Schemas contain a great deal of procedural
knowledge about problem-solving strategies
relevant to a given domain
Schemas contain relatively little procedural
knowledge about problem strategies relevant
to the given domain
Organization
Have well-organized, highly interconnected
units of knowledge in schemas
Have poorly organized, loosely interconnected, scattered units of knowledge
Use of time
Spend proportionately more time determining how to represent a problem than in
searching for and executing a problem
strategy
Spend proportionately more time searching for
and executing a problem strategy than in determining how to represent a problem
Representation of
problems
Develop sophisticated representation of
problems based on structural similarities
among problems
Develop relatively poor and naive representation of problems based on superficial
similarities among problems
Work direction
Work forward from given information to
implement strategies for finding unknown
information
Work backward from focusing on the unknown to finding problem strategies that make
use of given information
Strategy
Generally choose a strategy based on
elaborate schema of problem strategies; use
means–ends analysis only as a backup
strategy for handling unusual, atypical
problems
Frequently use means–ends analysis as a
strategy for handling most problems; sometimes choose a strategy based on knowledge
of problem strategies
Automatization
Have automatized many sequences of steps
within problem strategies
Show little or no automatization of any sequences of steps within problem strategies
Efficiency
Show highly efficient problem solving; when
time constraints are imposed, solve problems more quickly than novices
Show relatively inefficient problem solving;
solve problems less quickly than experts
Prediction of difficulty
Accurately predict the difficulty of solving
particular problems
Do not accurately predict the difficulty of
solving particular problems
Monitoring
Carefully monitor own problem-solving
strategies and processes
Show poor monitoring of own problemsolving strategies and processes
Accuracy of solution
Show high accuracy in reaching appropriate solutions
Show much less accuracy than experts in
reaching appropriate solutions
Confronting unusual
problems
When confronting highly unusual problems
with atypical structural features, take relatively more time than novices both to represent the problem and to retrieve appropriate
problem strategies
When confronting highly unusual problems
with atypical structural features, novices take
relatively less time than experts both to represent the problem and to retrieve problem
strategies
Handling contradictory
information
When provided with new information that
contradicts initial problem representation,
show flexibility in adapting to a more
appropriate strategy
Show less ability to adapt to new information
that contradicts initial problem representation
and strategy
Schemas
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CHAPTER 11 • Problem Solving and Creativity
Many scientists in the field of expertise prefer to minimize the contributions of
talent to expertise by locking talent in the trunk of “folk” psychology (Sternberg,
1996a). This tendency is not surprising, given two factors. The first is the widespread
use of the term talent outside the scientific community. The second is the lack of an
adequate, testable definition of talent.
Genetic heritage seems to make some difference in the acquisition of at least
some kinds of expertise. Studies of the heritability of reading disabilities, for example, seem to point to a strong role for genetic factors in people with a reading disability (see Haworth et al., 2009; Platko et al., 2008). Furthermore, differences in
the phonological awareness required for reading ability could be a factor in reading
for which individual differences are at least partially genetic (Wagner & Stanovich,
1996). In general, even if the role of practice is found to account for much of the
expertise shown in a given domain, the contributions of genetic factors to the remaining portion of expertise could make some difference in a world of intense
competition.
Artificial Intelligence and Expertise
Computer programs have been developed both to simulate human intelligence and
to exceed it. In many ways, computer programs have been created with the intention of solving problems faster and more efficiently than humans. But can a computer be intelligent at all? How can it be tested? Where are systems used that
mimic human expertise, and are they successful? These are some of the questions
we explore in the next sections.
Can a Computer Be Intelligent?
Much of early information-processing research centered on work based on computer
simulations of human intelligence as well as computer systems that use optimal
methods to solve tasks. Programs of both kinds can be classified as examples of artificial intelligence (AI), or intelligence in symbol-processing systems such as computers
(see Schank & Towle, 2000). Computers cannot actually think; they must be programmed to behave as though they are thinking. That is, they must be programmed
to simulate cognitive processes. In this way, they give us insight into the details of
how people process information cognitively. Essentially, computers are just pieces of
hardware—physical components of equipment—that respond to instructions. Other
kinds of hardware (other pieces of equipment) also respond to instructions. For example, if you can figure out how to give the instructions, a DVR (digital video recorder) will respond to your instructions and will do what you tell it to do.
What makes computers so interesting to researchers is that they can be given
highly complex instructions (computer programs, more commonly known as software). Programs tell the computer how to respond to new information.
Before we consider any intelligent programs, we need to consider seriously the
issue of what, if anything, would lead us to describe a computer program as being
“intelligent.”
The Turing Test
Probably the first serious attempt to deal with the issue of whether a computer program can be intelligent was made by Alan Turing (1963). The basic idea behind the
Turing Test is whether an observer can distinguish the performance of a computer
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Expertise: Knowledge and Problem Solving
A common trait among experts in various skills is that they put in tremendous numbers of hours of deliberate practice to
perfect their skills.
from that of a human. The test is conducted with a computer, a human respondent,
and an interrogator. The interrogator has two different “conversations” with an interactive computer program. The goal of the interrogator is to figure out which of
two parties is a person communicating through the computer, and which is the computer itself. The interrogator can ask the two parties any questions at all. However,
the computer will try to fool the interrogator into believing that it is human. The
human, in contrast, will be trying to show the interrogator that he or she truly is
human. The computer passes the Turing Test if an interrogator is unable to distinguish the computer from the human.
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Often, what researchers are interested in when assessing the “intelligence” of
computers is not their reaction time, which is often much faster than that of humans. They are interested instead in patterns of reaction time, that is, whether the
problems that take the computer relatively longer to solve also take human participants relatively longer.
Sometimes, the goal of a computer model is not to match human performance but
to exceed it. In this case, maximum AI, rather than simulation of human intelligence,
is the goal of the program. The criterion of whether computer performance matches
that of humans is no longer relevant. Instead, the criterion of interest is that of how
well the computer can perform the task assigned to it. Computer programs that play
chess, for example, typically play in a way that emphasizes “brute force,” or the consideration of all possible moves without respect to their quality. The programs evaluate
extremely large numbers of possible moves. Many of them are moves humans would
never even consider evaluating (Berliner, 1969; Bernstein, 1958). Using brute force,
the IBM program, “Deep Blue,” beat world champion Gary Kasparov in a 1997 chess
match. The same brute-force method is used in programs that play checkers (Samuel,
1963). These programs generally are evaluated in terms of how well they can beat each
other or, even more importantly, human contenders playing against them.
Expert Systems
Expert systems are computer programs that can perform the way an expert does in a
fairly specific domain. They are not developed to model human intelligence, but to
simulate performance in just one domain, often a narrow one. They are mostly based
on rules that are followed and worked down like a decision tree.
Several programs were developed to diagnose various kinds of medical disorders,
like cancer. Such programs are obviously of enormous potential significance, given
the very high costs (financial and personal) of incorrect diagnoses. Not only are
there expert systems for use by doctors, but there are even medical expert systems
on-line for use by consumers who would like an analysis of their symptoms.
Expert systems are used in other areas as well, for example in banks. The processing of small mortgages is relatively expensive for banks because a lot of factors need
to be considered. If the data are fed into a computer, however, an expert system
makes a decision about the mortgage application based on rules it was programmed
with. There is one expert system with which you may have made some experiences
yourself: Microsoft Windows offers troubleshooting through the “help section” where
you can enter into a dialogue with the system in order to figure out a solution to
your particular problem. Reflecting on your own experiences with computerized
troubleshooting processes, you can see the strengths but also weaknesses of expert
systems.
One has to be cautious in the use of expert systems. Because patients generally do
not have the knowledge their doctors have, their use of expert systems, such as on-line
ones, may lead them to incorrect conclusions about what illnesses they suffer. In medicine, patient use of the Internet is no substitute for the judgment of a medical doctor.
The application of expertise to problem solving generally involves converging
on a single correct solution from a broad range of possibilities. A complementary
asset to expertise in problem solving involves creativity. Here, an individual extends
the range of possibilities to consider never-before-explored options. In fact, many
problems can be solved only by inventing or discovering strategies to answer a complex question. We will discuss the role of creativity in problem solving in the next
section of this chapter.
Creativity
479
CONCEPT CHECK
1. How do the schemas of experts and novices differ?
2. Why does automatization help experts solve problems efficiently?
3. How does talent contribute to expertise?
4. What are expert systems?
Creativity
How can we possibly define creativity as a single construct that unifies the work of
Leonardo da Vinci and Marie Curie, of Vincent Van Gogh and Isaac Newton, and
of Toni Morrison and Albert Einstein? There may be about as many narrow definitions of creativity as there are people who think about creativity (Figure 11.15).
However, most investigators in the field of creativity would broadly define creativity
as the process of producing something that is both original and worthwhile (Csikszentmihalyi, 1999, 2000; Kozbelt, Beghetto, & Runco, 2010; Lubart & Mouchiroud,
2003; Sternberg & Lubart, 1996). The something could take many forms. It might be
Image not available due to copyright restrictions
Here are some original and worthwhile ways of defining creativity. How do you define
creativity?
Source: From “The Nature of Creativity as Manifest in Its Testing,” by E.P. Torrance in The Nature of Creativity, edited by Robert J. Sternberg. Copyright © 1988 by Cambridge University Press. Reprinted by permission
of Cambridge University Press and E.P. Torrance.
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CHAPTER 11 • Problem Solving and Creativity
a theory, a dance, a chemical, a process or procedure, a story, a symphony, or almost
anything else.
What does it take to create something original and worthwhile? What are creative people like? Almost everyone would agree that creative individuals show creative productivity. They produce inventions, insightful discoveries, artistic works,
revolutionary paradigms, or other products that are both original and worthwhile.
Conventional wisdom suggests that highly creative individuals also have creative
lifestyles. These lifestyles are characterized by flexibility, non-stereotyped behaviors,
and non-conforming attitudes.
What Are the Characteristics of Creative People?
Some psychologists measure creativity through divergent production—the generation
of a diverse assortment of appropriate responses, an approach originated by Guilford
(1950) (see Runco & Albert, 2010, for a history of the field, and Plucker & Makel,
2010, for a discussion of assessment of creativity). For example, creative individuals
often have high scores on assessments of creativity. An example of such an assessment is found in the Torrance Tests of Creative Thinking (Torrance, 1974, 1984).
They measure the diversity, quantity, and appropriateness of responses to openended questions. An example of such a question is to think of all the possible ways
in which to use a paper clip or a ballpoint pen. Torrance’s test also assesses creative
figural responses. For example, a person might be given a sheet of paper displaying
some circles, squiggles, or lines. The test would assess how many different ways the
person had used the given shapes to complete a drawing. Assessment of the Torrance test would consider particularly how much the person had used unusual or
richly elaborated details in completing a figure.
Other psychological researchers have focused on creativity as a cognitive process
by studying problem solving and insight (Finke, 1995; Ward & Kolomyts, 2010;
Weisberg, 1988, 2009). Some of these researchers believe that what distinguishes remarkably creative individuals from less remarkable people is their expertise and commitment to their creative endeavor. Highly creative individuals work long and hard.
They study the work of their predecessors and their contemporaries. They thereby
become thoroughly expert in their fields. They then build on and diverge from
what they know to create innovative approaches and products (Weisberg, 1988,
2009) and thereby change society (Moran, 2010). One study examined the creativity of projects completed by design students. The researchers found that the greater
the knowledge amassed by a student, the greater, on average, the creativity of the
project (Christiaans & Venselaar, 2007).
Some computer programs, such as those composing music or rediscovering scientific principles, can be viewed as creative. The question one always needs to ask with
these programs is whether their accomplishments truly are comparable to those of
creative humans, and whether the processes they use to be creative are the same as
those used by humans (Boden, 1999). Langley and colleagues’ (1987) programs of
scientific discovery actually rediscover scientific ideas rather than discover them for
the first time. Even Deep Blue, the computer program that beat world-champion
chess player Gary Kasparov, did so not by playing chess more creatively than Kasparov. Rather, it won through its enormous powers of rapid computation.
Personality and motivation play important roles in creativity (Barron, 1988;
Feist, 2010; Hennessey, 2010; Runco, 2010). Often underlying creativity are flexible
beliefs and broadly accepting attitudes toward other cultures, other races, and other
What do creative people such as Leonardo da Vinci, Albert Einstein, and Isaac Newton have in common?
religious creeds. Some investigators have focused on the importance of motivation in
creative productivity (e.g., Amabile, 1996; Collins & Amabile, 1999).
One may differentiate intrinsic motivation, which is internal to the individual, from extrinsic motivation, which is external to the individual. For example, intrinsic motivators might include sheer enjoyment of the creative process or personal
desire to solve a problem. Intrinsic motivation is essential to creativity. Extrinsic motivators might include a desire for fame or fortune. Extrinsic motivators actually may
impede creativity under many but not all circumstances (Amabile, 1996; Prabhu et al.,
2008). Curiously, in one experiment, extrinsic rewards for novel performance led to
an increase in both creativity and intrinsic motivation. Conversely, extrinsic rewards
for normal performance resulted in a decrease in both creativity and intrinsic motivation (Eisenberger & Shanock, 2003).
Certain traits seem consistently to be associated with creative individuals (Feist,
1998, 1999; Prabhu et al., 2008; Zhang & Sternberg, 2009). In particular, creative
individuals tend to be more open to new experiences, self-confident, self-accepting,
impulsive, ambitious, driven, dominant, and hostile than less creative individuals.
They also are less conventional.
Creativity needs to be viewed in the contexts in which it occurs (Csikszentmihalyi, 1988, 1996; Moran, 2010). One can seek to understand creativity by going
beyond the immediate social, intellectual, and cultural context to embrace the entire
sweep of history (Simonton, 1988, 1994, 1997, 1999, 2010). Creative contributions,
almost by definition, are unpredictable because they violate the norms established by
the forerunners and the contemporaries of the creator. Among the many attributes
of creative individuals are the abilities to make serendipitous discoveries and to pursue such discoveries actively (Simonton, 1994).
Evolutionary thinking also can be used to study creativity (Cziko, 1998; Gabora &
Kaufman, 2010; Simonton, 2010). Underlying such models is the notion that creative
ideas evolve much as organisms do. The idea is that creativity occurs as an outcome of
a process of blind variation and selective retention (Campbell, 1960). In blind variation,
creators first generate an idea. They have no real sense of whether the idea will be
© Omikron/Photo Researchers, Inc.
481
© SuperStock, Inc./SuperStock
© Pixtal/SuperStock
Creativity
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CHAPTER 11 • Problem Solving and Creativity
n BELIEVE IT OR NOT
DOES THE FIELD YOU’RE IN PREDICT WHEN
YOU WILL DO YOUR BEST WORK?
Creative people often long to make a contribution that will
change the world. What they may not realize is that the
age at which they make such a contribution depends not
only on them, but the field that they choose to enter. Dean
Simonton (1988, 1991, 1994) has studied career trajectories for creative contributions. He has found that the
age at which people make their outstanding creative
contributions varies somewhat widely by field. For example, in chemistry, the average age of one’s greatest work
is 38. In medicine, it is 42. Among composers, it is
around 41. But notice this: Despite the variation, chances
are pretty good that, on average, the best work will occur
roughly around the age of 40. So if you view yourself as
creative but have not yet had your great idea, and you
are under 40, remember that the best is probably yet to
come.
successful (selected for) in the world of ideas. As a result, their best bet for producing
lasting ideas is to go for a large quantity of ideas. Some of these ideas then will be valued
by their field. That is, they will be selectively retained by virtue of their being labeled as
creative.
Creative individuals tended to have moderately supportive, but often strict and relatively chilly (i.e., not warmly affectionate and nurturing) early family lives. They have
highly supportive mentors. Most showed an early interest in their chosen field, but
many were not particularly noteworthy (Gardner, 1993a, Policastro & Gardner, 1999;
see also Gruber, 1974/1981; Gruber & Davis, 1988). They generally tended to show an
early interest in exploring uncharted territory; but only after gaining mastery of their
chosen field, after about a decade of practicing their craft, did they have their initial
revolutionary breakthrough. Most creators seemed to have obtained at least some emotional and intellectual support at the time of their breakthrough. However, following
this initial breakthrough (and sometimes before), highly creative individuals generally
dedicated all their energies to their work. They sometimes abandoned, neglected, or
exploited close relationships during adulthood. About a decade after their initial creative achievement, most of the creators Gardner studied made a second breakthrough. It
was more comprehensive and more integrative but less revolutionary. Whether a creator continued to make significant contributions depended on the particular field of endeavor. Poets and scientists were less likely to do so than musicians and painters.
An alternative integrative theory of creativity suggests that multiple individual
and environmental factors must converge for creativity to occur (Sternberg & Lubart,
1991, 1996). What distinguishes the highly creative individual from the only modestly
creative one is the confluence of multiple factors, rather than extremely high levels of
any particular factor or even the possession of a distinctive trait. This theory is termed
the investment theory of creativity. The theme unifying these various factors is that the
creative individual takes a buy-low, sell-high approach to ideas (Sternberg & Lubart,
1995, 1996). In buying low, the creator initially sees the hidden potential of ideas that
are presumed by others to have little value. The creative person then focuses attention
on this idea. It is, at the time of the creator’s interest, unrecognized or undervalued by
contemporaries, but it has great potential for creative development. The creator then
develops the idea into a meaningful, significant creative contribution until at last
others also can recognize the merits of the idea. Some of these contributions may be
stunning; others more modest (Sternberg, Kaufman, & Pretz, 2001, 2002). Once the
idea has been developed and its value is recognized, the creator then sells high. He or
she then moves on to other pursuits and looks for the hidden potential in other
Creativity
483
INVESTIGATING COGNITIVE PSYCHOLOGY
Creativity in Problem Solving
Line up six toothpicks. Ask a friend to make four equilateral triangles with these six toothpicks without breaking the toothpicks into pieces. Most people will not be able to do this
task because they will try to make the four triangles on a single plane. When they give up,
make a single triangle flat on the table with three of the toothpicks; then with the other three
toothpicks, make a pyramid by joining the three toothpicks at the top and having the sides
connect with the intersections of the three toothpicks on the table. Your friend was fixated
on the plane of the alignment of the toothpicks. See if any of your friends can figure out this
problem if you give them the toothpicks standing up in a toothpick holder.
undervalued ideas. Thus, the creative person influences the field most by always staying a step ahead of the rest. In the ideal, students would develop not only a strong
knowledge base, but the skills and attributes discussed here that are essential to creativity (Beghetto, 2010; Smith & Smith, 2010).
Neuroscience and Creativity
The examination of creative thought and production has led researchers to identify
brain regions that are active during creativity (Kaufman, Kornilov, Bristol, Tan, &
Grigorenko, 2010). The prefrontal regions are especially active during the creative
process, regardless of whether the creative thought is effortful or spontaneous
(Dietrich, 2004).
In addition to the prefrontal area, other regions have also been identified as important for creativity. In one study, participants were given a list of words that were either
semantically related or unrelated (Bechtereva et al., 2004). The participants were then
asked to make up a story using all of these words. Forming a story from a list of unrelated words should require more creativity than using a list of semantically related
words. These researchers noted that Brodmann’s area (BA) 39 was active during the
unrelated-list story production but not during production of stories with the list of related words. Previous research has indicated that this and related Brodmann’s areas are
involved in verbal working memory, task switching, and imagination (Blackwood
et al., 2000; Collette et al., 2001; Sohn et al., 2000; Zurowski et al., 2002).
A selective thinning of cortical areas seems to correlate with intelligence and
creativity. In particular, a thinning of the left frontal lobe, lingual, cuneus, angular,
inferior parietal, and fusiform gyri is connected with high scores on creativity measures. These areas include several Brodmann’s areas, including BA 39. Additionally,
a relative thickness of the right posterior cingulate gyrus and right angular gyrus was
related to higher creativity as well. These variations in cortical thickness, and especially a thinning in various areas, probably influence information flow within the
brain (Jung et al., 2010).
CONCEPT CHECK
1. Name some ways how one can identify a creative individual.
2. What makes a contribution creative?
3. Which brain regions contribute to creative processes?
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CHAPTER 11 • Problem Solving and Creativity
Key Themes
This chapter highlights several of the themes first presented in Chapter 1.
Domain generality versus domain specificity. Early work on problem solving,
such as that by Allen Newell and Herbert Simon and their colleagues, emphasized
the domain generality of problem solving. These investigators sought to write computer routines, such as the General Problem Solver, that would solve a broad array
of problems. Later theorists have emphasized domain specificity in problem solving.
They have especially called attention to the need for a broad knowledge base to
solve problems successfully.
Validity of causal inference versus ecological validity. Most studies of creativity
have occurred in laboratory settings. For example, Paul Torrance gave students paperand-pencil tests of creative thinking administered in classrooms. In contrast, Howard
Gruber has been interested only in creativity as it occurred in natural settings, such as
when Darwin generated his many ideas behind the theory of evolution.
Applied versus basic research. The field of creativity has generated many insights
regarding fundamental processes used in creative thought. But the field has also
spawned a large industry of “creativity enhancement”—programs designed to make
people more creative. Some of these programs use insights of basic research. Others
represent little more than the intuitions of their inventors. When possible, training
should be based on psychological theory and research, rather than guesswork.
Summary
1. What are some key steps involved in solving
problems? Problem solving involves mentally
working to overcome obstacles that stand in
the way of reaching a goal. The key steps of
problem solving are problem identification,
problem definition and representation, strategy
construction, organization of information, allocation of resources, monitoring, and evaluation.
In everyday experiences, these steps may be implemented very flexibly. Various steps may be
repeated, may occur out of sequence, or may be
implemented interactively.
2. What are the differences between problems
that have a clear path to a solution versus problems that do not? Although well-structured
problems may have clear paths to solution,
the route to solution still may be difficult to
follow. Some well-structured problems can be
solved using algorithms. They may be tedious
to implement but are likely to lead to an accurate solution if applicable to a given problem.
Computers are likely to use algorithmic
problem-solving strategies. Humans are more
likely to use rather informal heuristics (e.g.,
means–ends analysis, working forward, working
backward, and generate and test) for solving
problems. When ill-structured problems are
solved, the choice of an appropriate problem
representation powerfully influences the ease
of reaching an accurate solution. Additionally,
in solving ill-structured problems, people may
need to use more than a heuristic or an algorithmic strategy; insight may be required.
Many ill-structured problems cannot be
solved without the benefit of insight. There
are several alternative views of how insightful
problem solving takes place. According to the
Gestaltist and the neo-Gestaltist views, insightful problem solving is a special process. It comprises more than the sum of its parts and may be
evidenced by the suddenness of realizing a
solution.
3. What are some of the obstacles and aids to
problem solving? A mental set (also termed
entrenchment) is a strategy that has worked
in the past but that does not work for a particular problem that needs to be solved in the
present.
A particular type of mental set is functional
fixedness. It involves the inability to see that
something that is known to have a particular
use also may be used for serving other purposes.
Thinking about Thinking
Transfer may be either positive or negative. It
refers to the carryover of problem-solving skills
from one problem or kind of problem to another. Positive transfer across isomorphic problems rarely occurs spontaneously, particularly
if the problems appear to be different in content
or in context. Incubation follows a period of
intensive work on a problem. It involves laying
a problem to rest for a while and then returning
to it. In this way, subconscious work can continue on the problem while the problem is consciously ignored.
4. How does expertise affect problem solving?
Experts differ from novices in both the amount
and the organization of knowledge that they bring
to bear on problem solving in the domain of
their expertise. For experts, many aspects of
problem solving may be governed by automatic
processes.
Such automaticity usually facilitates the expert’s ability to solve problems in the given area
of expertise. When problems involve novel elements requiring novel strategies, however, the
automaticity of some procedures actually may impede problem solving, at least temporarily. Expertise in a given domain is viewed mostly from the
practice-makes-perfect perspective. However,
485
talent should not be ignored and probably contributes much to the differences among experts.
5. What is creativity, and how can it be fostered?
Creativity involves producing something that is
both original and worthwhile. Several factors
characterize highly creative individuals. One is
extremely high motivation to be creative in a
particular field of endeavor (e.g., for the sheer
enjoyment of the creative process). A second factor is both non-conformity in violating any conventions that might inhibit the creative work
and dedication in maintaining standards of excellence and self-discipline related to the creative work. A third factor in creativity is deep
belief in the value of the creative work, as well
as willingness to criticize and improve the work.
A fourth is careful choice of the problems or subjects on which to focus creative attention.
A fifth characteristic of creativity is thought
processes characterized by both insight and divergent thinking. A sixth factor is risk taking.
The final two factors in creativity are extensive
knowledge of the relevant domain and profound commitment to the creative endeavor.
In addition, the historical context and the domain and field of endeavor influence the expression of creativity.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe the steps of the problem-solving cycle
and give an example of each step.
2. What are some of the key characteristics of expert problem solvers?
3. What are some of the insights into problem
solving gained through studying computer simulations of problem solving? How might a
computer-based approach limit the potential for
understanding problem solving in humans?
4. Compare and contrast the various approaches to
creativity.
5. Design a problem that would require insight for
its solution.
6. Design a context for problem solving that would
enhance the ease of reaching a solution.
7. Given what we know about some of the hindrances to problem solving, how could you
minimize those hindrances in your handling
of the problems you face?
8. Given some of the ideas regarding creativity
presented in this chapter, what can you do to
enhance your own creativity?
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CHAPTER 11 • Problem Solving and Creativity
Key Terms
algorithms, p. 449
analysis, p. 445
convergent thinking, p. 445
creativity, p. 479
divergent thinking, p. 445
expert systems, p. 478
expertise, p. 468
functional fixedness, p. 460
heuristics, p. 449
ill-structured problems, p. 447
incubation, p. 465
insight, p. 455
isomorphic, p. 450
mental set, p. 460
negative transfer, p. 462
positive transfer, p. 462
problem solving, p. 443
problem space, p. 449
problem-solving cycle, p. 444
productive thinking, p. 456
stereotypes, p. 460
synthesis, p. 445
transfer, p. 462
transparency, p. 465
well-structured problems, p. 447
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Monty Hall
12
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Decision Making and Reasoning
CHAPTER OUTLINE
Judgment and Decision Making
Classical Decision Theory
The Model of Economic Man and Woman
Subjective Expected Utility Theory
Heuristics and Biases
Heuristics
Biases
Fallacies
Gambler’s Fallacy and the Hot Hand
Conjunction Fallacy
Sunk-Cost Fallacy
The Gist of It: Do Heuristics Help Us
or Lead Us Astray?
Opportunity Costs
Naturalistic Decision Making
Group Decision Making
Benefits of Group Decisions
Groupthink
Antidotes for Groupthink
Neuroscience of Decision Making
Deductive Reasoning
What Is Deductive Reasoning?
Conditional Reasoning
What Is Conditional Reasoning?
The Wason Selection Task
Conditional Reasoning in Everyday Life
Influences on Conditional Reasoning
Evolution and Reasoning
Syllogistic Reasoning: Categorical Syllogisms
What Are Categorical Syllogisms?
How Do People Solve Syllogisms?
Aids and Obstacles to Deductive Reasoning
Heuristics in Deductive Reasoning
Biases in Deductive Reasoning
Enhancing Deductive Reasoning
Inductive Reasoning
What Is Inductive Reasoning?
Causal Inferences
Categorical Inferences
Reasoning by Analogy
An Alternative View of Reasoning
Neuroscience of Reasoning
Key Themes
Summary
Thinking about Thinking: Analytical, Creative,
and Practical Questions
Key Terms
Media Resources
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CHAPTER 12 • Decision Making and Reasoning
Here are some questions we will explore in this chapter:
1. What are some of the strategies that guide human decision making?
2. What are some of the forms of deductive reasoning that people may use, and what factors facilitate or
impede deductive reasoning?
3. How do people use inductive reasoning to make causal inferences and to reach other types of
conclusions?
4. Are there any alternative views of reasoning?
n BELIEVE IT OR NOT
CAN A SIMPLE RULE OF THUMB OUTSMART
LAUREATE’S INVESTMENT STRATEGY?
A
NOBEL
If you wanted to invest your money in the stock market,
would you rather rely on a Nobel laureate’s strategy or
on a simple heuristic (which is kind of a rule of thumb)?
Researchers (De Miguel, 2007) compared the levels of
success of 14 portfolio management strategies and
compared them with the success of the simple 1/N
heuristic. This heuristic simply suggests that you distribute your assets evenly among a given number of options. That is, each of the N options receives 1/N of
the total investment. Among the other strategies evaluated was Nobel laureate Harry Markowitz’s mean-
variance model, according to which investors should
optimize the trade-off between the mean and variance
of a portfolio return. Markowitz suggested you minimize your risk and maximize your return by considering
several factors, such as that sometimes certain groups
of stocks go up in price whereas others go down (e.g.,
if the oil price goes up, airline profits will go down).
The researchers found that the simple 1/N heuristic
actually outperformed all 14 other models. In this chapter, you will learn more about how humans make decisions and what shortcuts (heuristics) they use when they
are faced with uncertainty or more information than
they can process.
Let’s start this chapter with a puzzle. Read the following description in Investigating
Cognitive Psychology: The Conjunction Fallacy, and rate the likelihood of the presented
statements.
INVESTIGATING COGNITIVE PSYCHOLOGY
The Conjunction Fallacy
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As
a student, she was deeply concerned with issues of discrimination and social justice and
also participated in anti-nuclear demonstrations.
Based on the preceding description, list the likelihood that the following statements
about Linda are true (with 0 meaning completely unlikely and 100 meaning totally
likely):
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Linda
Linda
Linda
Linda
Linda
Linda
Linda
Linda
is a teacher in elementary school.
works in a bookstore and takes yoga classes.
is active in the feminist movement.
is a psychiatric social worker.
is a member of the League of Women Voters.
is a bank teller.
is an insurance salesperson.
is a bank teller and is active in the feminist movement.
(Tversky & Kahneman, 1983, p. 297).
Judgment and Decision Making
489
If you are like 85% of the people Tversky and Kahneman studied, you rated the
likelihood of item (h) as greater than the likelihood of item (f). Imagine a huge convention hall filled with the entire population of bank tellers. Now think about how
many of them would be at a hypothetical booth for feminist bank tellers—a subset of
the entire population of bank tellers. If Linda is at the booth for feminist bank tellers, she must, by definition, be in the convention hall of bank tellers. Hence, the
likelihood that she is at the booth (i.e., she is a feminist bank teller) cannot logically
be greater than the likelihood that she is in the convention hall (i.e., she is a bank
teller). Nonetheless, given the description of Linda, we intuitively feel more likely to
find her at the booth within the convention hall than in the entire convention hall,
which makes no sense. This intuitive feeling is an example of a fallacy—erroneous
reasoning—in judgment and reasoning.
In this chapter, we consider many ways in which we make judgments and decisions and use reasoning to draw conclusions. The first section deals with how we
make choices and judgments. Judgment and decision making are used to select
from among choices or to evaluate opportunities. Afterward, we consider various
forms of reasoning. The goal of reasoning is to draw conclusions, either deductively
from principles or inductively from evidence.
Judgment and Decision Making
In the course of our everyday lives, we constantly are making judgments and decisions. One of the most important decisions you may have made is that of whether
and where to go to college. Once in college, you still need to decide on which
courses to take. Later on, you may need to choose a major field of study. You make
decisions about friends, dates, how to relate to your parents, how to spend money,
and countless other things. How do you go about making these decisions?
Classical Decision Theory
The earliest models of how people make decisions are referred to as classical decision
theory. Most of these models were devised by economists, statisticians, and philosophers, not by psychologists. Hence, they reflect the strengths of an economic perspective. One such strength is the ease of developing and using mathematical
models for human behavior.
The Model of Economic Man and Woman
Among the early models of decision making crafted in the 20th century was that of
economic man and woman. This model assumed three things:
1. Decision makers are fully informed regarding all possible options for their decisions and of all possible outcomes of their decision options.
2. They are infinitely sensitive to the subtle distinctions among decision options.
3. They are fully rational in regard to their choice of options (Edwards, 1954; see
also Slovic, 1990).
The assumption of infinite sensitivity means that people can evaluate the difference between two outcomes, no matter how subtle the distinctions among options
may be. The assumption of rationality means that people make their choices to maximize something of value, whatever that something may be.
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CHAPTER 12 • Decision Making and Reasoning
Consider an example of how this model works. Suppose that a decision maker is
considering which of two smartphones to buy. The decision maker, according to this
model, will consider every aspect of each phone. The shopper will next decide on
some objective basis how favorable each phone is on each aspect. The shopper
then will weigh objectively each of the aspects in terms of how important it is.
The favorability ratings will be multiplied by the weights. Then an overall averaged
rating will be computed, taking into account all of the data. The shopper then will
buy the smartphone with the best score. A great deal of economic research has been
based on this model.
Subjective Expected Utility Theory
An alternative model makes greater allowance for the psychological makeup of each
individual decision maker. According to subjective expected utility theory, the goal of
human action is to seek pleasure and avoid pain. According to this theory, in making decisions, people will seek to maximize pleasure (referred to as positive utility)
and to minimize pain (referred to as negative utility). In doing so, however, each
of us uses calculations of two things. One is subjective utility, which is a calculation
based on the individual’s judged weightings of utility (value), rather than on objective criteria. The second is subjective probability, which is a calculation based on
the individual’s estimates of likelihood, rather than on objective statistical computations. The difference between this model and the former one is that here the ratings
and weights are subjective, whereas in the former model they are supposedly
objective.
Scientists soon noticed that human decision making is more complex than even
this modified theory implies. In particular, when have you seriously considered every
aspect of a decision, rated each possible choice, weighted the choice, and then used
your favorability ratings and weights to compute an averaged evaluation of each of
the choices? Probably not recently.
Heuristics and Biases
The world is full of information and stimuli of different kinds. In order to function
properly and not get overwhelmed, we need to filter out the information we need
among the many different pieces of information available to us. The same holds
true for decision making. In order to be able to make a decision within a reasonable
time frame, we need to reduce the available information to a manageable amount.
Heuristics help us achieve this goal and at the same time decrease our efforts by allowing us to examine fewer cues or deal with fewer pieces of information (Shah &
Oppenheimer, 2008). However, sometimes our thinking also gets biased by our tendencies to make decisions more simply. The mental shortcuts of heuristics and biases
lighten the cognitive load of making decisions, but they also allow for a much
greater chance of error. We will explore both heuristics and biases in more detail
in the next section.
Heuristics
In the following sections, we discuss several heuristics people use in their daily decision making. Heuristics are mental shortcuts that lighten the cognitive load of making decisions.
Judgment and Decision Making
491
Satisficing As early as the 1950s some researchers were beginning to challenge the
notion of unlimited rationality. Not only did these researchers recognize that we humans do not always make ideal decisions and that we usually include subjective considerations in our decisions. But they also suggested that we humans are not entirely
and boundlessly rational in making decisions. In particular, we humans are not necessarily irrational. Rather, we show bounded rationality—we are rational, but within
limits (Simon, 1957).
Whereas classical decision theory suggested that people optimize their decisions,
researchers began to realize that we have only limited resources and time to make a
decision, so often we try to get as close as possible to optimizing, without actually
optimizing.
One of the first heuristics that was formulated by researchers is termed satisficing
(Simon, 1957). In satisficing, we consider options one by one, and then we select
an option as soon as we find one that is satisfactory or just good enough to meet our
minimum level of acceptability. When there are limited working-memory resources
available, the use of satisficing for making decisions may be increased (Chen & Sun,
2003). Satisficing is also used in industrial contexts in which too much information
can impair the quality of decisions, as in the selection of suppliers in electronic marketplaces (Chamodrakas, et al., 2010).
Of course, satisficing is only one of several strategies people can use. The appropriateness of this strategy will vary with the circumstance. For example, satisficing
might be a reasonable strategy if you are in a hurry to buy a pack of gum and then
catch a train or a plane, but a poor strategy for diagnosing a disease.
© Bob Daemmrich/PhotoEdit
Elimination by Aspects We sometimes use a different strategy when faced with far
more alternatives than we feel that we reasonably can consider in the time we have
available (Tversky, 1972a, 1972b). In such situations, we do not try to manipulate
According to Herbert Simon, people often satisfice when they make important decisions, such as which car
to buy. They decide based on the first acceptable alternative that comes along.
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mentally all the weighted attributes of all the available options. Rather, we use a
process of elimination by aspects, in which we eliminate alternatives by focusing
on aspects of each alternative, one at a time. If you are trying to decide which college to attend, the process of elimination by aspects might look like this:
• focus on one aspect (attribute) of the various options (the cost of going to
college);
• form a minimum criterion for that aspect (tuition must be under $20,000 per
year);
• eliminate all options that do not meet that criterion (e.g., Stanford University is
more than $30,000 and would be eliminated);
• for the remaining options, select a second aspect for which we set a minimum
criterion by which to eliminate additional options (the college must be on the
West Coast); and
• continue using a sequential process of elimination of options by considering a
series of aspects until a single option remains (Dawes, 2000).
Here is another example of elimination by aspects. In choosing a car to buy, we
may focus on total price as an aspect. We may choose to dismiss factors, such as
maintenance costs, insurance costs, or other factors that realistically might affect
the money we will have to spend on the car in addition to the sale price. Once we
have weeded out the alternatives that do not meet our criterion, we choose another
aspect. We set a criterion value and weed out additional alternatives. We continue
in this way. We weed out more alternatives, one aspect at a time, until we are left
with a single option. In practice, it appears that we may use some elements of elimination by aspects or satisficing to narrow the range of options to just a few. Then we
use more thorough and careful strategies. Examples would be those suggested by subjective expected utility theory. They can be useful for selecting among the few remaining options (Payne, 1976).
We often use mental shortcuts and even biases that limit and sometimes distort
our ability to make rational decisions. One of the key ways in which we use mental
shortcuts centers on our estimations of probability. Consider some of the strategies
used by statisticians when calculating probability. They are shown in Table 12.1.
Another kind of probability is conditional probability, which is the likelihood of
one event, given another. For example, you might want to calculate the likelihood
Table 12.1
Rules of Probability
Hypothetical Example
Calculation of Probability
Lee is one of 10 highly qualified candidates applying for
one scholarship. What are Lee’s chances of getting the
scholarship?
Lee has a 0.1 chance of getting the scholarship.
If Lee is one of 10 highly qualified scholarship students
applying for one scholarship, what are Lee’s chances of not
getting the scholarship?
1 – 0.1 = 0.9
Lee’s roommate and Lee are among 10 highly qualified
scholarship students applying for one scholarship.
What are the chances that one of the two will get the
scholarship?
Lee has a 0.9 chance of not getting the scholarship.
0.1 + 0.1 = 0.2
There is a 0.2 chance that one of the two roommates will
get the scholarship.
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of receiving an “A” for a cognitive psychology course, given that you receive an
“A” on the final exam. The formula for calculating conditional probabilities in
light of evidence is known as Bayes’s theorem. It is quite complex, so most people
do not use it in everyday-reasoning situations. Nonetheless, such calculations are
essential to evaluating scientific hypotheses, forming realistic medical diagnoses,
analyzing demographic data, and performing many other real-world tasks. (For
a highly readable explanation of Bayes’s theorem, see Eysenck & Keane, 1990,
pp. 456–458.)
Representativeness Heuristic Before you read about representativeness, try the
following problem from Kahneman and Tversky (1972).
All the families having exactly six children in a particular city were surveyed. In
72 of the families, the exact order of births of boys and girls was G B G B B G (G,
girl; B, boy).
What is your estimate of the number of families surveyed in which the exact
order of births was B G B B B B?
Most people judging the number of families with the B G B B B B birth pattern
estimate the number to be less than 72. Actually, the best estimate of the number of
families with this birth order is 72, the same as for the G B G B B G birth order.
The expected number for the second pattern would be the same because the
gender for each birth is independent (at least, theoretically) of the gender for every
other birth. For any one birth, the chance of a boy (or a girl) is one of two.
Thus, any particular pattern of births is equally likely (1/2)6, even B B B B B B or
G G G G G G.
Why do many of us believe some birth orders to be more likely than others? In
part, the reason is that we use the heuristic of representativeness. In representativeness, we judge the probability of an uncertain event according to:
1. how obviously it is similar to or representative of the population from which it is
derived; and
2. the degree to which it reflects the salient features of the process by which it is generated (such as randomness) (see also Fischhoff, 1999; Johnson-Laird, 2000, 2004).
For example, people believe that the first birth order is more likely because:
(1) it is more representative of the number of females and males in the population;
and (2) it looks more like a random order than does the second birth order. In fact,
of course, either birth order is equally likely to occur by chance.
Similarly, suppose people are asked to judge the probability of flips of a coin
yielding the sequence H T H H T H (H, heads; T, tails). Most people will judge it
as higher than they will if asked to judge the sequence H H H H T H. If you expect
a sequence to be random, you tend to view as more likely a sequence that “looks
random.” Indeed, people often comment that the numbers in a table of random
numbers “don’t look random.” The reason is that people underestimate the number
of runs of the same number that will appear wholly by chance. We frequently reason
in terms of whether something appears to represent a set of accidental occurrences,
rather than actually considering the true likelihood of a given chance occurrence.
This tendency makes us more vulnerable to the machinations of magicians, charlatans, and con artists. Any of them may make much of their having predicted the
realistic probability of a non-random-looking event. For example, in one out of ten
cases two people in a group of 40 (e.g., in a classroom or a small nightclub audience)
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will share a birthday (the same month and day). In a group of 14 people, there are
better than even odds that two people will have birthdays within a day of each other
(Krantz, 1992).
That we frequently rely on the representativeness heuristic may not be terribly
surprising. It is easy to use and often works. For example, suppose we have not
heard a weather report prior to stepping outside. We informally judge the probability that it will rain. We base our judgment on how well the characteristics of
this day (e.g., the month of the year, the area in which we live, and the presence
or absence of clouds in the sky) represent the characteristics of days on which it
rains. Another reason that we often use the representativeness heuristic is that we
mistakenly believe that small samples (e.g., of events, of people, of characteristics)
resemble in all respects the whole population from which the sample is drawn
(Tversky & Kahneman, 1971). We particularly tend to underestimate the likelihood that the characteristics of a small sample (e.g., the people whom we know
well) of a population inadequately represent the characteristics of the whole
population.
We also tend to use the representativeness heuristic more frequently when we
are highly aware of anecdotal evidence based on a very small sample of the population. This reliance on anecdotal evidence has been referred to as a “man-who” argument (Nisbett & Ross, 1980). When presented with statistics, we may refute those
data with our own observations of, “I know a man who . . .” For example, faced with
statistics on coronary disease and high-cholesterol diets, someone may counter with,
“I know a man who ate whipped cream for breakfast, lunch, and dinner, smoked two
packs of cigarettes a day, and lived to be 110 years old. He would have kept going
but he was shot through his perfectly healthy heart by a jealous lover.”
One reason that people misguidedly use the representativeness heuristic is because they fail to understand the concept of base rates. Base rate refers to the prevalence of an event or characteristic within its population of events or
characteristics. In everyday decision making, people often ignore base-rate information, but it is important to effective judgment and decision making. In many
occupations, the use of base-rate information is essential for adequate job performance. For example, suppose a doctor was told that a 10-year-old boy was suffering
chest pains. The doctor would be much less likely to worry about an incipient
heart attack than if the doctor were told that a 60-year-old man had the identical
symptom. Why? Because the base rate of heart attacks is much higher in 60year-old men than in 10-year-old boys. Of course, people use other heuristics as
well. People can be taught how to use base rates to improve their decision making
(Gigerenzer, 1996; Koehler, 1996).
Availability Heuristic Most of us at least occasionally use the availability heuristic, in
which we make judgments on the basis of how easily we can call to mind what we perceive as relevant instances of a phenomenon (Tversky & Kahneman, 1973; see also
Fischhoff, 1999; Sternberg, 2000). For example, consider the letter R. Are there more
words in the English language that begin with the letter R or that have R as their third
letter? Most respondents say that there are more words beginning with the letter R
(Tversky & Kahneman, 1973). Why? Because generating words beginning with the letter
R is easier than generating words having R as the third letter. In fact, there are more
English-language words with R as their third letter. The same happens to be true of some
other letters as well, such as K, L, N, and V.
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The availability heuristic also has been observed in regard to everyday situations. In one study, married partners individually stated which of the two partners
performed a larger proportion of each of 20 different household chores (Ross &
Sicoly, 1979). These tasks included mundane chores such as grocery shopping or
preparing breakfast. Each partner stated that he or she more often performed about
16 of the 20 chores. Suppose each partner was correct. Then, to accomplish 100% of
the work in a household, each partner would have to perform 80% of the work.
Similar outcomes emerged from questioning members of college basketball teams
and joint participants in laboratory tasks.
Although clearly 80% þ 80% does not equal 100%, we can understand why
people may engage in using the availability heuristic when it confirms their beliefs
about themselves. However, people also use the availability heuristic when its use
leads to a logical fallacy that has nothing to do with their beliefs about themselves.
Two groups of participants were asked to estimate the number of words of a particular form that would be expected to appear in a 2,000-word passage. For one group
the form was _ _ _ _ing (i.e., seven letters ending in -ing). For the other group the
form was _ _ _ _ _n_ (i.e., seven letters with n as the second-to-the-last letter).
Clearly, there cannot be more seven-letter words ending in -ing than seven-letter
words with n as the second-to-the-last letter. But the greater availability of the former led to estimates of probability that were more than twice as high for the former,
as compared with the latter (Tversky & Kahneman, 1983).
Anchoring A heuristic related to availability is the anchoring-and-adjustment heuristic, by which people adjust their evaluations of things by means of certain reference
points called end-anchors. Before you read on, quickly (in less than 5 seconds) calculate in your head the answer to the following problem:
87654321
Now, quickly calculate your answer to the following problem:
12345678
Two groups of participants estimated the product of one or the other of the preceding two sets of eight numbers (Tversky & Kahneman, 1974). The median (middle)
estimate for the participants given the first sequence was 2,250. For the participants
given the second sequence, the median estimate was 512. (The actual product is
40,320 for both.) The two products are the same, as they must be because the numbers
are exactly the same (applying the commutative law of multiplication). Nonetheless,
people provide a higher estimate for the first sequence than for the second because
their computation of the anchor—the first few digits multiplied by each other—renders a higher estimate from which they make an adjustment to reach a final estimate.
Furthermore, the adjustment people make in response to an anchor is bigger when the
anchor is rounded than when it seems to be a precise value. For example, when the
price of a TV set is given as $3,000, people adjust their estimate of its production costs
more than when the price is given as $2,991 (Janiszewski & Uy, 2008). Anchoring
effects occur in a variety of settings, for example at art auctions, where the price of
paintings is anchored by the price the painting achieved in prior sales, or monthly economic forecasts, which are anchored toward the past month (Beggs & Graddy, 2009;
Campbell & Sharpe, 2009).
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Although riding a car is statistically much more risky than riding in a plane, people often feel less safe
in a plane, in part because of the availability heuristic. People hear about every major U.S. plane crash
that takes place, but they hear about relatively few car accidents.
Framing Another consideration in decision theory is the influence of framing effects, in which the way that the options are presented influences the selection of
an option (Tversky & Kahneman, 1981). For instance, we tend to choose options
that demonstrate risk aversion when we are faced with an option involving potential
gains. That is, we tend to choose options offering a small but certain gain rather
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INVESTIGATING COGNITIVE PSYCHOLOGY
Framing Effects
Suppose that you were told that 600 people were at risk of dying of a particular disease. Vaccine A could save the lives of 200 of the people at risk. With Vaccine B, there
is a 0.33 likelihood that all 600 people would be saved, but there is also a 0.66 likelihood that all 600 people will die. Which option would you choose? Explain how you
made your decision.
We tend to choose options that demonstrate risk seeking when we are faced with options involving potential losses. That is, we tend to choose options offering a large but
uncertain loss rather than a smaller but certain loss (as is the case for Vaccine B), unless
the uncertain loss is either tremendously greater or only modestly less than certain. Here
is an interesting example.
Suppose that for the 600 people at risk of dying of a particular disease, if Vaccine C is
used, 400 people will die. However, if Vaccine D is used, there is a 0.33 likelihood
that no one will die and a 0.66 likelihood that all 600 people will die. Which option
would you choose?
In the preceding situations, most people will choose Vaccine A and Vaccine D.
Now, try this:
• Compare the number of people whose lives will be lost or saved by using Vaccines
A or C.
• Compare the number of people whose lives will be lost or saved by using Vaccines
B or D.
The expected value is identical for Vaccines A and C; it is also identical for Vaccines B
and D. Our predilection for risk aversion versus risk seeking leads us to quite different
choices based on the way in which a decision is framed, even when the actual outcomes of the choices are the same.
than a larger but uncertain gain, unless the uncertain gain is either tremendously
greater or only modestly less than certain. The first example in Investigating Cognitive
Psychology: Framing Effects is only slightly modified from one used by Tversky and
Kahneman (1981).
Framing effects have public relevance. Messages from politicians, political parties, and other stakeholders can be framed in different ways and therefore take on a
different connotation. A message about the Ku Klux Klan, for example, can be
framed either as a free-speech issue or as a public-safety issue. Framing effects are
less persuasive when they come from sources of low credibility (Druckman, 2001).
Biases
In the next section, we discuss several biases that frequently occur when people
make decisions: illusory correlation, overconfidence, and hindsight bias.
Illusory Correlation We are predisposed to see particular events or attributes and
categories as going together, even when they do not. This phenomenon is called
illusory correlation (Hamilton & Lickel, 2000). In the case of events, we may see
spurious cause-effect relationships. In the case of attributes, we may use personal
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prejudices to form and use stereotypes (perhaps as a result of using the representativeness heuristic). For example, suppose we expect people of a given political party
to show particular intellectual or moral characteristics. The instances in which people show those characteristics are more likely to be available in memory and recalled
more easily than are instances that contradict our biased expectations. In other
words, we perceive a correlation between the political party and the particular
characteristics.
Illusory correlation even may influence psychiatric diagnoses based on projective
tests such as the Rorschach and the Draw-a-Person tests (Chapman & Chapman,
1967, 1969, 1975). Researchers suggested a false correlation in which particular
diagnoses would be associated with particular responses. For example, they suggested
that people diagnosed with paranoia tend to draw people with large eyes more than
do people with other diagnoses (which is not true). However, what happened when
individuals expected to observe a correlation between a drawing with large eyes and
the associated diagnosis of paranoia? They tended to see the illusory correlation,
although no actual correlation existed.
Overconfidence Another common error is overconfidence—an individual’s overvaluation of her or his own skills, knowledge, or judgment. For example, people answered 200 two-alternative statements, such as “Absinthe is (a) a liqueur, (b) a
precious stone.” (Absinthe is a licorice-flavored liqueur.) People were asked to
choose the correct answer and to state the probability that their answer was correct
(Fischhoff, Slovic, & Lichtenstein, 1977). People were overconfident. For example,
when people were 100% confident in their answers, they were right only 80% of the
time. In general, people tend to overestimate the accuracy of their judgments
(Kahneman & Tversky, 1996). Why are people overconfident? One reason is that people may not realize how little they know. Another is that they may not realize that
their information comes from unreliable sources (Carlson, 1995; Griffin & Tversky,
1992).
People sometimes make poor decisions as a result of overconfidence. These decisions are based on inadequate information and ineffective decision-making strategies. Why we tend to be overconfident in our judgments is not clear. One simple
explanation is that we prefer not to think about being wrong (Fischhoff, 1988).
Businesses sometimes use our tendencies toward overconfidence to their own advantage. Think about the American cell phone market, for example. Many contracts
consist of a monthly fee that includes usage of a certain amount of air-time minutes.
If a person exceeds this amount, he or she will incur steep charges. There are good
reasons for such a contract model, but from the company’s point of view, not from
the consumer’s point of view. Consumers tend to overestimate their usage of minutes, so they are willing to pay for a high-minute usage in advance. At the same
time, they are confident they will not go over their limit, so they do not even realize
the high costs they will incur if they exceed their free air-time minutes, until they
actually discover they have gone over (Grubb, 2009).
Hindsight Bias Finally, a bias that can affect all of us is hindsight bias—when we
look at a situation retrospectively, we believe we easily can see all the signs and
events leading up to a particular outcome (Fischhoff, 1982; Wasserman, Lempert,
& Hastie, 1991). For example, suppose people are asked to predict the outcomes of
psychological experiments in advance of the experiments. People rarely are able to
predict the outcomes at better-than-chance levels. However, when people are told of
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the outcomes of psychological experiments, they frequently comment that these outcomes were obvious and could easily have been predicted in advance. Similarly,
when intimate personal relationships are in trouble, people often fail to observe signs
of the difficulties until the problems reach crisis proportions. By then, it may be too
late to save the relationship. In retrospect, people may ask themselves, “Why didn’t I
see it coming? It was so obvious! I should have seen the signs.”
Hindsight bias hinders learning because it impairs one’s ability to compare one’s
expectations with the outcome—if one always expected the outcome that eventually
happened, one thinks there is nothing to learn! And indeed, studies show that investment bankers’ performance suffers when they exhibit a strong hindsight bias.
Curiously, experience does not reduce the bias (Biais & Weber, 2009).
Fallacies
Heuristics and fallacies are often studied together because they go hand in hand.
The application of a heuristic to make a decision may lead to fallacies in thinking.
Therefore, when we discuss some fallacies, we refer back to some of the heuristics in
association with which they often occur.
Gambler’s Fallacy and the Hot Hand
Gambler’s fallacy is a mistaken belief that the probability of a given random event,
such as winning or losing at a game of chance, is influenced by previous random
events. For example, a gambler who loses five successive bets may believe that a
win is therefore more likely the sixth time. He feels that he is “due” to win. In truth,
of course, each bet (or coin toss) is an independent event that has an equal probability of winning or losing. The gambler is no more likely to win on the 6th bet than
on the 1st—or on the 1001st. Gambler’s fallacy is an example of the representative
heuristic gone awry: One believes that the pattern representative of past events is
now likely to change.
A tendency opposite to that of gambler’s fallacy is called the “hot hand” effect.
It refers to a belief that a certain course of events will continue. Apparently, both
professional and amateur basketball players, as well as their fans, believe that a
player’s chances of making a basket are greater after making a previous shot than
after missing one. However, the statistical likelihoods (and the actual records of
players) show no such tendency (Gilovich, Vallone, & Tversky, 1985; see also
Roney & Trick, 2009). Shrewd players take advantage of this belief and closely
guard opponents immediately after they have made baskets. The reason is that the
opposing players will be more likely to try to get the ball to these perceived “streak
shooters.”
Conjunction Fallacy
Do you remember the experiment described in the section on the availability heuristic where people were asked to judge how often the form _ _ _ _ing (i.e., seven letters ending in –ing) or _ _ _ _ _n_ (i.e., seven letters with n as the second-to-the-last
letter) appears in a passage? The availability heuristic might lead to the conjunction
fallacy. In the conjunction fallacy, an individual gives a higher estimate for a subset of
events (e.g., the instances of -ing) than for the larger set of events containing the
given subset (e.g., the instances of n as the second-to-the-last letter). This fallacy
also is illustrated in the chapter opening vignette regarding Linda.
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People often mistakenly believe in the gambler’s fallacy. They think that if they have been unlucky in their
gambles, it is time for their luck to change. In fact, success or failure in past gambles has no effect on the
likelihood of success in future ones.
The representativeness heuristic may also induce individuals to engage in the
conjunction fallacy during probabilistic reasoning (Tversky & Kahneman, 1983; see
also Dawes, 2000). Tversky and Kahneman asked college students:
Please give your estimate of the following values: What percentage of the men surveyed
[in a health survey] have had one or more heart attacks?
What percentage of the men surveyed both are over 55 years old and have had one or
more heart attacks? (p. 308)
The mean estimates were 18% for the former and 30% for the latter. In fact,
65% of the respondents gave higher estimates for the latter (which is clearly a subset
of the former). However, people do not always engage in the conjunction fallacy.
Only 25% of respondents gave higher estimates for the latter question than for the
former when the questions were rephrased as frequencies rather than as percentages
(e.g., “how many of the 1,000 men surveyed have had one or more heart attacks?”).
The way statistical information is presented influences how likely it is that people
draw the correct conclusions (see also Gigerenzer & Hoffrage, 1995).
Sunk-Cost Fallacy
An error in judgment that is quite common in people’s thinking is the sunk-cost fallacy (Dupuy, 1998, 1999; Strough et al., 2008). This fallacy represents the decision
to continue to invest in something simply because one has invested in it before and
one hopes to recover one’s investment. For example, suppose you have bought a car.
It is a lemon. You already have invested thousands of dollars in getting it fixed. Now
you have another major repair on it confronting you. You have no reason to believe
that this additional repair really will be the last in the string of repairs. You think
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about how much money you have spent on repairs and reason that you need to do
the additional repair to justify past amounts already spent. So you do the repair
rather than buy a new car. You have just committed the sunk-cost fallacy. The problem is that you already have lost the money on those repairs. Throwing more money
into the repairs will not get that money back. Your best bet may well be to view the
money already spent on repairs as a “sunk cost” and then buy a new car.
Similarly, suppose you go on a two-week vacation. You are having a miserable
time. Should you go home a week early? You decide not to, thereby attempting to
justify the investment you have already made in the vacation. Again, you have committed the sunk-cost fallacy. Instead of viewing the money simply as lost on an unfortunate decision, you have decided to throw more money away. But you do so
without any hope that the vacation will get any better.
The Gist of It: Do Heuristics Help Us or Lead Us Astray?
Heuristics do not always lead to wrong judgments or poor decisions (Cohen, 1981).
Indeed, we use these mental shortcuts because they are so often right. Sometimes,
they are amazingly simple ways of drawing sound conclusions. For example, a simple
heuristic, take-the-best, can be amazingly effective in decision situations (Gigerenzer &
Brighton, 2009; Gigerenzer & Goldstein, 1996; Marsh, Todd, & Gigerenzer, 2004).
The rule is simple. In making a decision, identify the single most important criterion
to you for making that decision. For example, when you choose a new automobile, the
most important factor might be good gas mileage, safety, or appearance. Make your
choice on the basis of that attribute.
On its face this heuristic would seem to be inadequate. In fact, it often leads to
very good decisions. It produces even better decisions, in many cases, than far more
complicated heuristics. Thus, heuristics can be used for good as well as for bad decision making. Indeed, when we take people’s goals into account, heuristics often are
amazingly effective (Evans & Over, 1996).
The take-the-best heuristic belongs to a class of heuristics called fast-and-frugal
heuristics (FFH). As the name implies, this class of heuristics is based on a small
fraction of information, and decisions using the heuristics are made rapidly. These
heuristics set a standard of rationality that considers constraints including, time, information, and cognitive capacity (Bennis & Pachur, 2006; Gigerenzer, Todd, & the
ABC Research Group, 1999). Furthermore, these models consider the lack of optimum solutions and environments in which the decision is taking place. As a result,
these heuristics provide a good description of decision making during sports.
Fast-and-frugal heuristics can form a comprehensive description of how people
behave in a variety of contexts. These behaviors vary from lunch selections to how
physicians decide whether to prescribe medication for depression, to making business
decisions (Goldstein & Gigerenzer, 2009; Scheibehenne, Miesler, & Todd, 2007;
Smith & Gilhooly, 2006).
The work on heuristics and biases shows the importance of distinguishing between intellectual competence and intellectual performance as it manifests itself in
daily life. Even experts in the use of probability and statistics can find themselves
falling into faulty patterns of judgment and decision making in their everyday lives.
People may be intelligent in a conventional, test-based sense. Yet they may show
exactly the same biases and faulty reasoning that someone with a lower test score
would show. People often fail to fully utilize their intellectual competence in their
daily life. There can even be a wide gap between the two (Stanovich, 2010). Thus,
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if we wish to be intelligent in our daily lives and not just on tests, we have to be
street smart. In particular, we must be mindful of applying our intelligence to the
problems that continually confront us.
Opportunity Costs
Opportunity costs are the prices paid for availing oneself of certain opportunities.
Taking opportunity costs into account is important when judgments are made. For
example, suppose you see a great job offer in San Francisco. You always wanted to
live there. You are ready to take it. Before you do, you need to ask yourself a question: What other things will you have to forego to take advantage of this opportunity? An example might be the chance, on your budget, of having more than 500
square feet of living space. Another might be the chance to live in a place where
you probably do not have to worry about earthquakes. Any time you take advantage
of an opportunity, there are opportunity costs. They may, in some cases, make what
looked like a good opportunity look like not such a great opportunity at all. Ideally,
you should try to look at these opportunity costs in an unbiased way.
Naturalistic Decision Making
Many researchers contend that decision making is a complex process that cannot be
reproduced adequately in the laboratory because real decisions are frequently made
in situations where there are high stakes. For instance, the mental state and cognitive pressure experienced by an emergency room doctor encountering a patient is
difficult to reproduce outside a clinical setting.
This criticism has led to the development of a field of study that is based on decision making in natural environments (naturalistic decision making). Much of the research completed in this area is from professional settings, such as hospitals or nuclear
plants (Carroll, Hatakenaka, & Rudolph, 2006; Galanter & Patel, 2005; Roswarski, &
Murray, 2006). These situations share a number of features, including the challenges
of ill-structured problems, changing situations, high risk, time pressure, and sometimes,
a team environment (Orasanu & Connolly, 1993). A number of models are used to
explain performance in these high-stakes situations. These models allow for the consideration of cognitive, emotional, and situational factors of skilled decision makers;
they also provide a framework for advising future decision makers (Klein, 1997;
Lipshitz et al., 2001). For instance, Orasanu (2005) developed recommendations for
training astronauts to be successful decision makers by evaluating what makes current
astronauts successful, such as developing team cohesion and managing stress. Naturalistic decision making can be applied to a broad range of behaviors and environments.
These applications can include individuals as diverse as badminton players, railroad
controllers, and NASA astronauts (Farrington-Darby et al., 2006; Macquet & Fleurance, 2007; Orasanu, 2005; Patel, Kaufman, & Arocha, 2002).
Group Decision Making
Groups form decisions differently than individuals. Often, there are benefits to making decisions in groups. However, a phenomenon called “groupthink” can occur that
seriously impairs the quality of decisions made. In the next sections we will explore
group decision making in more detail.
Judgment and Decision Making
503
IN THE LAB OF GERD GIGERENZER
Making Decisions in
an Uncertain World
The study of the ecological rationality of
a given heuristic investigates in what
world it succeeds.
If you were in my lab, you would talk to preThe third question concerns intuitive
docs, post-docs, and researchers from ten
design. Here we use the results of our
different disciplines as well as nationalities.
research to design heuristics and environWe investigate bounded rationality, that is,
ments that help experts and laypeople
how humans make decisions in an uncermake better decisions. For instance,
tain world. This differs from the study of debased on our work, physicians in Michiductive reasoning, syllogisms, or classical
gan hospitals use heuristics called fastGERD GIGERENZER
decision theory, where all alternatives, conand-frugal trees when making ICU allocasequences, and probabilities are known for
tions. These simple heuristics mirror the
certain. In the real world, omniscience is absent and
sequential, intuitive thinking of doctors, are fast and
surprises can happen; nevertheless, people have to
frugal, and are nevertheless better than complex linear
make decisions, such as whom to trust, what medication
regression models at predicting heart attacks.
to take, or how to invest money. How does this rationality
A particularly relevant aspect of intuitive design is
for mortals work?
risk communication. Consider the contraceptive pill
The first question we pose is descriptive: What heurscare in the United Kingdom. The media reported that
istics do people rely on, consciously or unconsciously, to
third-generation pills increase the risk of potentially lifemake decisions in an uncertain world? A heuristic is a
threatening blood clots (thrombosis) by 100%. Disstrategy that focuses on the most relevant pieces of infortressed by this news, many women stopped taking the
mation and ignores the rest. We have investigated a
pill, which led to unwanted pregnancies and an estinumber of these, including those relying on:
mated 13,000 additional abortions in England and
Wales. How big is 100%? The studies on which the
• recognition (the recognition and fluency heuristics),
warning was based had shown that out of every
• one good reason (such as take-the-best), and
• on the wisdom of others (such as imitate-the-majority). 7,000 women who took the earlier second-generation
pill, about 1 had a thrombosis; this number increased
The study of the adaptive toolbox investigates the
to 2 among women who took third-generation pills.
heuristics used, their building blocks, and the core cogThat is, the absolute risk increase was only 1 in
nitive capacities they exploit.
7,000 while the relative risk increase was indeed
Our second question is prescriptive: In what envi100%. Had the media reported the absolute risks,
ronment does a heuristic work, and where would it fail?
few women would have panicked. The pill scare illusTo find answers, one needs to develop formal models
trates how citizens’ fears are manipulated by framing
of heuristics, using analysis and computer simulation.
numbers in a misleading and non-transparent way.
One surprising discovery we made is that simple heurWe study and develop transparent representations—
istics that rely on only one good reason (such as takesuch as absolute risks and natural frequencies—that
the-best) can actually make more accurate predictions
help people understand health statistics. During the
than can complex strategies such as multiple regression
last few years, I have trained some 1,000 physicians
or neural networks. In contrast to what many textbooks
and dozens of U.S. federal judges in understanding
still preach, this result shows that heuristics are not
risks, for instance when evaluating cancer screening
second-best, and that less information, computation,
or DNA tests. Few physicians and lawyers have been
and time can lead to better decisions. In fact, unlike in
educated in risk communication, and this blind spot is
certain worlds, in an uncertain world one needs to igan important area in which psychologists can apply
nore part of the information to make good judgments.
their knowledge and help.
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CHAPTER 12 • Decision Making and Reasoning
Benefits of Group Decisions
Working as a group can enhance the effectiveness of decision making, just as it can
enhance the effectiveness of problem solving. Many companies combine individuals
into teams to improve decision making. By forming decision-making teams, the
group benefits from the expertise of each of the members. There is also an increase
in resources and ideas (Salas, Burke, & Cannon-Bowers, 2000). Another benefit of
group decision making is improved group memory over individual memory (Hinsz,
1990). Groups that are successful in decision making exhibit a number of similar
characteristics, including the following:
•
•
•
•
•
the group is small;
it has open communication;
members share a common mind-set;
members identify with the group; and
members agree on acceptable group behavior (Shelton, 2006).
In juries, members share more information during decision making when the group
is made up of diverse members (Sommers, 2006). The juries are thereby in a position to
make better decisions. Furthermore, in examining decision making in public policy
groups, interpersonal influence is important (Jenson, 2007). Group members frequently employed tactics to affect other members’ decisions (Jenson, 2007). The most
frequently used and influential tactics were inspirational and rational appeals.
Groupthink
There can be disadvantages associated with group decision making, however. Of these
disadvantages, one of the most explored is groupthink. Groupthink is a phenomenon
characterized by premature decision making that is generally the result of group members attempting to avoid conflict (Janis, 1971). Groupthink frequently results in suboptimal decision making that avoids non-traditional ideas (Esser, 1998).
What conditions lead to groupthink? Janis cited three kinds:
(1) an isolated, cohesive, and homogeneous group is empowered to make
decisions;
(2) objective and impartial leadership is absent, within the group or outside it; and
(3) high levels of stress impinge on the group decision-making process.
Another cause of groupthink is anxiety (Chapman, 2006). When group members are anxious, they are less likely to explore new options and will likely try to
avoid further conflict.
The groups responsible for making foreign policy decisions are excellent candidates for groupthink. They are usually like-minded. Moreover, they frequently isolate
themselves from what is going on outside their own group. They generally try to
meet specific objectives and believe they cannot afford to be impartial. Also, of
course, they are under very high stress because the stakes involved in their decisions
can be tremendous.
But what exactly is groupthink? Janis (1971) delineated six symptoms of
groupthink:
1. Closed-mindedness—the group is not open to alternative ideas.
2. Rationalization—the group goes to great lengths to justify both the process and
the product of its decision making, distorting reality where necessary in order
to be persuasive.
Judgment and Decision Making
505
3. Squelching of dissent—those who disagree with the group are ignored, criticized,
or even ostracized.
4. Formation of a “mindguard” for the group—one person appoints himself or herself
the keeper of the group norm and ensures that people stay in line.
5. Feeling invulnerable—the group believes that it must be right, given the intelligence of its members and the information available to them.
6. Feeling unanimous—members believe that everyone unanimously shares the opinions expressed by the group.
Defective decision making results from groupthink, which in turn is due to examining alternatives insufficiently, examining risks inadequately, and seeking information about alternatives incompletely.
Consider how groupthink might arise in a decision when college students decide
to damage a statue on the campus of a football rival to teach a lesson to the students
and faculty in the rival university. The students rationalize that damage to a statue
really is no big deal. Who cares about an old ugly statue anyway? When one group
member dissents, other members quickly make him feel disloyal and cowardly. His
dissent is squelched. The group’s members feel invulnerable. They are going to damage the statue under the cover of darkness, and the statue is never guarded. They are
sure they will not be caught. Finally, all the members agree on the course of action.
This apparent feeling of unanimity convinces the group members that far from being
out of line, they are doing what needs to be done.
Antidotes for Groupthink
Janis has prescribed several antidotes for groupthink. For example, the leader of a
group should encourage constructive criticism, be impartial, and ensure that members seek input from people outside the group. The group should also form subgroups
that meet separately to consider alternative solutions to a single problem. It is important that the leader take responsibility for preventing spurious conformity to a group
norm.
In 1997, members of the Heaven’s Gate cult in California committed mass suicide in the hope of meeting up with extraterrestrials in a spaceship trailing the HaleBopp comet. Although this group suicide is a striking example of conformity to a
destructive group norm, similar events have occurred throughout human history,
such as the suicide of more than 900 members of the Jonestown, Guyana, religious
cult in 1978. In 2010, a series of incredibly bad decisions by a group of oil-rig operators
on the Deepwater Horizon, situated in the Gulf of Mexico, led to the largest oil-well
leak in history. And even in the 21st century, suicide bombers are killing themselves
and others in carefully planned attacks.
Neuroscience of Decision Making
As in problem solving, the prefrontal cortex, and particularly the anterior cingulate
cortex, is active during the decision-making process (Barraclough, Conroy, & Lee,
2004; Kennerley et al., 2006; Rogers et al., 2004). Explorations of decision making in
monkeys have noted activation in the parietal regions of the brain (Platt & Glimcher,
1999). The amount of gain associated with a decision also affects the amount of activation observed in the parietal region (Platt & Glimcher, 1999).
Examination of decision making in drug abusers identified a number of areas involved in risky decisions. The researchers studied drug abusers because drug abuse,
CHAPTER 12 • Decision Making and Reasoning
Erich Kaiser/AP Photos
506
In 1997, 39 members of the Heaven’s Gate cult committed mass suicide in order to “evacuate” Earth and
meet with a UFO that would lead them to a better existence.
by its very nature, produces risky decisions. They found decreased activation in the
left pregenual anterior cingulate cortex of drug abusers (Fishbein et al., 2005). These
findings suggest that during decision making, the anterior cingulate cortex is involved in the consideration of potential rewards.
Another study had healthy participants play the gambling game Blackjack. The
researchers found that suboptimal decisions (too risky or too cautious) were associated with increased activity in the anterior cingulate cortex (Hewig et al., 2008).
Another interesting effect seen in this area is observed in participants who have
difficulty with a decision. In one study, participants made decisions concerning
whether an item was old or new and which of two items was larger (Fleck et al.,
2006). Decisions that were rated lowest in confidence and that took the most time
to answer were associated with higher activation of the anterior cingulate cortex.
These findings suggest that this area of the brain is involved in the comparison
and weighing of possible solutions.
CONCEPT CHECK
1. Why can the model of the economic man and woman not explain human decision
making satisfactorily?
2. Why do we use heuristics?
3. What is the difference between overconfidence and hindsight bias?
4. Name and describe three fallacies.
5. What are the symptoms of groupthink?
6. Which parts of the brain play prominent roles in decision making?
Deductive Reasoning
507
Deductive Reasoning
Judgment and decision making involve evaluating opportunities and selecting one
choice over another. A related kind of thinking is reasoning. Reasoning is the process
of drawing conclusions from principles and from evidence (Leighton & Sternberg,
2004; Sternberg, 2004; Wason & Johnson-Laird, 1972). In reasoning, we move from
what is already known to infer a new conclusion or to evaluate a proposed conclusion.
Reasoning is often divided into two types: deductive and inductive reasoning.
We explore both kinds of reasoning in the remainder of this chapter.
What Is Deductive Reasoning?
Deductive reasoning is the process of reasoning from one or more general statements
regarding what is known to reach a logically certain conclusion (Johnson-Laird,
2000; Rips, 1999; Williams, 2000). It often involves reasoning from one or more
general statements regarding what is known to a specific application of the general
statement.
Deductive reasoning is based on logical propositions. A proposition is basically
an assertion, which may be either true or false. Examples are “Cognitive psychology
students are brilliant,” “Cognitive psychology students wear shoes,” or “Cognitive
psychology students like peanut butter.” In a logical argument, premises are propositions about which arguments are made. Cognitive psychologists are interested particularly in propositions that may be connected in ways that require people to draw
reasoned conclusions. That is, deductive reasoning is useful because it helps people
connect various propositions to draw conclusions. Cognitive psychologists want to
know how people connect propositions to draw conclusions. Some of these conclusions are well reasoned; others are not.
Much of the difficulty of reasoning is in even understanding the language of problems (Girotto, 2004). Some of the mental processes used in language understanding
and the cerebral functioning underlying them are used in reasoning, too (Lawson,
2004).
Conditional Reasoning
One type of deductive reasoning is conditional reasoning. In the next sections, we
will explore what conditional reasoning is and how it works.
What Is Conditional Reasoning?
One of the primary types of deductive reasoning is conditional reasoning, in which
the reasoner must draw a conclusion based on an if-then proposition. The conditional if-then proposition states that if antecedent condition p is met, then consequent event q follows. For example, “If students study hard, then they score high
on their exams.” Under some circumstances, if you have established a conditional
proposition, then you may draw a well-reasoned conclusion. The usual set of conditional propositions from which you can draw a well-reasoned conclusion is, “If p,
then q. p. Therefore, q.” This inference illustrates deductive validity. That is, it follows logically from the propositions on which it is based. The following is also
logical:
“If students eat pizza, then they score high on their exams. They eat pizza.
Therefore, they score high on their exams.”
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CHAPTER 12 • Decision Making and Reasoning
As you may have guessed, deductive validity does not equate with truth. You
can reach deductively valid conclusions that are completely untrue with respect to
the world. Whether the conclusion is true depends on the truthfulness of the
premises. In fact, people are more likely mistakenly to accept an illogical argument
as logical if the conclusion is factually true. For now, however, we put aside the issue
of truth and focus only on the deductive validity, or logical soundness, of the
reasoning.
One set of propositions and its conclusion is the argument:
“If p, then q. p.
Therefore, q,”
which is termed a modus ponens argument. In the modus ponens argument, the
reasoner affirms the antecedent (p). For example, take the argument “If you are a
husband, then you are married. Harrison is a husband. Therefore, he is married.”
The set of propositions for the modus ponens argument is shown in Table 12.2.
In addition to the modus ponens argument, you may draw another well-reasoned
conclusion from a conditional proposition, given a different second proposition:
“If p, then q. Not q. Therefore, not p.”
This inference is also deductively valid. This particular set of propositions and its
conclusion is termed a modus tollens argument, in which the reasoner denies the consequent. For example, we modify the second proposition of the argument to deny
the consequent:
“If you are a husband, then you are married. Harrison is not married. Therefore,
he is not a husband.”
Table 12.2 shows two conditions in which a well-reasoned conclusion can be
reached. It also shows two conditions in which such a conclusion cannot be reached.
Table 12.2
Conditional Reasoning: Deductively Valid Inferences and Deductive Fallacies
Two kinds of conditional propositions lead to valid deductions, and two others lead to deductive fallacies; p is called
the antecedent; q is called the consequent. ! stands for then, and \stands for therefore.
Type of Argument
Deductively
valid
inferences
Deductive
fallacies
Conditional Proposition
Existing Condition
Inference
Modus ponens—
affirming the
antecedent
p!q
If you are a mother, then
you have a child.
p
You are a mother.
∴q
Therefore, you have
a child.
Modus tollens—
denying the
consequent
p!q
If you are a mother, then
you have a child.
¬q
You do not have a
child.
∴¬p
Therefore, you are
not a mother.
Denying the
antecedent
p!q
If you are a mother, then
you have a child.
¬p
You are not a mother.
∴¬q
Therefore, you do
not have a child.
Affirming the
consequent
p!q
If you are a mother, then
you have a child.
q
You have a child.
∴p
Therefore, you are
a mother.
Deductive Reasoning
509
As the examples illustrate, some inferences based on conditional reasoning are fallacies, which lead to conclusions that are not deductively valid. When using conditional
propositions, we cannot reach a deductively valid conclusion based either on denying
the antecedent condition or on affirming the consequent. Let’s return to the proposition, “If you are a husband, then you are married.” We would not be able to confirm
or to refute the proposition based on denying the antecedent: “Joan is not a husband.
Therefore, she is not married.” Even if we ascertain that Joan is not a husband, we
cannot conclude that she is not married. Similarly, we cannot deduce a valid conclusion by affirming the consequent: “Joan is married. Therefore, she is a husband.” Even
if Joan is married, her spouse may not consider her a husband.
The Wason Selection Task
Conditional reasoning can be studied in the laboratory using a “selection task”
(Wason, 1968, 1969, 1983; Wason & Johnson-Laird, 1970, 1972). Participants are
presented with a set of four two-sided cards. Each card has a number on one side and
a letter on the other side. Face up are two letters and two numbers. The letters are a
consonant and a vowel. The numbers are an even number and an odd number. For
example, participants might be presented with the set of cards shown in Figure 12.1.
Each participant then is told a conditional statement. For example, “If a card
has a consonant on one side, then it has an even number on the other side.” The
task is to determine whether the conditional statement is true or false. One does so
by turning over the exact number of cards necessary to test the conditional statement. That is, the participant must not turn over any cards that are not valid tests
of the statement. But the participant must turn over all cards that are valid tests of
the conditional proposition. Which cards would you turn?
Table 12.3 illustrates the four possible tests participants might perform on the
cards. Two of the tests (modus ponens: affirming the antecedent, and modus tollens:
denying the consequent) are both necessary and sufficient for testing the conditional
statement:
• That is, to evaluate the deduction, the participant must turn over the card
showing a consonant to see whether it has an even number on the other side.
He or she thereby affirms the antecedent (the modus ponens argument).
• In addition, the participant must turn over the card showing an odd number (i.e.,
not an even number) to see whether it has a vowel (i.e., not a consonant) on the
other side. He or she thereby denies the consequent (the modus tollens argument).
The other two possible tests (denying the antecedent and affirming the consequent) are irrelevant. That is, the participant need not turn over the card showing a
S
Figure 12.1
3
A
2
Which two cards would you turn to confirm the rule, “If a card has a consonant on one side, then it has an even number on the other side”?
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CHAPTER 12 • Decision Making and Reasoning
Table 12.3
Conditional Reasoning: Wason’s Selection Task
In the Wason selection task, Peter Wason presented participants with a set of four cards, from which the participants
were to test the validity of a given proposition. This table illustrates how a reasoner might test the conditional proposition (p ! q), “If a card has a consonant on one side (p), then it has an even number on the other side (q).”
Proposition based on what shows
on the face of the card
Test
Type of Reasoning
p
A given card has a consonant on one
side (e.g., “S,” “F,” “V,” or “P”)
∴q
Does the card have an even number
on the other side?
Based on modus
ponens
¬q
A given card does not have an even
number on one side. That is, a given
card has an odd number on one side
(e.g., “3,” “5,” “7,” or “9”).
∴¬p
Does the card not have a consonant
on the other side? That is, does the
card have a vowel on the other side?
Based on modus
tollens
¬p
A given card does not have a consonant on one side. That is, a given card
has a vowel on one side (e.g., “A,”
“E,” “I,” or “O”).
∴¬q
Does the card not have an even
number on the other side? That is,
does the card have an odd number
on the other side?
q
A given card has an even number on
one side (e.g., “2,” “4,” “6,” or “8”).
∴p
Does the card have a consonant on
the other side?
Deductively
valid
inferences
Based on denying
the antecedent
Deductive
fallacies
Based on affirming
the consequent
vowel (i.e., not a consonant). To do so would be to deny the antecedent. He or she
also need not turn over the card showing an even number (i.e., not a odd number).
To do so would be to affirm the consequent.
Most participants knew to test for the modus ponens argument. However, many
participants failed to test for the modus tollens argument. Some of these participants
instead tried to deny the antecedent as a means of testing the conditional
proposition.
Conditional Reasoning in Everyday Life
Most people of all ages (at least starting in elementary school) appear to have little
difficulty in recognizing and applying the modus ponens argument. However, few people spontaneously recognize the need for reasoning by means of the modus tollens argument. Many people do not recognize the logical fallacies of denying the
antecedent or affirming the consequent, at least as these fallacies are applied to abstract reasoning problems (Braine & O’Brien, 1991; O’Brien, 2004; Rips, 1988,
1994). In fact, some evidence suggests that even people who have taken a course
in logic fail to demonstrate deductive reasoning across various situations (Cheng
et al., 1986). Even training aimed directly at improving reasoning leads to mixed
results. After training aimed at increasing reasoning, there is a significant increase
in the use of mental models and rules. However, after this training, there may be
only a moderate increase in the use of deductive reasoning (Leighton, 2006).
Why might both children and adults fallaciously affirm the consequent or deny
the antecedent? Perhaps they do so because of invited inferences that follow from
normal discourse comprehension of conditional phrasing (Rumain, Connell, &
Braine, 1983). For instance, suppose that a textbook publisher advertises,
Deductive Reasoning
511
“If you buy the Introduction to Ethics textbook, then we will give you a $5
rebate.”
You probably correctly infer that if you do not buy this textbook, the publisher
will not give you a $5 rebate. However, formal deductive reasoning would consider
this denial of the antecedent to be fallacious. The statement says nothing about
what happens if you do not buy the textbook. Similarly, you may infer that you
must have bought this textbook (affirm the consequent) if you received a $5 rebate
from the publisher. But the statement says nothing about the range of circumstances
that lead you to receive the $5 rebate. There may be other ways to receive it. Both
inferences are fallacious according to formal deductive reasoning, but both are quite
reasonably invited inferences in everyday situations. It helps when the wording of
conditional reasoning problems either explicitly or implicitly disinvites these inferences. People are then much less likely to engage in these logical fallacies.
The demonstration of conditional reasoning also is influenced by the presence
of contextual information that converts the problem from one of abstract deductive
reasoning to one that applies to an everyday situation. For example, participants received both the Wason Selection Task and a modified version of the Wason Selection Task (Griggs & Cox, 1982). In the modified version, the participants were
asked to suppose that they were police officers. As officers, they were attempting to
enforce the laws applying to the legal age for drinking alcoholic beverages. The particular rule to be enforced was:
“If a person is drinking beer, then the person must be over 19 years of age.”
Each participant was presented with a set of four cards:
(1)
(2)
(3)
(4)
drinking a beer
drinking a Coke
16 years of age
22 years of age.
The participant then was instructed to “Select the card or cards that you definitely need to turn over to determine whether or not the people are violating the
rule” (p. 414). On the one hand, none of Griggs and Cox’s participants had responded correctly on the abstract version of the Wason Selection Task. On the
other hand, a remarkable 72% of the participants correctly responded to the modified version of the task; that is, they turned cards 1 and 3.
Influences on Conditional Reasoning
A more recent modification of the task based on drinking and age has shown that
beliefs regarding plausibility influence whether people choose the modus tollens argument (denying the consequent—checking to see whether a person who is younger
than 19 years of age is not drinking beer). When the test involves checking to see
whether an 18-year-old is drinking beer, people are far more likely to try the modus
tollens argument than when they have to check whether a 4-year-old is drinking
beer. Nevertheless, the logical argument is the same in both cases (Kirby, 1994).
How do people use deductive reasoning in realistic situations? Two investigators
have suggested that, rather than using formal inference rules, people often use pragmatic reasoning schemas (Cheng & Holyoak, 1985). Pragmatic reasoning schemas
are general organizing principles or rules related to particular kinds of goals, such as
permissions, obligations, or causations. These schemas sometimes are referred to as pragmatic rules. These pragmatic rules are not as abstract as formal logical rules. Yet, they
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CHAPTER 12 • Decision Making and Reasoning
are sufficiently general and broad so that they can apply to a wide variety of specific
situations. Prior beliefs, in other words, matter in reasoning (Evans & Feeney, 2004).
Alternatively, one’s performance may be affected by perspective effects—that is,
whether one takes the point of view of the police officers or of the people drinking
the alcoholic beverages (Almor & Sloman, 1996; Staller, Sloman, & Ben-Zeev,
2000). So it may not be permissions per se that matter. Rather, what may matter
are the perspectives one takes when solving such problems.
Thus, consider situations in which our previous experiences or our existing knowledge cannot tell us all we want to know. Pragmatic reasoning schemas help us deduce
what might reasonably be true. Particular situations or contexts activate particular
schemas. For example, suppose that you are walking across campus and see someone
who looks extremely young. Then you see the person walk to a car. He unlocks it, gets
in, and drives away. This observation would activate your permission schema for driving: “If you are to be permitted to drive alone, then you must be at least 16 years old.”
You might now deduce that the person you saw is at least 16 years old. In one experiment, 62% of participants correctly chose modus ponens and modus tollens arguments
when the conditional-reasoning task was presented in the context of permission statements. Only 11% did so when the task was presented in the context of arbitrary statements unrelated to pragmatic reasoning schemas (Cheng & Holyoak, 1985).
Researchers conducted an extensive analysis comparing the standard abstract
Wason selection task with an abstract form of a permission problem (Griggs &
Cox, 1993). The standard abstract form might be “If a card has an ‘A’ on one side,
then it must have a ‘4’ on the other side.” The abstract permission form might be, “If
one is to take action ‘A,’ then one must first satisfy precondition ‘P.’ ” Performance
on the abstract-permission task was still superior (49% correct overall) to performance on the standard abstract task (only 9% correct overall) (Griggs & Cox,
1993; Manktelow & Over, 1990, 1992).
Evolution and Reasoning
A different approach to conditional reasoning takes an evolutionary view of cognition (Cummins, 2004). This view asks what kinds of thinking skills would provide a
naturally selective advantage for humans in adapting to our environment across evolutionary time (Cosmides, 1989; Cosmides & Tooby, 1996). To gain insight into
human cognition, we should look to see what kinds of adaptations would have
been most useful in the distant past. So we hypothesize on how human hunters
and gatherers would have thought during the millions of years of evolutionary time
that predated the relatively recent development of agriculture and the very recent
development of industrialized societies.
How has evolution influenced human cognition? Humans may possess something like a schema-acquisition device (Cosmides, 1989). It facilitates our ability to
quickly glean important information from our experiences. It also helps us to organize that information into meaningful frameworks. In Cosmides’ view, these schemas
are highly flexible. But they also are specialized for selecting and organizing the information that will most effectively aid us in adapting to the situations we face. One
of the distinctive adaptations shown by human hunters and gatherers has been in
the area of social exchange. There are two kinds of inferences in particular that
social-exchange schemas facilitate: inferences related to cost-benefit relationships
and inferences that help people detect when someone is cheating in a particular social exchange. In earlier times, detecting a cheater may have made the difference
between life and death.
Deductive Reasoning
513
Syllogistic Reasoning: Categorical Syllogisms
In addition to conditional reasoning, the other key type of deductive reasoning is
syllogistic reasoning, which is based on the use of syllogisms. Syllogisms are deductive arguments that involve drawing conclusions from two premises (Maxwell, 2005;
Rips, 1994, 1999). All syllogisms comprise a major premise, a minor premise, and a
conclusion. Unfortunately, sometimes the conclusion may be that no logical conclusion may be reached based on the two given premises.
What Are Categorical Syllogisms?
Probably the most well-known kind of syllogism is the categorical syllogism. Like
other kinds of syllogisms, categorical syllogisms comprise two premises and a conclusion. In the case of the categorical syllogism, the premises state something about the
category memberships of the terms. In fact, each term represents all, none, or some
of the members of a particular class or category. As with other syllogisms, each premise contains two terms. One of them must be the middle term, common to both premises. The first and the second terms in each premise are linked through the
categorical membership of the terms. That is, one term is a member of the class indicated by the other term. However the premises are worded, they state that some
(or all or none) of the members of the category of the first term are (or are not)
members of the category of the second term. To determine whether the conclusion
follows logically from the premises, the reasoner must determine the category memberships of the terms. An example of a categorical syllogism would be as follows:
All cognitive psychologists are pianists.
All pianists are athletes.
Therefore, all cognitive psychologists are athletes.
Logicians often use circle diagrams to illustrate class membership. They make it
easier to figure out whether a particular conclusion is logically sound. The conclusion for this syllogism does in fact follow logically from the premises. This is shown
in the circle diagram in Figure 12.2. However, the conclusion is false because the
premises are false. For the preceding categorical syllogism, the subject is cognitive
psychologists, the middle term is pianists, and the predicate is athletes. In both premises, we asserted that all members of the category of the first term were members
of the category of the second term.
There are four kinds of premises (see also Table 12.4):
1. Statements of the form “All A are B” sometimes are referred to as universal affirmatives, because they make a positive (affirmative) statement about all members
of a class (universal).
2. Universal negative statements make a negative statement about all members of a
class (e.g., “No cognitive psychologists are flutists.”).
3. Particular affirmative statements make a positive statement about some members
of a class (e.g., “Some cognitive psychologists are left-handed.”).
4. Particular negative statements make a negative statement about some members of
a class (e.g., “Some cognitive psychologists are not physicists.”).
In all kinds of syllogisms, some combinations of premises lead to no logically valid
conclusion. In categorical syllogisms, in particular, we cannot draw logically valid
conclusions from categorical syllogisms with two particular premises or with two
negative premises. For example, “Some cognitive psychologists are left-handed. Some
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Pianists
Athletes
Cognitive
psychologists
Pianists
Athletes
Pianists
Cognitive
psychologists
Figure 12.2 Circle Diagrams Representing a Categorical Syllogism.
Circle diagrams may be used to represent categorical syllogisms such as the one shown here: “All cognitive psychologists
are pianists. All pianists are athletes. Therefore, all cognitive psychologists are athletes.” It follows from the syllogism that
all cognitive psychologists are athletes. However, if the premises are not true, a deduction that is logically valid still is not
necessarily true, as is the case in this example.
Source: From In Search of the Human Mind, by Robert J. Sternberg. Copyright © 1995 by Harcourt Brace & Company. Reproduced
by permission of the publisher.
left-handed people are smart.” Based on these premises, you cannot conclude even
that some cognitive psychologists are smart. The left-handed people who are smart
might not be the same left-handed people who are cognitive psychologists. We just
don’t know. Consider a negative example: “No students are stupid. No stupid people
eat pizza.” We cannot conclude anything one way or the other about whether students
eat pizza based on these two negative premises. As you may have guessed, people
appear to have more difficulty (work more slowly and make more errors) when
trying to deduce conclusions based on one or more particular premises or negative
premises.
How Do People Solve Syllogisms?
Various theories have been proposed as to how people solve categorical syllogisms.
One of the earliest theories was the atmosphere bias (Begg & Denny, 1969;
Woodworth & Sells, 1935). There are two basic ideas of this theory:
Deductive Reasoning
Table 12.4
515
Categorical Syllogisms: Types of Premises
The premises of categorical syllogisms may be universal affirmatives, universal negatives, particular affirmatives, or
particular negatives.
Type of
Premise
Form of Premise
Statements
Description
Examples
Reversibility*
Universal
affirmative
All A are B.
The premise positively
(affirmatively) states that
all members of the first
class (universal) are
members of the second
class.
All men are
males.
All men are males 6¼
All males are men.
Non-reversible
All A are B 6¼
All B are A.
Universal
negative
No A are B.
(Alternative:
All A are not B.)
The premise states that
none of the members of
the first class are members of the second
class.
No men are
females.
or
All men are
not females.
No men are females ¼
No females are men.
$Reversible$
No A are B ¼
No B are A.
Particular
affirmative
Some A are B.
The premise states that
only some of the members of the first class are
members of the second
class.
Some
females are
women.
Some females are women 6¼
Some women are females.
Non-reversible
Some A are B 6¼
Some B are A.
Particular
negative
Some A are not B.
The premise states that
some members of the
first class are not members of the second
class.
Some women
are not
females.
Some women are not females 6¼
Some females are not women.
Non-reversible
Some A are not B 6¼
Some B are not A.
*In formal logic, the word some means “some and possibly all.” In common parlance, and as used in cognitive psychology, some means
“some and not all.” Thus, in formal logic, the particular affirmative also would be reversible. For our purposes, it is not.
1. If there is at least one negative in the premises, people will prefer a negative
solution.
2. If there is at least one particular in the premises, people will prefer a particular
solution. For example, if one of the premises is “No pilots are children,” people
will prefer a solution that has the word no in it.
Nonetheless, the theory does not account very well for large numbers of responses.
Other researchers focused attention on the conversion of premises (Chapman &
Chapman, 1959). Here, the terms of a given premise are reversed. People sometimes
believe that the reversed form of the premise is just as valid as the original form. The
idea is that people tend to convert statements like “If A, then B” into “If B, then
A.” They do not realize that the statements are not equivalent. These errors are
made by children and adults alike (Markovits, 2004).
A more widely accepted theory is based on the notion that people solve
syllogisms by using a semantic (meaning-based) process based on mental models
(Ball & Quayle, 2009; Espino et al., 2005; Johnson-Laird & Savary, 1999;
Johnson-Laird & Steedman, 1978). This view of reasoning as involving semantic
processes based on mental models may be contrasted with rule-based (“syntactic”)
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processes, such as those characterized by formal logic. A mental model is an internal
representation of information that corresponds analogously with whatever is being
represented (see Johnson-Laird, 1983). Some mental models are more likely to lead
to a deductively valid conclusion than are others. In particular, some mental models
may not be effective in disconfirming an invalid conclusion.
For example, in the Johnson-Laird study, participants were asked to describe
their conclusions and their mental models for the syllogism, “All of the artists are
beekeepers. Some of the beekeepers are clever. Are all artists clever?” One participant said, “I thought of all the little . . . artists in the room and imagined they all
had beekeeper’s hats on” (Johnson-Laird & Steedman, 1978, p. 77). Figure 12.3
shows two different mental models for this syllogism. As the figure shows, the choice
of a mental model may affect the reasoner’s ability to reach a valid deductive conclusion. Because some models are better than others for solving some syllogisms, a
person is more likely to reach a deductively valid conclusion by using more than
one mental model. In the figure, the mental model shown in (a) may lead to the
(a)
(b)
Figure 12.3 Mental Models Representing a Syllogism.
Philip Johnson-Laird and Mark Steedman hypothesized that people use various mental models analogously to represent
the items within a syllogism. Some mental models are more effective than others, and for a valid deductive conclusion
to be reached, more than one model may be necessary, as shown here. (See text for explanation.)
Deductive Reasoning
517
deductively invalid conclusion that some artists are clever. By observing the alternative model in (b), we can see an alternative view of the syllogism. It shows that the
conclusion that some artists are clever may not be deduced on the basis of this information alone. Specifically, perhaps the beekeepers who are clever are not the same
as the beekeepers who are artists.
As mentioned previously, circle diagrams are often used to represent categorical
syllogisms. In circle diagrams, you can use overlapping, concentric, or nonoverlapping circles to represent the members of different categories (see Figure
12.2). People can learn how to improve their reasoning by being taught how to
draw circle diagrams (Nickerson, 2004). Amazingly, even congenitally blind persons
are able to create spatial mental models to assist them in their reasoning processes
(Fleming et al., 2006; Knauff & May, 2006).
The difficulty of many problems of deductive reasoning relates to the number of
mental models needed for adequately representing the premises of the deductive argument (Johnson-Laird, Byrne, & Schaeken, 1992). Arguments that entail only one
mental model may be solved quickly and accurately. However, to infer accurate conclusions based on arguments that may be represented by multiple alternative models
is much harder. Such inferences place great demands on working memory (Gilhooly,
2004). In these cases, the individual must simultaneously hold in working memory
each of the various models. Only in this way can he or she reach or evaluate a conclusion. Thus, limitations of working-memory capacity may underlie at least some of
the errors observed in human deductive reasoning (Johnson-Laird, Byrne, & Schaeken, 1992).
In two experiments, the role of working memory was studied in syllogistic reasoning (Gilhooly et al., 1993). In the first, syllogisms were simply presented either
orally or visually. Oral presentation placed a considerably higher load on working
memory because participants had to remember the premises. In the visualpresentation condition, participants could look at the premises. As predicted, performance was lower in the oral-presentation condition. In a second experiment,
participants needed to solve syllogisms while at the same time performing another
task. Either the task drew on working-memory resources or it did not. The researchers found that the task that drew on working-memory resources interfered
with syllogistic reasoning. The task that did not draw on these resources did not.
Other factors also may contribute to the ease of forming appropriate mental
models. People seem to solve logical problems more accurately and more easily
when the terms have high imagery value (Clement & Falmagne, 1986).
Some deductive reasoning problems comprise more than two premises. For
example, transitive-inference problems, in which problem solvers must order
multiple terms, can have any number of premises linking large numbers of terms.
Mathematical and logical proofs are deductive in character and can have many
steps as well.
Aids and Obstacles to Deductive Reasoning
In deductive reasoning, as in many other cognitive processes, we engage in many
heuristic shortcuts. These shortcuts sometimes lead to inaccurate conclusions. In addition to these shortcuts, we often are influenced by biases that distort the outcomes
of our reasoning. In this section, we examine heuristics and biases in deductive reasoning. Finally, we look at ways to enhance your deductive reasoning skills.
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Heuristics in Deductive Reasoning
Heuristics in syllogistic reasoning include overextension errors. In these errors, we
overextend the use of strategies that work in some syllogisms to syllogisms in which
the strategies fail us. For example, although reversals work well with universal negatives, they do not work with other kinds of premises. We also experience foreclosure
effects when we fail to consider all the possibilities before reaching a conclusion. In
addition, premise-phrasing effects may influence our deductive reasoning, for example,
the sequence of terms or the use of particular qualifiers or negative phrasing.
Premise-phrasing effects may lead us to leap to a conclusion without adequately reflecting on the deductive validity of the syllogism.
Biases in Deductive Reasoning
Biases that affect deductive reasoning generally relate to the content of the premises and the believability of the conclusion. They also reflect the tendency toward confirmation bias. In confirmation bias, we seek confirmation rather than
disconfirmation of what we already believe. Suppose the content of the premises
and a conclusion seem to be true. In such cases, reasoners tend to believe in the
validity of the conclusion, even when the logic is flawed (Evans, Barston, & Pollard, 1983).
Confirmation bias can be detrimental and even dangerous in some circumstances. For instance, in an emergency room, if a doctor assumes that a patient has
condition X, the doctor may interpret the set of symptoms as supporting the diagnosis without fully considering all alternative interpretations (Pines, 2005). This shortcut can result in inappropriate diagnosis and treatment, which can be extremely
dangerous. Other circumstances where the effects of confirmation bias can be observed are in police investigations, paranormal beliefs, and stereotyping behavior
(Ask & Granhag, 2005; Biernat & Ma, 2005; Lawrence & Peters, 2004). To a lesser
extent, people also show the opposite tendency to disconfirm the validity of the
conclusion when the conclusion or the content of the premises contradicts the reasoner’s existing beliefs (Evans, Barston, & Pollard, 1983; Janis & Frick, 1943).
Enhancing Deductive Reasoning
To enhance our deductive reasoning, we may try to avoid heuristics and biases that
distort our reasoning. We also may engage in practices that facilitate reasoning. For
example, we may take longer to reach or to evaluate conclusions. Effective reasoners
also consider more alternative conclusions than do poor reasoners (Galotti, Baron,
& Sabini, 1986). In addition, training and practice seem to increase performance
on reasoning tasks. The benefits of training tend to be strong when the training relates to pragmatic reasoning schemas (Cheng et al., 1986) or to such fields as law
and medicine (Lehman, Lempert, & Nisbett, 1987). The benefits are weaker for abstract logical problems divorced from our everyday life (see Holland et al., 1986; Holyoak & Nisbett, 1988).
One factor that affects syllogistic reasoning is mood. When people are in a sad
mood, they tend to pay more attention to details (Schwarz & Skurnik, 2003). Perhaps surprisingly, they tend to do better in syllogistic reasoning tasks when they
are in a sad mood than when they are in a happy mood (Fiedler, 1988; Melton,
1995). People in a neutral mood tend to show performance in between the two
extremes.
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519
PRACTICAL APPLICATIONS OF COGNITIVE PSYCHOLOGY
IMPROVING YOUR DEDUCTIVE REASONING SKILLS
Even without training, you can improve your own deductive reasoning through developing
strategies to avoid making errors. For example, an unscrupulous politician might state that,
“We know that some suspicious-looking people are illegal aliens. We also know that some
illegal aliens are terrorists. Therefore, we can be sure that some of those people whom we
think are suspicious are terrorists, and that they are out to destroy our country!” The politician’s
syllogistic reasoning is wrong. If some A are B and some B are C, it is not necessarily the
case that any A are C. This is obvious when you realize that some men are happy people
and some happy people are women, but this does not imply that some men are women.
Make sure you are using the proper strategies in solving syllogisms. Remember that reversals only work with universal negatives. Sometimes translating abstract terms to concrete
ones (e.g., the letter C to cows) can help. Also, take the time to consider contrary examples
and create more mental models. The more mental models you use for a given set of premises, the more confident you can be that if your conclusion is not valid, it will be disconfirmed. Thus, the use of multiple mental models increases the likelihood of avoiding errors.
The use of multiple mental models also helps you to avoid the tendency to engage in confirmation bias. Circle diagrams also can be helpful in solving deductive-reasoning problems.
Is the use of fingerprints in solving a crime an example of deductive reasoning? Why or
why not?
CONCEPT CHECK
1. Which are deductively valid inferences in conditional reasoning?
2. What are categorical syllogisms?
3. How can mental models be helpful when solving categorical syllogisms?
4. What does “reversibility” mean with respect to premises?
5. Name some biases that we are prone to in deductive reasoning.
Inductive Reasoning
We now consider inductive reasoning in more detail. First, we discuss what inductive reasoning is. Next, we will explore how we make causal inferences. Last, we will
consider categorical inferences and reasoning by analogies.
What Is Inductive Reasoning?
Inductive reasoning is the process of reasoning from specific facts or observations to
reach a likely conclusion that may explain the facts. The inductive reasoner then
may use that probable conclusion to attempt to predict future specific instances
(Johnson-Laird, 2000). The key feature distinguishing inductive from deductive reasoning is that, in inductive reasoning, we never can reach a logically certain conclusion. We only can reach a particularly well-founded or probable conclusion. With
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deductive reasoning, in contrast, reaching logically certain—deductively valid—conclusions is possible.
For example, suppose that you notice that all the people enrolled in your cognitive psychology course are on the dean’s list (or honor roll). From these observations, you could reason inductively that all students who enroll in cognitive
psychology are excellent students (or at least earn the grades to give that impression). However, unless you can observe the grade-point averages of all people who
ever have taken or ever will take cognitive psychology, you will be unable to prove
your conclusion. Furthermore, a single poor student who happened to enroll in a
cognitive psychology course would disprove your conclusion. Still, after large numbers of observations, you might conclude that you had made enough observations to
reason inductively.
The fundamental riddle of induction is how we can make any inductions at all.
As the future has not happened, how can we predict what it will bring? There is also
an important so-called new riddle of induction (Goodman, 1983). Given possible
alternative futures, how do we know which one to predict? For example, in the number series problem 2, 4, 6, ?, most people would replace the question mark with an 8.
But we cannot know for sure that the correct number is 8. A mathematical formula
could be proposed that would yield any number at all as the next number. So why
choose the pattern of ascending even numbers? Partly we choose it because it seems
simple to us. It is a less complex formula than others we might choose. And partly
we choose it because we are familiar with it. We are used to ascending series of even
numbers. But we are not used to other complex series in which 2, 4, 6, may be embedded, such as 2, 4, 6, 10, 12, 14, 18, 20, 22, and so forth.
Inductive reasoning forms the basis of the empirical method (Holyoak & Nisbett, 1998). In it, we cannot logically leap from saying, “All observed instances to
date of X are Y” to saying, “Therefore, all X are Y.” It is always possible that the
next observed X will not be a Y. For example, you may say that all swans that you
have ever seen are white. However, you cannot form the conclusion then that all
swans are white because the next swan you happen upon might be black. Indeed,
black swans do exist.
In research, when we reject the null hypothesis (the hypothesis of no difference), we use inductive reasoning. We never know for sure whether we are correct
in rejecting a null hypothesis.
Cognitive psychologists probably agree on at least two of the reasons why people
use inductive reasoning. First, it helps them to become increasingly able to make
sense out of the great variability in their environment. Second, it also helps them
to predict events in their environment, thereby reducing their uncertainty. Thus,
cognitive psychologists seek to understand the how rather than the why of inductive
reasoning. We may (or may not) have some innate schema-acquisition device. But
we certainly are not born with all the inferences we manage to induce.
We already have implied that inductive reasoning often involves the processes
of generating and testing hypotheses. In addition, we reach inferences by generalizing some broad understandings from a set of specific instances. As we observe additional instances, we further broaden our understanding. Or, we may infer
specialized exceptions to the general understandings. For example, after observing
quite a few birds, we may infer that birds can fly. But after observing penguins
and ostriches, we may add to our generalized knowledge specialized exceptions for
flightless birds.
Inductive Reasoning
521
Causal Inferences
One approach to studying inductive reasoning is to examine causal inferences—
how people make judgments about whether something causes something else
(Cheng, 1997, 1999; Spellman, 1997). The philosopher David Hume observed that
we are most likely to infer causality when we observe covariation over time: First
one thing happens, then another. If we see the two events paired enough, we may
come to believe that the first causes the second.
Perhaps our greatest failing is one that extends to psychologists, other scientists,
and non-scientists: We demonstrate confirmation bias, which may lead us to errors
such as illusory correlations (Chapman & Chapman, 1967, 1969, 1975). Furthermore, we frequently make mistakes when attempting to determine causality based
on correlational evidence alone. Correlational evidence cannot indicate the direction
of causation. Suppose we observe a correlation between Factor A and Factor B. We
may find one of three things:
1. it may be that Factor A causes Factor B;
2. it may be that Factor B causes Factor A; or
3. some higher order, Factor C, may be causing both Factors A and B to occur
together.
Based on the correlational data we cannot determine which of the three options indeed causes the observed phenomenon.
A related error occurs when we fail to recognize that many phenomena have
multiple causes. For example, a car accident often involves several causes. It may
have originated with the negligence of several drivers, rather than just one. Once
we have identified one of the suspected causes of a phenomenon, we may commit
what is known as a discounting error. We stop searching for additional alternative or
contributing causes.
Confirmation bias can have a major effect on our everyday lives. For example, we
may meet someone, expecting not to like her. As a result, we may treat her in ways that
are different from how we would treat her if we expected to like her. She then may respond to us in less favorable ways. She thereby “confirms” our original belief that she is
not likable. Confirmation bias thereby can play a major role in schooling. Teachers
often expect little of students when they think them low in ability. The students then
give the teachers little. The teachers’ original beliefs are thereby “confirmed” (Sternberg,
1997). This effect is referred to as a self-fulfilling prophecy (Harber & Jussim, 2005).
Categorical Inferences
On what basis do people draw inferences? People generally use both bottom-up strategies and top-down strategies for doing so (Holyoak & Nisbett, 1988). That is, they
use both information from their sensory experiences and information based on what
they already know or have inferred previously. Bottom-up strategies are based on observing various instances and considering the degree of variability across instances.
From these observations, we abstract a prototype (see Chapters 8 and 9). Once a
prototype or a category has been induced, the individual may use focused sampling
to add new instances to the category. He or she focuses chiefly on properties that
have provided useful distinctions in the past. Top-down strategies include selectively
searching for constancies within many variations and selectively combining existing
concepts and categories.
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Reasoning by Analogy
Inductive reasoning may be applied to a broader range of situations than those requiring causal or categorical inferences. For example, inductive reasoning may be applied to reasoning by analogy. Consider an example analogy problem:
Fire is to asbestos as water is to: (a) vinyl, (b) air, (c) cotton, (d) faucet.
In reasoning by analogy, the reasoner must observe the first pair of items (“fire”
and “asbestos” in this example) and must induce from those two items one or more
relations (in this case, surface resistance because surfaces coated with asbestos can
resist fire). The reasoner then must apply the given relation in the second part of
the analogy. In the example analogy, the reasoner chooses the solution to be “vinyl”
because surfaces coated with vinyl can resist water.
Some investigators have used reaction-time methodology to figure out how people solve induction problems. For example, using mathematical modeling you might
be able to break down the amounts of time participants spent on various processes of
analogical reasoning. Most of the time spent in solving simple verbal analogies is
spent in encoding the terms and in responding (Sternberg, 1977). Only a small
part actually is spent in doing reasoning operations on these encodings.
The difficulty of encoding can become even greater in various puzzling analogies. For example, in the analogy:
RAT : TAR :: BAT : (a. CONCRETE, b. MAMMAL, c. TAB, d. TAIL),
the difficulty is in encoding the analogy as one involving letter reversal rather than
semantic content for its solution. In a problematic analogy such as the following, the
difficulty is in recognizing the meanings of the words:
AUDACIOUS : TIMOROUS :: MITIGATE :
(a. ADUMBRATE, b. EXACERBATE, c. EXPOSTULATE, d. EVISCERATE)
If reasoners know the meanings of the words, they probably will find it relatively
easy to figure out that the relation is one of antonyms. (Did this example audaciously exacerbate your difficulties in solving problems involving analogies?)
An application of analogies in reasoning can be seen in politics. Analogies can
help governing bodies come to conclusions (Breuning, 2003). These analogies also
can be effectively used to conveying the justification of the decision to the public
(Breuning, 2003). However, the use of analogies is not always successful. This highlights both the utility and possible pitfalls of using analogies in political deliberation.
In 2010, opponents of the war in Afghanistan drew an analogy to Vietnam to argue
for withdrawing from Afghanistan. They asserted that the failure of U.S. policies to lead
to a conclusive victory were analogous between Vietnam and Afghanistan. Some members of government then turned the tables, using an analogy to Vietnam to argue that
withdrawal from Afghanistan could lead to mass slaughter, as they asserted happened in
Vietnam after the Americans left. Thus, analogies can end up being largely in the eye of
the beholder rather than in the actual elements being compared.
Analogies are also used in everyday life as we make predictions about our environment. We connect our perceptions with our memories by means of analogies.
The analogies then activate concepts and items stored in our mind that are similar
to the current input. Through this activation, we can then make a prediction of
what is likely in a given situation (Bar, 2007). For example, predictions about global
warming are being guided in part by people drawing analogies to times in the past
when the people believed either that the atmosphere warmed up or did not.
An Alternative View of Reasoning
523
Whether a given individual believes in global warming depends in part upon what
analogy or analogies the individual decides to draw.
CONCEPT CHECK
1. What is inductive reasoning?
2. Which strategies do people use to draw inferences?
3. What is an analogy?
4. What leads analogies to succeed or fail?
An Alternative View of Reasoning
By now you have reasonably inferred that cognitive psychologists often disagree—
sometimes rather heatedly—about how and why people reason as they do. An alternative perspective on reasoning, dual-process theory, contends that two complementary
systems of reasoning can be distinguished. The first is an associative system, which
involves mental operations based on observed similarities and temporal contiguities
(i.e., tendencies for things to occur close together in time). The second is a rule-based
system, which involves manipulations based on the relations among symbols (Barrett,
Tugade, & Engle, 2004; Sloman, 1996).
The associative system can lead to speedy responses that are highly sensitive to
patterns and to general tendencies. Through this system, we detect similarities between observed patterns and patterns stored in memory. We may pay more attention
to salient features (e.g., highly typical or highly atypical ones) than to defining features of a pattern. This system imposes rather loose constraints that may inhibit the
selection of patterns that are poor matches to the observed pattern. It favors remembered patterns that are better matches to the observed pattern. An example of associative reasoning is use of the representativeness heuristic.
Another example is the belief-bias effect in syllogistic reasoning (Markovits et al.,
2009; Tsujii et al., 2010). This effect occurs when we agree more with syllogisms
that affirm our beliefs, whether or not these syllogisms are logically valid. An example of the workings of the associative system may be in the false-consensus effect.
Here, people believe that their own behavior and judgments are more common
and more appropriate than those of other people (Ross, Greene, & House, 1977).
Suppose people have an opinion on an issue. They are likely to believe that because
it is their opinion, it is likely to be shared and believed to be correct by others
(Dawes & Mulford, 1996; Krueger, 1998). Associating others’ views with our own
simply because they are our own is a questionable practice, however.
The rule-based system of reasoning usually requires more deliberate, sometimes
painstaking procedures for reaching conclusions. Through this system, we carefully
analyze relevant features (e.g., defining features) of the available data, based on rules
stored in memory. This system imposes rigid constraints that rule out possibilities
that violate the rules. Evidence in favor of rule-based reasoning includes:
1. We can recognize logical arguments when they are explained to us.
2. We can recognize the need to make categorizations based on defining features
despite similarities in typical features. For example, we can recognize that a
coin with a 3-inch diameter, which looks exactly like a quarter, must be a
counterfeit.
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3. We can rule out impossibilities, such as cats conceiving and giving birth to
puppies.
4. We can recognize many improbabilities. For example, it is unlikely that the U.S.
Congress will pass a law that provides annual salaries to all full-time college
students.
According to Sloman, we need both complementary systems. We need to respond quickly and easily to everyday situations, based on observed similarities and
temporal contiguities. Yet we also need a means for evaluating our responses more
deliberately.
The two systems may be conceptualized within a connectionist framework (Sloman, 1996). The associative system is represented easily in terms of pattern activation and inhibition, which readily fits the connectionist model. The rule-based
system may be represented as a system of production rules (see Chapter 8).
An alternative connectionist view suggests that deductive reasoning may occur
when a given pattern of activation in one set of nodes (e.g., those associated with a
particular premise or set of premises) entails or produces a particular pattern of activation in a second set of nodes (Rips, 1994). Similarly, a connectionist model of
inductive reasoning may involve the repeated activation of a series of similar patterns across various instances. This repeated activation then may strengthen the
links among the activated nodes. It thereby leads to generalization or abstraction of
the pattern for a variety of instances.
Connectionist models of reasoning and the other approaches described in this
chapter offer diverse views of the available data regarding how we reason and make
judgments. At present, no one theoretical model explains all the data well. But each
model explains at least some of the data satisfactorily. Together, the theories help us
understand human intelligence and cognition.
Consider a concrete example of the interface between intelligence and cognition in Investigating Cognitive Psychology: When There Is No “Right” Choice.
CONCEPT CHECK
1. What are the two complementary systems of reasoning?
2. How does a connectionist model conceptualize deductive reasoning?
Neuroscience of Reasoning
As in both problem solving and decision making, the process of reasoning involves
the prefrontal cortex (Bunge et al., 2004). Further, reasoning involves brain areas
associated with working memory, such as the basal ganglia (Melrose, Poulin, &
Stern, 2007). One would expect working memory to be involved because reasoning
involves the integration of information (which needs to be held in working memory
while it is being integrated).
The basal ganglia are involved in a variety of functions, including cognition and
learning. This area is also associated with the prefrontal cortex through a variety of
connections (Melrose, Poulin, & Stern, 2007).
However, when a person is presented with a statement that is either to be remembered, on the one hand, or to be used for reasoning, on the other, the processes
Neuroscience of Reasoning
525
INVESTIGATING COGNITIVE PSYCHOLOGY
When There Is No “Right” Choice
Consider this passage from Shakespeare’s Macbeth:
First Apparition: Macbeth! Macbeth! Beware Macduff; Beware the thane of Fife.
Dismiss me: enough….
Second Apparition: Be bloody, bold, and resolute; laugh to scorn the power of
man, for none of woman born shall harm Macbeth.
Macbeth: Then live, Macduff: what need I fear of thee? But yet I’ll make assurance double sure, and take a bond of fate: thou shalt not live; that I may tell
pale-hearted fear it lies, and sleep in spite of thunder.
In this passage, Macbeth mistakenly took the Second Apparition’s vision to mean
that no man could kill him, so he boldly decided to confront Macduff. However, Macduff was born by abdominal (Cesarean) delivery, so he did not fall into the category of
men who could not harm Macbeth. Macduff eventually killed Macbeth because Macbeth came to a wrong conclusion based on the Second Apparition’s premonition. The
First Apparition’s warning about Macduff should have been heeded.
Suppose you are trying to decide between buying an SUV or a subcompact car. You
would like the room of the SUV, but you would like the fuel efficiency of the subcompact
car. Whichever one you choose, did you make the right choice? This is a difficult question
to answer because most of our decisions are made under conditions of uncertainty. Thus,
let us say that you bought the SUV. You can carry a number of people, you have the
power to pull a trailer easily up a hill, and you sit higher so your road vision is much better. However, every time you fill up the gas tank, you are reminded of how much fuel this
vehicle takes. On the other hand, let us say that you bought the subcompact car. When
picking up friends at the airport, you have difficulty fitting all of them and their luggage;
you cannot pull trailers up hills (or at least, not very easily); and you sit so low that when
there is an SUV in front of you, you can hardly see what is on the road. However, every
time you fill up your gas tank or hear someone with an SUV complaining about how much
it costs to fill up his or her tank, you see how little you have to pay for gas. Again, did you
make the right choice? There are no “right” or “wrong” answers to most of the decisions
we make. We use our best judgment at the time of our decisions and think that they are
more nearly right than wrong as opposed to definitively right or wrong.
in the brain do differ somewhat. This means there may be more going on than encoding for recall when a person knows he or she will have to reason with a statement. In particular, for syllogistic reasoning, the left lateral frontal lobe (Broca’s
areas 44 and 45) is more active than when a statement just needs to be remembered.
This activation cannot be found for processing of conditional premises.
While people were engaged in the integration of the information (solving the
syllogistic and conditional reasoning problems), the left fronto-lateral cortex as well
as the basal ganglia were activated for both conditional and syllogistic reasoning.
However, syllogistic reasoning also involved activation in the lateral parietal cortex,
precuneus, and left ventral fronto-lateral cortex (Reverberi et al., 2010). Thus, syllogistic and conditional reasoning seem to involve processing in different parts of the
brain.
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CHAPTER 12 • Decision Making and Reasoning
Exploration of conditional reasoning through event-related potential (ERP)
methods revealed an increased negativity in the anterior cingulate cortex approximately 600 milliseconds and 2,000 milliseconds after task presentation (Qui et al.,
2007). This negativity suggests increased cognitive control, as would be expected
in a reasoning task.
In one study exploring moral reasoning in persons who show antisocial behaviors indicative of poor moral reasoning, malfunctions were noted in several areas
within the prefrontal cortex, including the dorsal and ventral regions (Raine &
Yang, 2006). Additionally, impairments in the amygdala, hippocampus, angular gyrus, anterior cingulate, and temporal cortex were also observed. Recall that the anterior cingulate is involved in decision making and the hippocampus is involved in
working memory. Therefore, it is to be expected that malfunctions in these areas
would result in deficiencies in reasoning.
CONCEPT CHECK
1. Which parts of the brain are prominently involved in reasoning processes?
2. Why can we expect that the parts of the brain that are involved in working memory are
also active during reasoning?
Key Themes
Several of the themes discussed in Chapter 1 are relevant to this chapter.
Rationalism versus empiricism. One way of understanding errors in syllogistic
reasoning is in terms of the particular logical error made, independently of the mental
processes the reasoner has used. For example, affirming the consequent is a logical error. One need do no empirical research to understand at the level of symbolic logic
the errors that have been made. Moreover, deductive reasoning is itself based on rationalism. A syllogism such as, “All toys are chairs. All chairs are hot dogs. Therefore, all
toys are hot dogs,” is logically valid but factually incorrect. Thus, deductive logic can
be understood at a rational level, independently of its empirical content. But if we
wish to know psychologically why people make errors or what is factually true, then
we need to combine empirical observations with rational logic.
Domain generality versus domain specificity. The rules of deductive logic apply
equally in all domains. One can apply them, for example, to abstract or to concrete
content. But research has shown that, psychologically, deductive reasoning with
concrete content is easier than reasoning with abstract content. So although the
rules apply in exactly the same way generally across domains, ease of application is
not psychologically equivalent across those domains.
Nature versus nurture. Are people preprogrammed to be logical thinkers? Piaget, the famous Swiss cognitive developmental psychologist, believed so. He believed
that the development of logical thinking follows an inborn sequence of stages that
unfold over time. According to Piaget, there is not much one can do to alter either
the sequence or timing of these stages. But research has suggested that the sequence
Piaget proposed does not unfold as he thought. For example, many people never
reach his highest stage, and some children are able to reason in ways he would not
have predicted they would be able to reason until they were older. So once again,
nature and nurture interact.
Summary
527
Summary
1. What are some of the strategies that guide
human decision making? Early theories were designed to achieve practical mathematical models
of decision making and assumed that decision
makers are fully informed, infinitely sensitive to
information, and completely rational. Subsequent
theories began to acknowledge that humans often use subjective criteria for decision making,
that chance elements often influence the outcomes of decisions, that humans often use subjective estimates for considering the outcomes, and
that humans are not boundlessly rational in making decisions. People apparently often use satisficing strategies, settling for the first minimally
acceptable option, and strategies involving a process of elimination by aspects to eliminate an
overabundance of options.
One of the most common heuristics most of us
use is the representativeness heuristic. We fall
prey to the fallacious belief that small samples
of a population resemble the whole population
in all respects. Our misunderstanding of base rates
and other aspects of probability often leads us to
other mental shortcuts as well, such as in the
conjunction fallacy and the inclusion fallacy.
Another common heuristic is the availability
heuristic, in which we make judgments based on
information that is readily available in memory,
without bothering to seek less available information. The use of heuristics, such as anchoring
and adjustment, illusory correlation, and framing effects, also often impairs our ability to
make effective decisions.
Once we have made a decision (or better yet,
another person has made a decision) and the
outcome of the decision is known, we may engage in hindsight bias, skewing our perception of
the earlier evidence in light of the eventual outcome. Perhaps the most serious of our mental
biases, however, is overconfidence, which seems
to be amazingly resistant to evidence of our own
errors.
2. What are some of the forms of deductive reasoning that people may use, and what factors
facilitate or impede deductive reasoning? Deductive reasoning involves reaching conclusions
from a set of conditional propositions or from a
syllogistic pair of premises. Among the various
types of syllogisms are linear syllogisms and categorical syllogisms. In addition, deductive reasoning may involve complex transitiveinference problems or mathematical or logical
proofs involving large numbers of terms. Also,
deductive reasoning may involve the use of
pragmatic reasoning schemas in practical, everyday situations.
In drawing conclusions from conditional propositions, people readily apply the modus ponens
argument, particularly regarding universal affirmative propositions. Most of us have more difficulty, however, in using the modus tollens
argument and in avoiding deductive fallacies,
such as affirming the consequent or denying the
antecedent, particularly when faced with propositions involving particular propositions or negative propositions.
In solving syllogisms, we have similar difficulties with particular premises and negative premises and with terms that are not presented in
the customary sequence. Frequently, when trying
to draw conclusions, we overextend a strategy
from a situation in which it leads to a deductively
valid conclusion to one in which it leads to a
deductive fallacy. We also may foreclose on a
given conclusion before considering the full
range of possibilities that may affect the conclusion. These mental shortcuts may be exacerbated
by situations in which we engage in confirmation
bias (tending to confirm our own beliefs).
We can enhance our ability to draw wellreasoned conclusions in many ways, such as by
taking time to evaluate the premises or propositions carefully and by forming multiple mental
models of the propositions and their relationships. We also may benefit from training and
practice in effective deductive reasoning. We
are particularly likely to reach well-reasoned
conclusions when such conclusions seem plausible and useful in pragmatic contexts, such as
during social exchanges.
3. How do people use inductive reasoning to reach
causal inferences and to reach other types
of conclusions? Although we cannot reach logically certain conclusions through inductive reasoning, we can at least reach highly probable
conclusions through careful reasoning. When
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CHAPTER 12 • Decision Making and Reasoning
making categorical inferences, people tend to use
both top-down and bottom-up strategies. Processes of inductive reasoning generally form the
basis of scientific study and hypothesis testing as a
means to derive causal inferences. In addition, in
reasoning by analogy people often spend more
time encoding the terms of the problem than in
performing the inductive reasoning. Reasoning
by analogy can lead to better conclusions, but
also to worse ones if the analogy is weak or based
on faulty assumptions. It appears that people
sometimes may use reasoning based on formalrule systems, such as by applying rules of formal
logic, and sometimes use reasoning based on associations, such as by noticing similarities and
temporal contiguities.
4. Are there any alternative views of reasoning?
A number of scientists have suggested that people have two distinct systems of reasoning: an
associative system that is sensitive to observed
similarities and temporal contiguities and a
rule-based system that involves manipulations
based on relations among symbols. The two systems can work together to help us reach reasonable conclusions in an efficient way.
Thinking about Thinking: Analytical, Creative,
and Practical Questions
1. Describe some of the heuristics and biases people use while making judgments or reaching
decisions.
2. What are the two logical arguments and the two
logical fallacies associated with conditional reasoning, as in the Wason Selection Task?
3. Which of the various approaches to conditional
reasoning seems best to explain the available
data? Give reasons for your answer.
4. Some cognitive psychologists question the
merits of studying logical formalisms such as
linear or categorical syllogisms. What do you
think can be gained by studying how people
reason in regard to syllogisms?
5. Based on the information in this chapter, design
a way to help high school students more effectively apply deductive reasoning to the problems
they face.
6. Design a question, such as the ones used by
Kahneman and Tversky, which requires people
to estimate subjective probabilities of two different events. Indicate the fallacies that you may
expect to influence people’s estimates, or tell
why you think people would give realistic estimates of probability.
7. Suppose that you need to rent an apartment.
How would you go about finding one that most
effectively meets your requirements and your
preferences? How closely does your method resemble the methods described by subjective expected utility theory, by satisficing, or by
elimination by aspects?
8. Give two examples showing how you use rulebased reasoning and associative reasoning in
your everyday experiences. In what kinds of
instances do you believe each type of reasoning
works better, or not as well?
Key Terms
availability heuristic, p. 494
base rate, p. 494
bounded rationality, p. 491
categorical syllogism, p. 513
causal inferences, p. 521
conditional reasoning, p. 507
confirmation bias, p. 518
deductive reasoning, p. 507
deductive validity, p. 508
elimination by aspects, p. 492
fallacy, p. 489
heuristics, p. 490
hindsight bias, p. 498
illusory correlation, p. 497
inductive reasoning, p. 519
judgment and decision
making, p. 489
mental model, p. 516
overconfidence, p. 498
pragmatic reasoning
schema, p. 511
premises, p. 507
proposition, p. 507
reasoning, p. 507
representativeness, p. 493
satisficing, p. 491
subjective probability, p. 490
subjective utility, p. 490
syllogisms, p. 513
Media Resources
529
Media Resources
Visit the companion website—www.cengagebrain.com—for quizzes, research articles, chapter outlines, and more.
Explore CogLab by going to http://coglab.wadsworth.com. To learn more, examine the following experiments:
Risky Decisions
Typical Reasoning
Wason Selection Task
Glossary
accessibility the degree to which we can gain access to the
available information
ACT Adaptive Control of Thought. In his ACT model,
John Anderson synthesized some of the features of serial
information-processing models and some of the features
of semantic-network models. In ACT, procedural
knowledge is represented in the form of production systems. Declarative knowledge is represented in the form
of propositional networks
ACT-R a model of information processing that integrates
a network representation for declarative knowledge
and a production-system representation for procedural
knowledge
agnosia a severe deficit in the ability to perceive sensory
information
algorithms sequences of operations that may be repeated over
and over again and that, in theory, guarantee the solution to a problem
Alzheimer’s disease a disease of older adults that causes
dementia as well as progressive memory loss
amacrine cells along with horizontal cells, they make single
lateral connections among adjacent areas of the retina
in the middle layer of cells
amnesia severe loss of explicit memory
amygdala plays an important role in emotion, especially in
anger and aggression
analog codes a form of knowledge representation that
preserves the main perceptual features of whatever is
being represented for the physical stimuli we observe
in our environment
analysis breaking down the whole of a complex problem into
manageable elements
anterograde amnesia the inability to remember events that
occur after a traumatic event
aphasia an impairment of language functioning caused by
damage to the brain
arousal a degree of physiological excitation, responsivity, and
readiness for action, relative to a baseline
artifact categories groupings that are designed or invented by
humans to serve particular purposes or functions
artificial intelligence (AI) the attempt by humans to construct systems that show intelligence and, particularly,
the intelligent processing of information; intelligence in
symbol-processing systems such as computers
associationism examines how events or ideas can become
associated with one another in the mind to result in a
form of learning
530
attention the active cognitive processing of a limited amount
of information from the vast amount of information
available through the senses, in memory, and through
cognitive processes; focus on a small subset of available
stimuli
autobiographical memory refers to memory of an individual’s
history
automatic processes involve no conscious control
automatization the process by which a procedure changes
from being highly conscious to being relatively automatic; also termed proceduralization
availability the presence of information stored in long-term
memory
availability heuristic cognitive shortcut that occurs when we
make judgments on the basis of how easily we can call
to mind what we perceive as relevant instances of a
phenomenon
axon the part of the neuron through which intraneuronal
conduction occurs (via the action potential) and at
the terminus of which is located the terminal buttons
that release neurotransmitters
base rate refers to the prevalence of an event or characteristic
within its population of events or characteristics
basic level degree of specificity of a concept that seems to be
a level within a hierarchy that is preferred to other
levels; sometimes termed natural level
behaviorism a theoretical outlook that psychology should
focus only on the relation between observable behavior,
on the one hand, and environmental events or stimuli,
on the other
bilinguals people who can speak two languages
binaural presentation presenting the same two messages, or
sometimes just one message, to both ears simultaneously
binocular depth cues based on the receipt of sensory information in three dimensions from both eyes
bipolar cells make dual connections forward and outward to
the ganglion cells, as well as backward and inward to the
third layer of retinal cells
blindsight traces of visual perceptual ability in blind areas
bottleneck theories theories proposing a bottleneck that
slows down information passing through
bottom-up theories data-driven (i.e., stimulus-driven)
theories
bounded rationality belief that we are rational, but within
limits
brain the organ in our bodies that most directly controls our
thoughts, emotions, and motivations
Glossary
brainstem connects the forebrain to the spinal cord
categorical perception discontinuous categories of speech
sounds
categorical syllogism a deductive argument in which the
relationship among the three terms in the two premises
involves categorical membership
category a concept that functions to organize or point out
aspects of equivalence among other concepts based on
common features or similarity to a prototype
causal inferences how people make judgments about
whether something causes something else
central executive both coordinates attentional activities and
governs responses
cerebellum controls bodily coordination, balance, and
muscle tone, as well as some aspects of memory involving procedure-related movements; from Latin, “little
brain”
cerebral cortex forms a 1- to 3-millimeter layer that wraps
the surface of the brain somewhat like the bark of a
tree wraps around the trunk
cerebral hemispheres the two halves of the brain
change blindness the inability to detect changes in objects or
scenes that are being viewed
characteristic features qualities that describe (characterize or
typify) the prototype but are not necessary for it
coarticulation occurs when phonemes or other units are produced in a way that overlaps them in time
cocktail party problem the process of tracking one conversation in the face of the distraction of other conversations
cognitive maps internal representations of our physical environment, particularly centering on spatial relationships
cognitive neuroscience the field of study linking the brain
and other aspects of the nervous system to cognitive
processing and, ultimately, to behavior
cognitive psychology the study of how people perceive,
learn, remember, and think about information
cognitive science a cross-disciplinary field that uses ideas and
methods from cognitive psychology, psychobiology, artificial intelligence, philosophy, linguistics, and anthropology
cognitivism the belief that much of human behavior can be
understood in terms of how people think
communication exchange of thoughts and feelings
comprehension processes used to make sense of the text as a
whole
concept an idea about something that provides a means of
understanding the world
conditional reasoning occurs when the reasoner must draw a
conclusion based on an if-then proposition
cones one of the two kinds of photoreceptors in the eye; less
numerous, shorter, thicker, and more highly concentrated
in the foveal region of the retina than in the periphery of
the retina than are rods (the other type of photoreceptor); virtually nonfunctional in dim light, but highly
effective in bright light and essential to color vision
531
confirmation bias the tendency to seek confirmation rather
than disconfirmation of what we already believe
confounding variable a type of irrelevant variable that has
been left uncontrolled in a study
conjunction search looking for a particular combination
(conjunction: joining together) of features
connectionist models according to connectionist models, we
handle very large numbers of cognitive operations at
once through a network distributed across incalculable
numbers of locations in the brain
connotation a word’s emotional overtones, presuppositions,
and other non-explicit meanings
consciousness includes both the feeling of awareness and the
content of awareness
consolidation the process of integrating new information into
stored information
constructive prior experience affects how we recall things
and what we actually recall from memory
constructive perception the perceiver builds (constructs) a
cognitive understanding (perception) of a stimulus; he
or she uses sensory information as the foundation for
the structure but also uses other sources of information
to build the perception
content morphemes the words that convey the bulk of the
meaning of a language
context effects the influences of the surrounding environment on perception
contextualism belief that intelligence must be understood in
its real-world context
contralateral from one side to another
controlled processes accessible to conscious control and
even require it
convergent thinking attempt to narrow down the multiple
possibilities to converge on a single best answer
converging operations the use of multiple approaches and
techniques to address a problem
cooperative principle principle in conversation that holds
that we seek to communicate in ways that make it
easy for our listener to understand what we mean
core refers to the defining features something must have to be
considered an example of a category
corpus callosum a dense aggregate of neural fibers connecting the two cerebral hemispheres
creativity the process of producing something that is both
original and worthwhile
culture-fair equally appropriate and fair for members of all
cultures
culture-relevant tests measure skills and knowledge that
relate to the cultural experiences of the test-takers
decay occurs when simply the passage of time causes an individual to forget
decay theory asserts that information is forgotten because of
the gradual disappearance, rather than displacement, of
the memory trace
532
Glossary
declarative knowledge knowledge of facts that can be stated
deductive reasoning the process of reasoning from one or
more general statements regarding what is known to
reach a logically certain conclusion
deductive validity logical soundness
deep structure refers to an underlying syntactic structure that
links various phrase structures through the application
of various transformation rules
defining feature a necessary attribute
dendrites the branch-like structures of each neuron that
extend into synapses with other neurons and that
receive neurochemical messages sent into synapses by
other neurons
denotation the strict dictionary definition of a word
dependent variable a response that is measured and is presumed to be the effect of one or more independent
variables
depth the distance from a surface, usually using your own
body as a reference surface when speaking in terms of
depth perception
dialect a regional variety of a language distinguished by features such as vocabulary, syntax, and pronunciation
dichotic presentation presenting a different message to each ear
direct perception theory belief that the array of information
in our sensory receptors, including the sensory context,
is all we need to perceive anything
discourse encompasses language use at the level beyond the
sentence, such as in conversation, paragraphs, stories,
chapters, and entire works of literature
dishabituation change in a familiar stimulus that prompts us
to start noticing the stimulus again
distracters nontarget stimuli that divert our attention away
from the target stimulus
distributed practice learning in which various sessions are
spaced over time
divergent thinking when one tries to generate a diverse
assortment of possible alternative solutions to a problem
divided attention the prudent allocation of available attentional resources to coordinate the performance of more
than one task at a time
dual-code theory belief suggesting that knowledge is represented both in images and in symbols
dual-system hypothesis suggests that two languages are
represented somehow in separate systems of the mind
dyslexia difficulty in deciphering, reading, and comprehending text
ecological validity the degree to which particular findings in
one environmental context may be considered relevant
outside that context
electroencephalograms (EEGs) recordings of the electrical
frequencies and intensities of the living brain, typically
recorded over relatively long periods
elimination by aspects occurs when we eliminate alternatives
by focusing on aspects of each alternative, one at a time
emotional intelligence the ability to perceive and express
emotion, assimilate emotion in thought, understand
and reason with emotion, and regulate emotion in the
self and others
empiricist one who believes that we acquire knowledge via
empirical evidence
encoding refers to how you transform a physical, sensory
input into a kind of representation that can be placed
into memory
encoding specificity what is recalled depends on what is
encoded
episodic buffer a limited-capacity system that is capable of
binding information from the subsidiary systems and
from long-term memory into a unitary episodic representation
episodic memory stores personally experienced events or
episodes
event-related potential an electrophysiological response to a
stimulus, whether internal or external
executive attention a subfunction of attention that includes
processes for monitoring and resolving conflicts that
arise among internal processes
exemplars typical representatives of a category
expertise superior skills or achievement reflecting a welldeveloped and well-organized knowledge base
expert systems computer programs that can perform the way
an expert does in a fairly specific domain
explicit memory when participants engage in conscious
recollection
factor analysis a statistical method for separating a construct
into a number of hypothetical factors or traits that the
researchers believe form the basis of individual differences in test performance
fallacy erroneous reasoning
feature-integration theory explains the relative ease of conducting feature searches and the relative difficulty of
conducting conjunction searches
feature-matching theories suggest that we attempt to match
features of a pattern to features stored in memory
feature search simply scanning the environment for a particular feature or features
figure-ground what stands out from versus what recedes into
the background
filter theories theories proposing a filter that blocks some of
the information going through and thereby selects only
a part of the total of information to pass through to the
next stage
flashbulb memory a memory of an event so powerful that the
person remembers the event as vividly as if it were
indelibly preserved on film
flow chart a model path for reaching a goal or solving a
problem
fovea a part of the eye located in the center of the retina that
is largely responsible for the sharp central vision people
Glossary
use in activities such as reading or watching television
or movies
frontal lobe associated with motor processing and higher
thought processes, such as abstract reasoning
functional-equivalence hypothesis belief that although
visual imagery is not identical to visual perception, it
is functionally equivalent to it
functional fixedness the inability to realize that something
known to have a particular use may also be used for
performing other functions
functional magnetic resonance imaging (fMRI) a neuroimaging technique that uses magnetic fields to construct a
detailed representation in three dimensions of levels of
activity in various parts of the brain at a given moment
functionalism seeks to understand what people do and why
they do it
function morphemes a morpheme that adds detail and
nuance to the meaning of the content morphemes or
helps the content morphemes fit the grammatical context
ganglion cells a kind of neuron usually situated near the
inner surface of the retina of the eye; receive visual
information from photoreceptors by way of bipolar
cells and amacrine cells; send visual information from
the retina to several different parts of the brain, such
as the thalamus and the hypothalamus
Gestalt approach to form perception based on the notion
that the whole differs from the sum of its individual parts
Gestalt psychology states that we best understand psychological phenomena when we view them as organized, structured wholes
“g” factor general ability
grammar the study of language in terms of noticing regular
patterns
habituation involves our becoming accustomed to a stimulus
so that we gradually pay less and less attention to it
heuristics informal, intuitive, speculative strategies that
sometimes lead to an effective solution and sometimes
do not
hindsight bias when we look at a situation retrospectively,
we believe we easily can see all the signs and events
leading up to a particular outcome
hippocampus plays an essential role in memory formation
horizontal cells along with amacrine cells, they make single
lateral connections among adjacent areas of the retina
in the middle layer of cells
hypermnesia a process of producing retrieval of memories
that seem to have been forgotten
hypothalamus regulates behavior related to species survival:
fighting, feeding, fleeing, and mating; also active in regulating emotions and reactions to stress
hypotheses tentative proposals regarding expected empirical
consequences of the theory
hypothesis testing a view of language acquisition that asserts
that children acquire language by mentally forming
533
tentative hypotheses regarding language, based on their
inherited facility for language acquisition and then testing these hypotheses in the environment
hypothetical constructs concepts that are not themselves
directly measurable or observable but that serve as mental models for understanding how a psychological phenomenon works
iconic store a discrete visual sensory register that holds information for very short periods
ill-structured problems problems that lack well-defined
paths to solution
illusory correlation occurs when we tend to see particular
events or particular attributes and categories as going
together because we are predisposed to do so
imagery the mental representation of things that are not currently being sensed by the sense organs
implicit memory when we recollect something but are not
consciously aware that we are trying to do so
incubation putting the problem aside for a while without
consciously thinking about it
independent variable a variable that is varied or purposefully
manipulated and that affects one or more dependent
variables
indirect requests the making of a request without doing so
straightforwardly
inductive reasoning the process of reasoning from specific
facts or observations to reach a likely conclusion that
may explain the facts
infantile amnesia the inability to recall events that happened
when we were very young
insight a distinctive and sometimes seemingly sudden understanding of a problem or of a strategy that aids in solving
the problem
intelligence the capacity to learn from experience, using
metacognitive processes to enhance learning, and the
ability to adapt to the surrounding environment
interference occurs when competing information causes an
individual to forget something
interference theory refers to the view that forgetting occurs
because recall of certain words interferes with recall of
other words
introspection looking inward at pieces of information passing
through consciousness
ipsilateral on the same side
isomorphic the formal structure is the same, and only the
content differs
jargon specialized vocabulary commonly used within a group,
such as a profession or a trade
judgment and decision making used to select from among
choices or to evaluate opportunities
knowledge representation the form for what you know in
your mind about things, ideas, events, and so on that
exist outside your mind
Korsakoff’s syndrome produces loss of memory function
534
Glossary
language the use of an organized means of combining words
in order to communicate
law of Prägnanz tendency to perceive any given visual array
in a way that most simply organizes the disparate elements into a stable and coherent form
levels-of-processing framework postulates that memory does
not comprise three or even any specific number of separate stores but rather varies along a continuous dimension in terms of depth of encoding
lexical access the identification of a word that allows us to
gain access to the meaning of the word from memory
lexical processes used to identify letters and words
lexicon the entire set of morphemes in a given language or in
a given person’s linguistic repertoire
limbic system important to emotion, motivation, memory,
and learning
linguistic relativity the assertion that speakers of different
languages have differing cognitive systems and that
these different cognitive systems influence the ways in
which people speaking the various languages think
about the world
linguistic universals characteristic patterns across all languages of various cultures
lobes divide the cerebral hemispheres and cortex into four
parts
localization of function refers to the specific areas of the
brain that control specific skills or behaviors
long-term store very large capacity, capable of storing information for very long periods, perhaps even indefinitely
magnetic resonance imaging (MRI) scan a technique for
revealing high-resolution images of the structure of the
living brain by computing and analyzing magnetic
changes in the energy of the orbits of nuclear particles
in the molecules of the body
magnetoencephalography (MEG) an imaging technique that
measures the magnetic fields generated by electrical
activity in the brain by highly sensitive measuring
devices
massed practice learning in which sessions are crammed
together in a very short space of time
medulla oblongata brain structure that controls heart activity
and largely controls breathing, swallowing, and
digestion
memory the means by which we retain and draw on our past
experiences to use this information in the present
mental models knowledge structures that individuals construct to understand and explain their experiences; an
internal representation of information that corresponds
analogously with whatever is being represented
mental rotation involves rotationally transforming an
object’s visual mental image
mental set a frame of mind involving an existing model for
representing a problem, a problem context, or a procedure for problem solving
metacognition our understanding and control of our cognition; our ability to think about and control our own
processes of thought and ways of enhancing our thinking
metamemory strategies involve reflecting on our own memory processes with a view to improving our memory
metaphor two nouns juxtaposed in a way that positively
asserts their similarities, while not disconfirming their
dissimilarities
mnemonic devices specific techniques to help you memorize
lists of words
mnemonist someone who demonstrates extraordinarily keen
memory ability, usually based on the use of special techniques for memory enhancement
modular divided into discrete modules that operate more or
less independently of each other
monocular depth cues can be represented in just two dimensions and observed with just one eye
monolinguals people who can speak only one language
morpheme the smallest unit that denotes meaning within a
particular language
multimode theory proposes that attention is flexible; selection of one message over another message can be made
at any of various different points in the course of information processing
myelin a fatty substance coating the axons of some neurons
that facilitates the speed and accuracy of neuronal
communication
natural categories groupings that occur naturally in the world
negative transfer occurs when solving an earlier problem
makes it harder to solve a later one
nervous system the organized network of cells (neurons)
through which an individual receives information from
the environment, processes that information, and then
interacts with the environment
networks a web of relationships (e.g., category membership,
attribution) between nodes
neurons individual nerve cells
neurotransmitters chemical messengers used for interneuronal communication
nodes the elements of a network
nodes of Ranvier gaps in the myelin coating of myelinated
axons
nominal kind the arbitrary assignment of a label to an entity
that meets a certain set of prespecified conditions
noun phrase syntactic structure that contains at least one
noun (often, the subject of the sentence) and includes
all the relevant descriptors of the noun
object-centered representation the individual stores a representation of the object, independent of its appearance to
the viewer
occipital lobe associated with visual processing, the primary
motor cortex, which specializes in the planning, control,
and execution of movement, particularly of movement
involving any kind of delayed response
Glossary
optic ataxia impaired visual control of the arm in reaching
out to a visual target
optic nerve the nerve that transmits information from the
retina to the brain
overconfidence an individual’s overvaluation of her or his
own skills, knowledge, or judgment
overregularization occurs when individuals apply the general
rules of language to the exceptional cases that vary from
the norm
parallel distributed processing (PDP) models or connectionist models the handling of very large numbers of cognitive operations at once through a network distributed
across incalculable numbers of locations in the brain
parallel processing occurs when multiple operations are executed all at once
parietal lobe associated with somatosensory processing
perception the set of processes by which we recognize, organize, and make sense of the sensations we receive from
environmental stimuli
perceptual constancy occurs when our perception of an
object remains the same even when our proximal sensation of the distal object changes
phoneme is the smallest unit of speech sound that can be
used to distinguish one utterance in a given language
from another
phonemic-restoration effect sounds that are missing from a
speech signal are constructed by the brain so it seems to
the listener that he actually heard the missing sound
phonological loop briefly holds inner speech for verbal comprehension and for acoustic rehearsal
photopigments chemical substances that absorb light,
thereby starting the complex transduction process that
transforms physical electromagnetic energy into an electrochemical neural impulse; rods and cones contain
different types of photopigments; different types of
photopigments absorb differing amounts of light and
may detect different hues
photoreceptors the third layer of the retina contains the
photoreceptors, which transduce light energy into electrochemical energy
phrase-structure grammar syntactical analysis of the structure of phrases as they are used
pons serves as a kind of relay station because it contains neural fibers that pass signals from one part of the brain to
another
positive transfer occurs when the solution of an earlier problem makes it easier to solve a new problem
positron emission tomography (PET) scans measure increases in glucose consumption in active brain areas
during particular kinds of information processing
pragmatic reasoning schemas general organizing principles or
rules related to particular kinds of goals, such as permissions, obligations, or causations
pragmatics the study of how people use language
535
pragmatists ones who believe that knowledge is validated by
its usefulness
premises propositions about which arguments are made
primacy effect refers to superior recall of words at and near
the beginning of a list
primary motor cortex region of the cerebral cortex that is
chiefly responsible for directing the movements of all
muscles
primary somatosensory cortex receives information from the
senses about pressure, texture, temperature, and pain
prime a node that activates a connected node; this activation
is known as the priming effect
priming the facilitation of one’s ability to utilize missing
information; occurs when recognition of certain stimuli
is affected by prior presentation of the same or similar
stimuli
priming effect the resulting activation of the node
proactive interference occurs when the interfering material
occurs before, rather than after, learning of the tobe-remembered material
problem solving an effort to overcome obstacles obstructing
the path to a solution
problem-solving cycle includes problem identification, problem definition, strategy formulation, organization of
information, allocation of resources, monitoring, and
evaluation
problem space the universe of all possible actions that can be
applied to solving a problem, given any constraints that
apply to the solution of the problem
procedural knowledge knowledge of procedures that can be
implemented
production the generation and output of a procedure
production system an ordered set of productions in which
execution starts at the top of a list of productions, continues until a condition is satisfied, and then returns to
the top of the list to start anew
productive thinking involves insights that go beyond the
bounds of existing associations
proposition basically an assertion, which may be either true
or false
propositional theory belief suggesting that knowledge is
represented only in underlying propositions, not in the
form of images or of words and other symbols
prototype a sort of average of a class of related objects or
patterns, which integrates all the most typical (most frequently observed) features of the class
prototype theory suggests that categories are formed on the
basis of a (prototypical, or averaged) model of the category
psycholinguistics the psychology of our language as it interacts with the human mind
rationalist one who believes that the route to knowledge is
through logical analysis
reasoning the process of drawing conclusions from principles
and from evidence
536
Glossary
recall to produce a fact, a word, or other item from memory
recency effect refers to superior recall of words at and near
the end of a list
recognition to select or otherwise identify an item as being
one that you learned previously
recognition-by-components (RBC) theory the belief that we
quickly recognize objects by observing the edges of
objects and then decomposing the objects into geons
reconstructive involving the use of various strategies (e.g.,
searching for cues, drawing inferences) for retrieving
the original memory traces of our experiences and then
rebuilding the original experiences as a basis for retrieval
referent the thing or concept in the real world that a word
refers to
rehearsal the repeated recitation of an item
representativeness occurs when we judge the probability of
an uncertain event according to (1) its obvious similarity to or representation of the population from which it
is derived and (2) the degree to which it reflects the
salient features of the process by which it is generated
(such as randomness)
reticular activating system (RAS) a network of neurons
essential to the regulation of consciousness (sleep, wakefulness, arousal, and even attention to some extent and
to such vital functions as heartbeat and breathing); also
called reticular formation
retina a network of neurons extending over most of the back
(posterior) surface of the interior of the eye. The retina
is where electromagnetic light energy is transduced—
that is, converted—into neural electrochemical impulses
retrieval (memory) refers to how you gain access to information stored in memory
retroactive interference caused by activity occurring after we
learn something but before we are asked to recall that
thing; also called retroactive inhibition
retrograde amnesia occurs when individuals lose their purposeful memory for events prior to whatever trauma
induces memory loss
rods light-sensitive photoreceptors in the retina of the eye
that provide peripheral vision and the ability to see
objects at night or in dim light; rods are not color
sensitive
satisficing occurs when we consider options one by one, and
then we select an option as soon as we find one that is
satisfactory or just good enough to meet our minimum
level of acceptability
schemas mental frameworks for representing knowledge that
encompass an array of interrelated concepts in a meaningful organization
script a structure that describes appropriate sequences of
events in a particular context
search refers to a scan of the environment for particular features—actively looking for something when you are not
sure where it will appear
selective attention choosing to attend to some stimuli and to
ignore others
selective-combination insight involves taking selectively
encoded and compared snippets of relevant information and combining that information in a novel,
productive way
selective-comparison insight involves novel perceptions of
how new information relates to old information
selective-encoding insight involves distinguishing relevant
from irrelevant information
semantic memory stores general world knowledge
semantic network a web of interconnected elements of
meaning
semantics the study of meaning in a language
sensory adaptation a lessening of attention to a stimulus that
is not subject to conscious control
sensory store capable of storing relatively limited amounts of
information for very brief periods
septum is involved in anger and fear
serial-position curve represents the probability of recall of a
given word, given its serial position (order of presentation) in a list
serial processing means by which information is handled
through a linear sequence of operations, one operation
at a time
short-term store capable of storing information for somewhat
longer periods but also of relatively limited capacity
signal a target stimulus
signal detection the detection of the appearance of a particular stimulus
signal-detection theory (SDT) a theory of how we detect
stimuli that involves four possible outcomes of the presence or absence of a stimulus and our detection or nondetection of a stimulus
simile introduces the word like or as into a comparison
between items
single-system hypothesis suggests that two languages are
represented in just one system
slips of the tongue inadvertent linguistic errors in what
we say
soma the cell body of a neuron that is the part of the neuron
essential to the life and reproduction of the cell
spacing effect refers to the fact that long-term recall is best
when the material is learned over a longer period of
time
spatial cognition refers to the acquisition, organization, and
use of knowledge about objects and actions in two- and
three-dimensional space
speech acts addresses the question of what you can accomplish with speech
split-brain patients people who have undergone operations
severing the corpus callosum
spreading activation excitation that fans out along a set of
nodes within a given network
Glossary
statistical significance indicates the likelihood that a given
set of results would be obtained if only chance factors
were in operation
stereotypes beliefs that members of a social group tend more
or less uniformly to have particular types of
characteristics
storage (memory) refers to how you retain encoded information in memory
Stroop effect demonstrates the psychological difficulty in
selectively attending to the color of the ink and trying
to ignore the word that is printed with the ink of that
color
structuralism seeks to understand the structure (configuration of elements) of the mind and its perceptions by
analyzing those perceptions into their constituent
components
structure-of-intellect (SOI) Guilford’s model for a threedimensional structure of intelligence, embracing various
contents, operations, and products of intelligence
subjective probability a calculation based on the individual’s
estimates of likelihood, rather than on objective statistical computations
subjective utility a calculation based on the individual’s
judged weightings of utility (value), rather than on
objective criteria
surface structure a level of syntactic analysis that involves
the specific syntactical sequence of words in a sentence
and any of the various phrase structures that may result
syllogisms deductive arguments that involve drawing conclusions from two premises
symbolic representation meaning that the relationship
between the word and what it represents is simply
arbitrary
synapse a small gap between neurons that serves as a point
of contact between the terminal buttons of one or
more neurons and the dendrites of one or more other
neurons
syntax refers to the way in which users of a particular language put words together to form sentences
synthesis putting together various elements to arrange them
into something useful
templates highly detailed models for patterns we potentially
might recognize
temporal lobe associated with auditory processing
terminal buttons knobs at the end of each branch of an
axon; each button may release a chemical neurotransmitter as a result of an action potential
thalamus relays incoming sensory information through
groups of neurons that project to the appropriate region
in the cortex
thematic roles ways in which items can be used in the context of communication
theory an organized body of general explanatory principles
regarding a phenomenon
537
theory-based view of meaning holds that people understand
and categorize concepts in terms of implicit theories, or
general ideas they have regarding those concepts
theory of multiple intelligences belief that intelligence comprises multiple independent constructs, not just a single,
unitary construct
tip-of-the-tongue phenomenon experience of trying to
remember something that is known to be stored in
memory but that cannot readily be retrieved
top-down theories driven by high-level cognitive processes,
existing knowledge, and prior expectations
transcranial magnetic stimulation (TMS) technique that
temporarily disrupts the normal activity of the brain in
a limited area. This technique requires placing a coil on
a person’s head and then allowing an electrical current to
pass though it. The current generates a magnetic field.
This field disrupts the small area (usually no more than
a cubic centimeter) beneath it. The researcher can then
look at cognitive functioning when the particular area is
disrupted
transfer any carryover of knowledge or skills from one problem situation to another
transformational grammar involves the study of transformational rules that guide the ways in which underlying
propositions can be rearranged to form various phrase
structures
transparency occurs when people see analogies where they
do not exist because of similarity of content
triarchic theory of human intelligence belief that intelligence comprises three aspects, dealing with the relation
of intelligence (1) to the internal world of the person,
(2) to experience, and (3) to the external world
verbal comprehension the receptive ability to comprehend
written and spoken linguistic input, such as words, sentences, and paragraphs
verbal fluency the expressive ability to produce linguistic
output
verb phrase syntactic structure that contains at least one
verb and whatever the verb acts on, if anything
viewer-centered representation an individual stores the way
the object looks to him or her
vigilance refers to a person’s ability to attend to a field of
stimulation over a prolonged period, during which the
person seeks to detect the appearance of a particular target stimulus of interest
visuospatial sketchpad briefly holds some visual images
well-structured problems problems that have well-defined
paths to solution
word-superiority effect letters are read more easily when they
are embedded in words than when they are presented
either in isolation or with letters that do not form words
working memory holds only the most recently activated portion of long-term memory, and it moves these activated
elements into and out of brief, temporary memory storage
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Name Index
Page numbers followed by F indicate figures; T, tables.
Aaronson, D., 413
Abbott, L. E., 61
ABC Research Group, 501
Abelson, R. P., 337
Abernethy, B., 474
Abler, B., 64
Abrams, D. M., 365
Ackerman, P. L., 18, 209
Ackil, J. K., 256
Adams, M. J., 386
Adler, J., 404
Adolphs, R., 46
Aglioti, S. M., 290
Agulera, A., 64
Ahlers, R. H., 294
Akhtar, N., 367
Albert, M. L., 414
Albert, R. S., 480
Al’bertin, S. V., 66
Aleman, A., 289
Alex (parrot), 431
Alkire, M. T., 78
Allain, P., 339
Allefeld, C., 90
Almor, A., 512
Alpert, N. M., 179
Altenmüller, E., 71
Altschuler, E. L., 436
Alzheimer, A., 221
Amabile, T. M., 264, 481
Ambinder, M. S., 2
Aminoff, E., 256
Anderson, A. K., 46
Anderson, D. P., 434
Anderson, J. R., 172, 281, 341,
344, 345, 346F, 347, 466
Anderson, N. D., 248
Anderson, R. C., 396
Anderson, S. W., 219
Anderson, V., 466
Andreasen, N. C., 459
Andreou, G., 412
Andreou, P., 164
Ang, S., 18
Anglin, J. M., 367
Appleton-Knapp, S. L., 235
Archer, T., 66
Archibald, J., 366
Ardekani, B. A., 74
Argamon, S., 428
Aristotle, 6, 38, 39
Armstrong, S. L., 328
Arocha, J. F., 502
Aronoff, M., 366
Ask, K., 518
Atkinson, R., 193, 194
Atkinson, Richard, 53, 203
Atkinson, Rita, 53
Atran, S., 331
Austin, G. A., 322, 356
Averbach, E., 196, 197
Awh, E., 206
Azevedo, R., 303
B., D. (patient), 181
Backhaus, J., 237
Bacon, F., 24
Baddeley, A. D., 187, 203, 204,
205, 219, 231, 263, 264,
343, 418
Badecker, W., 324
Badgaiyan, R. D., 179
Bahna, S. L., 163
Bahrami, B., 138
Bahrick, H. P., 199, 235, 413
Bahrick, P. O., 199
Bailenson, J. N., 423
Bain, J. D., 210
Baker, D. B., 15
Baker, S. C., 466
Bakker, D. J., 387
Baldeweg, T., 68
Baliki, M., 74
Ball, L. J., 515
Ball, T. M., 297, 300
Balota, D. A., 191, 263
Baltes, P. B., 192, 474
Banaji, M., 265, 266, 404
Banaji, M. R., 403
Band, G. P. H., 290, 292
Bandler, R., 50
Bandura, A., 13
Bar, M., 106, 109, 522
Barker, R. A., 46
Baron, J., 465, 518
Baron-Cohen, S., 46, 438
Barraclough, D. J., 505
Barrett, L. F., 254, 523
Barrett, P. T., 79
Barron, F., 480
Barsalou, L. W., 323, 336, 378
Barston, J. I., 518
Bartlett, F. C., 248, 249
Barton, J. J. S., 121
Bassok, M., 462, 465
Bastiaanse, R., 438
Bastian, B., 331
Bastien, C., 131
Batterman, N., 327
Battista, C., 290
Baudouin, A., 208
Bauer, P. J., 189, 190
Baumberger, T., 410
Baumgartner, C., 74
Baune, B. T., 193
Bavelier, D., 145, 199
Bearden, C. E., 264
Beardsley, M., 420
Beauchamp, M. S., 374
Bechtel, W., 14
Bechtereva, N. P., 483
Beck, I. L., 394
Becker, C. A., 391
Becklen, R., 154
Bee, M. A., 148
Begg, I., 514
Beggs, A., 495
Beghetto, R. A., 479, 483
Behrmann, M., 128
Beier, M. E., 209
Bell, M. A., 293
Bellezza, F. S., 201
Bem, D., 53
Bencini, G., 378
Benjamin, L. T., Jr., 15
Bennis, W. M., 501
Ben-Zeev, T., 512
Beran, M. J., 430
Beresh, H., 78
Bergerbest, D., 179
Berkow, R., 50
Berkowitz, S. R., 260
Berlin, B., 407
Berliner, H. J., 478
Berlucchi, G., 52
Berman, M. G., 251, 252
Bernardi, S., 74
Bernstein, A., 478
Bernstein, D. M., 260
Bernstein, M. J., 120
Berntson, G. G., 50
Berry, C. J., 190
Berry, D., 14
Berryhill, M. E., 210
Bertsch, K., 174
Besnard, D., 462
Bessman, P., 169
Best, J., 238
Beste, C., 293
Bethell-Fox, C. E., 290
Beyer, J. L., 74
Bhatia, T. T., 412
Bhatt, R. S., 116
Biais, B., 499
Bialystok, E., 412, 413
Bibi, U., 255
Bickerton, D., 416
Biederman, I., 106–7, 109
Biederman, J., 163
Biernat, M., 518
Bilalic, M., 468, 471, 474
Bilda, Z., 277
Binder, J. R., 432, 433, 438
Bingman, V. P., 310
Birbaumer, N., 320
Birdsong, D., 413
Bisiach, E., 166
Bjork, E. L., 255
Bjork, R. A., 193
Bjorklund, D. F., 187
Black, J. B., 338
Black, M., 420
Blackwood, N. J., 483
Blades, M., 260
Blake, R., 93
Blake, W. C. A., 294
Blakemore, S.-J., 288
Blandon-Gitlin, I., 259
Blessing, S. B., 465
Bloom, B. S., 446
Bloom, I., 446
Blore, R., 270
Blumstein, S. E., 370
Bock, K., 378, 379, 384, 385, 419
Boden, M. A., 480
Bohannon, J., 255
Bohlin, G., 163
Boloix, E., 131
Bolte, S., 120
Boon, F., 46
Boring, E. G., 16, 18, 24
Born, J., 237
Boroditsky, L., 409
Borovsky, D., 264
593
594
Name Index
Bors, D. A., 246
Borst, G., 298, 299, 300
Bosco, A., 294
Bothwell, R. K., 259
Bourg, T., 290, 300
Bourguignon, E., 138
Bousfield, W. A., 232, 238
Bowden, E. M., 459
Bower, G. H., 201, 245, 253,
263, 281, 312, 338, 345,
405
Bowers, C. A., 54
Bowers, K. S., 179, 262
Boyle, M. O., 209
Bradshaw, J. L., 374
Brady, T. F., 199, 232
Braine, M. D. S., 510
Brambati, S. M., 174
Bransford, J. D., 202, 229, 253,
405, 444
Braun, C. M. J., 339
Braun, K. A., 260
Braver, T. S., 80
Brebion, G., 187
Brefczynski-Lewis, J. A., 469
Bregman, A. S., 371
Breier, J., 372
Brennan, S. E., 421, 424
Brenneis, C. B., 262
Brennen, T., 180
Brent, S. B., 326
Bresnan, J. W., 381
Bressan, P., 165
Breuning, M., 522
Brewer, W. F., 301,
303, 323
Briere, J., 261
Brigden, R., 195
Brigham, J. C., 259
Brighton, H., 501
Bristol, A. S., 483
Broadbent, D. E., 14–15,
150, 151
Broca, P., 52, 53, 66, 432
Brockmole, J. R., 443
Broder, L. J., 446
Broeder, A., 421
Brooks, L. R., 279
Brown, A. M., 408
Brown, A. S., 180
Brown, C., 13
Brown, C. M., 432
Brown, J. A., 247
Brown, N. R., 312
Brown, R., 180, 255, 361
Brown, S. C., 187, 200
Brown, V., 142
Bruce, D., 244
Bruck, M., 259, 261
Bruhn, P., 163
Bruner, J. S., 107, 322, 356
Brungard, D. S., 149
Bruno, J. P., 50
Brush, L. N., 248
Bryan, W. L., 172
Bryson, M., 469
Buceta, M. J., 277
Buckley, M., 99
Budak, F., 162
Budwig, N., 364
Bullmore, E., 64
Bülthoff, H. H., 92, 112
Bunge, S. A., 524
Bunting, M., 248
Bunting, M. F., 149
Burgess, M., 200
Burgess, P., 80
Burgund, E. D., 112
Burke, C. S., 504
Burns, B. D., 474
Burns, H. J., 257, 258
Buschke, H., 223
Butler, J., 264
Butters, N., 224
Butterworth, B., 379
Byrne, A., 159
Byrne, R. M. J., 301, 397, 517
Cabeza, R., 210
Cacitti, L., 462
Cahill, L., 224
Cain, D. P., 46
Cain, K., 395, 397
Calvanio, R., 306
Cameron, J., 11
Campbell, D. A., 481
Campbell, J. I. D., 462
Campbell, K. B., 79
Campbell, R., 437
Campbell, S. D., 495
Campitelli, G., 99
Campos, A., 277
Canli, T., 224
Cannon-Bowers, J. A., 504
Cant, J. S., 99
Cappa, S. F., 434
Caramazza, A., 436, 437
Carey, S., 404
Carlson, E. R., 498
Carlson, M. P., 446
Carlson, N. R., 61, 62
Carmichael, L., 285, 285F
Carolei, A., 335
Carpenter, P. A., 208, 290, 388
Carroll, D. W., 377
Carroll, J. B., 19
Carroll, J. S., 502
Carroll, L., 378
Carter, M., 142
Carvalho, J. P., 27
Carver, L. J., 190
Case, K., 165
Casey, B. J., 46
Cassia, V. M., 103
Castelli, F., 73
Castellucci, V. F., 166, 168
Castle, L., 164
Catlin, J., 325
Catroppa, C., 466
Cattell, J. M., 390
Cattell, R. B., 19, 465
Cave, K. R., 146, 157
Cazalis, F., 466
Ceci, S. J., 253, 259, 262
Cepeda, N. J., 235
Ceponiene, R., 67
Chabris, C. F., 80
Chaix, Y., 387
Chambers, D., 283, 284, 284F,
285, 286, 300
Chamodrakas, I., 491
Chan, A. S., 439
Chan, C. C. H., 277
Chapman, E., 180
Chapman, J. P., 498, 504, 515,
521
Chapman, L. J., 498, 515, 521
Charltona, S. G., 159
Chase, W. G., 215, 263, 266,
282, 354, 443, 470F
Chaucer, G., 364
Chen, C.-Y., 438
Chen, X., 453
Chen, Y., 491
Chen, Z., 462
Cheng, P. W., 510, 511, 512,
518, 521
Cheng, Y. D., 117
Cherry, C., 148–49, 151, 153
Chi, M. T. H., 464, 469, 471
Chiu, C. Y. P., 5, 331
Cho, K., 186
Choi, S., 410
Chomsky, N., 14, 378, 381, 382,
383, 384, 398, 431, 432
Christal, R. E., 208
Christensen, T. C., 254
Christiaans, H., 480
Christiaens, D., 109
Christoff, K., 459
Chubb, C., 118
Chun, M. M., 117, 119, 198
Churchland, P., 61
Cisler, J. M., 143
Clark, A., 96
Clark, E. V., 324, 361, 362, 418,
424
Clark, H. H., 282, 324, 361, 397,
418, 421, 424, 425
Clark, U. S., 225
Clegg, A. B., 381
Clement, C. A., 517
Clinton, S. M., 48
Coane, J. H., 263
Coenen, A., 54
Cohen, G., 312
Cohen, J., 501
Cohen, J. D., 73
Cohen, J. T., 159
Cohen, M. M., 374
Cohen, M. S., 293
Cole, M., 331
Coleman, J., 361
Coley, J. D., 331
Collette, F., 483
Collier, M., 327
Collins, A. M., 332, 333–34, 345
Collins, D. W., 293
Collins, M. A., 481
Colom, R., 80, 209
Committeri, G., 113
Conn, C., 387
Connell, J., 510
Connolly, D. A., 253, 255
Connolly, T., 502
Conrad, R., 230
Conroy, M. L., 505
Conte, J. R., 261
Conway, A. R. A., 149, 209
Conway, M. A., 255
Cook, A. E., 393
Cooper, C., 162
Corballis, M. C., 304
Corcoran, D. W. J., 244
Corcoran, J., 164
Corcoran, M. E., 46
Coren, S., 122, 129
Coriell, A. S., 196, 197
Corina, D., 370
Corkin, S., 218, 221, 222, 336
Coslett, H. B., 387
Cosmides, L., 512
Costello, C. G., 191
Coupe, P., 312
Courchesne, E., 438
Coventry, L., 423
Cowan, N., 149, 151, 198
Cox, J. R., 511, 512
Craik, F., 201
Craik, F. I. M., 187, 200, 202,
265, 412
Craik, K., 396
Crivelli, C., 125
Cronly-Dillon, J., 270
Crouch, D. J., 159
Crowder, R. G., 187, 266
Cruz, N. V., 163
Crystal, D., 365, 418, 419
Csikszentmihalyi, M., 479, 481
Cui, X., 277
Culham, J. C., 57
Culicover, P. W., 384
Cumming, B. G., 125
Cummings, A., 67
Cummins, D. D., 14, 512
Cummins, J., 412
Cummins, R., 14
Cunningham, S. J., 201
Name Index
Curie, M., 479
Curran, T., 256
Cutler, B. L., 257
Cutting, J., 110
Cziko, G. A., 481
Daehler, M. W., 462
Dahlgren, A., 26
Dakin, S. C., 105
Dalezman, R. E., 231
Dallenbach, K. M., 90
Damasio, A. R., 121, 129
Damasio, H., 121
Dambacher, M., 433
D’Amico, A., 208
Damoiseaux, J. S., 459
Daneman, M., 208, 386
Daniel, M. H., 18
Daniels, K., 192
Danker, J. F., 345
Danks, J. H., 361
Darley, J., 405, 406
Darwin, C. J., 150
Darwin, C. R., 484
Das, J. P., 161
Dattalo, P., 164
Davidson, J. E., 161, 165, 447,
464
Davidson, R. J., 51
Davis, D., 131, 253
Davis, M. P., 64
Davis, S. N., 482
Dawes, R., 492, 500
Dax, M., 52, 82, 432
Dean, L. M., 422
De Beni, R., 216
Dedeogle, A., 75
Deeprose, C., 179
Deese, J., 262
Deffenbacher, J. L., 159
Deffenbacher, K. A., 259
De Graef, P., 109
De Groot, A. D., 354
Dehaene, S., 161
De Houwer, A., 414
De Jong, P. F., 174
De la Iglesia, J. C. F., 277
Dell, G. S., 419
Della Sala, S., 205
DeMiguel, V., 488
Démonet, J.-F., 387
Dempster, F. N., 80
Denis, M., 297, 298, 299
Denny, J., 514
Denny, L. L., 148
De Renzi, E., 121
Dermietzel, R., 62, 64
Derntl, B., 46
De Rosa, E., 248
Desai, R., 432
Descartes, R., 7, 38, 430
Detre, J. A., 74
Detterman, D. K., 225, 462
Deutsch, D., 152
Deutsch, J. A., 152
DeValois, K. K., 105
DeValois, R. L., 105
Devitt, M., 384
Dew, N., 471
De Weerd, P., 57, 137
Dewey, J., 9, 38
Dewhurst, S. A., 263
De Yoe, E. A., 95
DiCarlo, S. E., 62
Diesendruck, G., 362
Dietrich, A., 483
Di Eugenio, B., 392
Di Giacomo, D., 335
DiGirolamo, G. J., 138, 143
Dijksterhuis, A., 136
Dijkstra, K., 364
Ditchburn, R. W., 89
Dittmann-Kohli, F., 192
Dixon, R. A., 192
Dixon, T. L., 347
Do, H.-H., 337
Dodd, J. V., 125
Dodd, M. D., 263
Dolan, M., 257
Dolderer, M., 326
Donders, F. C., 28
Donk, M., 111
Dosher, B., 207
Dosher, B. A., 203
Downing, P. E., 354
Doyle, C. L., 11
Drapier, D., 64
Dressel, S., 303
Drews, F. A., 159
Dror, I. E., 292
Druckman, J. N., 497
Dueck, A., 253, 405
DuFault, D., 264
Duffau, H., 433
Dugger, M., 294
Dully, H., 12
Dunbar, K., 323
Duncan, E., 290, 300
Duncan, J., 80, 145, 157
Duncker, K., 462
Dupuy, J. P., 500
Durgin, F. H., 95
Dybdahl, R., 180
D’Ydewalle, G., 109
Eagle, M., 231
Eales, M. J., 426
Eason, R., 153
Easton, N., 74
Ebbinghaus, H., 10, 38, 235
Ebert, P. L., 248
Edelman, S., 92
Edwards, W., 489
Egeth, H. E., 261
Eggemeier, F. T., 231
Ehrlich, K., 301
Eich, E., 264
Eich, J. E., 264
Eichenbaum, H., 46, 223
Einstein, A., 479, 481
Eisenberger, R., 481
Eisenegger, C., 293
Ekstrom, A. D., 223
Ellenbogen, J. M., 237
Ellis, R., 260
Elman, J. L., 351, 371
Elshout-Mohr, M., 395
Emmorey, K., 199, 370
Emslie, H., 80
Engel, A. S., 73
Engen, T., 230
Engin, E., 46
Engle, R. W., 209, 449, 471, 523
Engstler-Schooler, T. Y., 406
Epstein, W., 126
Erdelyi, M., 216
Ericsson, K. A., 177, 215, 266,
472, 474
Escher, M. C., 125
Espe Pfeifer, P., 414
Espino, O., 515
Esselman, E. D., 201
Esser, J. K., 504
Estes, W. K., 327
Evans, J. St. B. T., 501, 512, 518
Evans, K. M., 210
Evers, C. A., 93
Eysenck, H. J., 79
Eysenck, M., 159, 210, 493
F., S. (mnemonist), 215
Fagin, R., 337
Faglioni, P., 121
Fagot, J., 116
Fahle, M., 108
Falmagne, R. J., 517
Faloon, S., 215, 266
Farah, M. J., 47, 54, 112, 116,
117, 121, 127, 129, 286,
287, 300, 305, 306, 307
Faraone, S. V., 163
Farrell, P., 381
Farrington-Darby, T., 502
Farthing, G. W., 138
Farvolden, P., 262
Fdez-Riverola, F., 323
Federmeier, K. D., 62, 210
Feeney, A., 512
Feinberg, T. E., 129
Feist, G. J., 480, 481
Feldman, J. A., 212
Feynman, R., 427
Fiedler, K., 518
Fincham, J. M., 454
Fink, A., 162
Fink, G. R., 122
595
Finke, R., 299, 300
Finke, R. A., 286, 287, 480
Finley, S., 324
Fiorio, M., 290
Fischhoff, B., 493, 494, 498
Fischman, J., 221, 222
Fishbein, D. H., 506
Fisher, D., 411
Fisher, D. L., 157
Fisher, R. P., 202, 396
Fisk, A. D., 142
Fiske, A., 380
Flanders, M., 370
Fleck, J. I., 453
Fleck, M. S., 506
Flege, J., 413
Fleming, P., 517
Fleurance, P., 502
Floden, D., 56
Fodor, J. A., 16, 214, 324,
354, 418
Foerde, K., 157
Foley, M. A., 263
Fombonne, E., 438
Forgas, J. P., 263
Forrin, B., 246
Foulke, E., 369
Fowler, C. A., 372, 373
Frackowiak, R. S. J., 46
Frackowiak, S. J., 310
Franks, J., 202
Frean, M., 212
Freeman, R. D., 375
Freeman, W., 12
Frensch, P. A., 263, 443, 449,
467, 473, 474
Frick, F., 518
Friederici, A. D., 433
Friedman, A., 312
Frith, C. D., 310
Fromkin, V. A., 31, 385, 418
Frost, N., 232
Funke, J., 467
Gabora, L., 481
Gabrieli, J. D. E., 73, 179, 387
Gage, P., 31, 65
Gagliardo, A., 310
Gaillard, W. D., 73, 434
Gais, S., 237
Galaburda, A. M., 51
Galanter, C. A., 502
Galanter, E. H., 16, 354
Galantucci, B., 372, 373, 374
Galdo-Alvarez, S., 180
Gall, F.-J., 16
Gallagher, S., 99
Galotti, K. M., 518
Galpin, A., 131, 165
Gamble, J., 431
Gandour, J., 417
Ganel, T., 111, 120
596
Name Index
Ganis, G., 276, 288
Garcia, A. M., 417
Gardner, H., 14, 19, 20, 165,
354, 482
Garner, W., 15
Garnham, A., 301
Garrett, M. F., 379, 384, 418,
419, 432, 436
Garrod, S., 386
Garry, M., 257
Gasser, M., 361
Gauthier, I., 117, 120
Gazzaniga, M. S., 43, 48, 51, 54,
56, 57, 66, 74, 76, 205, 304
Ge, L., 120
Gelman, S. A., 330, 331
Genie (case study), 31
Gentile, J. R., 462
Gentner, D., 464, 465
Gentner, D. R., 464
Georgopoulos, A. P., 292, 293
Gernsbacher, M. A., 361
Gerrig, R. J., 403, 420
Ghahremani, D. G., 179
Gibbs, R. W., 424, 425
Gibson, E., 99
Gibson, J. J., 88, 97, 98, 99
Gick, M. L., 462, 463
Gigerenzer, G., 136, 494, 500,
501, 503
Gignac, G., 78
Gilbert, J. A. E., 396, 408
Gilboa, A., 224
Gildea, P. M., 367
Gilhooly, K. J., 501, 517
Gillam, B., 122
Gilligan, S. G., 201
Gilovich, T., 449, 499
Ginns, P., 11
Girelli, L., 174
Girgus, J. S., 122
Girotto, V., 507
Giuliodori, M. J., 62
Gladwell, M., 472
Gladwin, T., 314, 332
Glaescher, J., 80
Glaser, R., 469
Glass, A. L., 109
Gleitman, H., 328
Gleitman, L. R., 328
Glenberg, A. M., 235, 314
Glickstein, M., 52
Glimcher, P. W., 505
Gloor, P., 42, 46
Gluck, M. A., 46
Glucksberg, S., 244, 361, 405,
420
Gobbini, M. I., 120
Gobet, F., 99, 469
Godbout, L., 339
Godden, D. R., 264
Göder, R., 192
Gogos, A., 289, 300
Goldberg, B., 216
Golden, C. J., 414
Goldsmith, M., 255
Goldstein, D. G., 501
Goldstein, E. B., 213
Goldstone, R. L., 108
Goldvarg, Y., 301
Gollan, T. H., 180
Golomb, J. D., 189
Gonsalvez, C. J., 174
Gonzalez, R., 191
Goodale, M. A., 85, 95, 96, 99,
111, 130
Goodall, J., 430
Goodman, N., 520
Goodnow, J. J., 322, 356
Goodwin, G. P., 301
Gopher, D., 156
Gopnik, A., 410
Gordon, D., 423
Gordon, J. D., 432
Gordon, P., 402
Graddy, K., 495
Graesser, A. C., 397, 443, 468
Graf, P., 219
Graham, J. D., 159
Grainger, J., 390
Granhag, A., 518
Grant, E. R., 253
Gray, J. A., 151
Gray, J. R., 80
Grayson, D., 423
Green, D. W., 347
Greenberg, R., 248
Greene, D., 523
Greene, J. A., 303
Greenfield, P. M., 431
Greeno, J. G., 449, 450T
Greenough, W. T., 62
Greenwald, A. G., 265
Gregory, R. L., 107
Gregory, T., 162
Grice, H. P., 426
Griffey, R. T., 241
Griffin, D., 498
Griffin, H. J., 138
Griggs, R. A., 511, 512
Grigorenko, E. L., 18, 22, 193,
483
Grimes, C. E., 365
Grodzinsky, Y., 386
Groenholm, P., 73
Grossi, D., 121
Grossman, L., 231
Grosvald, M., 370
Grosz, B. J., 393
Grubb, M. D., 498
Gruber, H. E., 482, 484
Guarnera, M., 208
Gudjonsson, G. H., 261
Gueraud, S., 393
Gugerty, L., 341, 342
Guilford, J. P., 480
Gunzelmann, G., 466
Gupta, R., 224
H., L., 305, 305F, 306F, 307
Haber, R. N., 194
Hackman, D., 47
Haden, P. E., 219
Haefeli, W., 220
Hagoort, P., 432
Hagtvet, B. E., 394
Haier, R. J., 73, 78, 79, 80
Hakuta, K., 413
Hall, E. T., 422
Hall, G. B. C., 120
Hall, L. K., 199
Hambrick, D. Z., 209, 449, 471
Hamilton, D. L., 497
Hamm, A. O., 181
Hamm, J. P., 294
Hammond, K. M., 306
Hampton, J. A., 327
Hancock, T. W., 263
Hanley, J. R., 180, 408
Hanson, E. K., 371
Harber, K. D., 521
Hardy, J. K., 310
Hare, T. A., 46
Harley, T., 390
Harm, M. W., 390
Harnish, R. M., 423
Harris, C. L., 403
Harris, C. S., 110
Harris, G. J., 232
Harris, J. R., 110
Harsch, N., 255
Harter, N., 172
Hasel, L. E., 258
Haslam, N., 331
Hastie, R., 498
Hatakenaka, S., 502
Hausknecht, K. A., 163
Haviland, S. E., 397
Hawk, T. C., 148
Haworth, C. M. A., 476
Haxby, J. V., 95, 120, 129, 205
Hayden, A., 116
Hayes, J. R., 452
Hayes-Roth, B., 310
Hayne, H., 262
Head, K., 78, 99
Healy, A. F., 420
Hebb, D., 14
Hegel, G., 5, 38
Heil, M., 293
Heilman, K. M., 54
Heindel, W. C., 224
Heinrichs, M., 438
Heit, E., 323
Helms-Lorenz, M., 331
Helms Tillery, S. I., 51
Henley, N. M., 334
Hennessey, B. A., 480
Henriksen, L., 163
Henry, J. D., 242
Henry, L. A., 261
Henry, O., 392
Hernandez, A. E., 414
Hernández Blasi, C., 187
Herring, S. C., 428
Herschensohn, J., 413
Herwig, U., 293
Herz, R. S., 230
Hess, R. F., 105
Hesse, M., 420
Hewig, J., 506
Hewitt, K., 422
Hill, E. L., 439
Hill, J. H., 431
Hillis, A. E., 436, 437
Hillyard, S. A., 153, 433
Himmelbach, M., 130
Hinsz, V. B., 504
Hinton, G. E., 289, 300
Hirst, W., 153, 154, 160, 255
Hirtle, S. C., 310, 313, 314
Hochberg, J., 124
Hoeksema, S. N., 53
Hoff, E., 365, 367
Hoffding, H., 97, 100
Hoffman, C., 410
Hoffman, H., 163
Hoffrage, U., 500
Hogan, H. P., 285, 285F
Holland, J. H., 518
Holmes, D., 216
Holyoak, K. J., 443, 462, 463,
464, 464F, 473, 474, 511,
512, 518, 520, 521
Homa, D., 388
Honey, G., 74
Hong, L., 443
Honzik, C. H., 309
Hopfinger, J. B., 153
Hopkins, W. D., 431
Hopko, D. R., 27
Hornung, O. P., 237
Horwitz, B., 437
House, P., 523
Howard, D., 379
Howard, M., 46
Howland, J. G., 46
Hu, M., 394
Hu, Y., 472
Hubel, D., 66, 105, 145
Hubel, D. H., 104
Hugdahl, K., 51, 294
Hulme, C., 198
Hume, D., 521
Humphreys, G. W., 145, 157
Humphreys, M., 210
Hunt, E. B., 155, 161, 215, 391,
394, 404, 449, 450T, 473
Name Index
Huntsman, L. A., 289
Hurt, H., 47
Hutsler, J. J., 51, 54
Hutter, M., 17
Hybel, D., 89
Hyoenae, J., 391
Iaria, G., 310
Inagaki, H., 292
Inoue, S., 311
Intons-Peterson, M. J., 299, 303
Isaacowitz, D. M., 118
Isard, S., 371
Ischebeck, A., 433
Ishii, R., 289
Itti, L., 144, 145
Ivry, R. B., 43, 48, 57, 66, 74, 76
Izquierdo, I., 63
J., A. (mnemonist), 256
Jack, C. R., 221
Jackendoff, R., 384
Jackson, S., 99
Jackson, S. R., 130
Jacobson, R. R., 225
Jacoby, L. L., 137, 192
Jaffe, E., 85
James, T. W., 303
James, W., 9, 38, 137, 176, 193
Jameson, K. A., 407
Jamis, M., 259
Jan, D., 423
Jäncke, L., 71, 293
Janis, I. L., 504, 505, 518
Janiszewski, C., 495
Jansen-Osmann, P., 290, 293
Jansiewicz, E. M., 169
Jarvin, L., 22
Jefferson, G., 426
Jenkins, J. J., 192
Jensen, A. R., 162
Jensen, P., 164
Jenson, J. L., 504
Jerde, T. E., 370
Jerison, H. J., 78
Jia, G., 413
Jiang, Y., 127
Jiang, Y. V., 248
Jick, H., 438
Johnson, D. R., 410
Johnson, M. K., 211, 229, 253,
263, 405, 420
Johnson-Laird, P. N., 301, 302,
317, 396, 397, 493, 507,
509, 515, 516, 517, 519
Johnston, J. C., 390
Johnston, W. A., 157, 158
Johnstone, S. J., 174
Jolicoeur, P., 287, 289, 290, 301
Jonassen, D. H., 455
Jones, E. G., 48
Jones, G., 341
Jones, P. E., 31
Jonides, J., 314
Jonkers, R., 438
Jonsson, G., 66
Jordan, K., 289, 293, 294
Jordan, P. J., 446
Jung, R. E., 78, 79, 80, 483
Jung-Beeman, M., 459
Jusczyk, P. W., 373
Jussim, L., 521
Just, M. A., 79, 290, 388
Kahneman, D., 4, 156, 488, 489,
493, 494, 496, 497, 498,
500
Kail, R. V., 291
Kalénine, S., 323
Kali (goddess), 42
Kalisch, R., 74
Kalla, R., 145
Kamio, Y., 232
Kan, K. J., 174
Kandel, E. R., 166, 168
Kane, M. J., 209
Kanner, L., 438
Kant, I., 7, 38
Kanwisher, N., 100, 117, 124,
354
Kaplan, E., 395
Kaplan, G. B., 351
Karapetsas, A., 412
Karlin, M. B., 253, 405
Karnath, H. O., 130, 166
Karni, A., 236
Karpicke, J. D., 24, 26
Kaschak, M. P., 361
Kashino, M., 371
Kasparov, G., 478, 480
Kass, S. J., 294
Kassin, S. M., 258
Katz, J. J., 324
Kaufman, A. B., 192, 483
Kaufman, A. S., 18
Kaufman, D. R., 502
Kaufman, J. C., 22, 161, 482
Kaufman, S. B., 481
Kaufmann, L., 174
Kawachi, K., 418
Kay, P., 407, 408
Kaye, J. A., 438
Keane, M. T., 210, 301, 493
Keating, D. P., 192
Keele, S. W., 231
Keenan, J. M., 396
Keil, F. C., 325, 327, 409
Keller, E., 426
Keller, H., 360, 374
Keller, P. E., 276
Kemp, I. A., 161, 165
Kennedy, K. M., 192
Kennedy, R., 12
Kennerley, S. W., 505
Kensinger, E. A., 221, 222, 223,
336
Kentridge, R. W., 181
Keppel, G., 247, 248
Kerr, N., 302
Ketcham, K., 257
Keysar, B., 420
Khader, P., 245
Khubchandani, L. M., 413
Kiesel, A., 468
Kiga, D. L., 78
Kigar, D. L., 52
Kihara, K., 277
Kihlstrom, J. F., 171
Kim, K. H., 417
Kimchi, R., 103
Kimura, D., 293, 435, 436
Kintsch, W., 395, 396, 397, 449
Kirby, J. R., 161
Kirby, K. N., 511
Kirker, W. S., 201
Kirwan, C. B., 256
Kitada, R., 73
Kleim, J. A., 62
Klein, G., 502
Klein, K. L., 129
Kleinhans, N. M., 46
Kliegl, R., 433
Kluger, B., 54
Knauff, M., 517
Knowlton, B. J., 205, 224
Koch, G., 74
Koehler, J. J., 494
Koenig, O., 295
Koffka, K., 113
Köhler, S., 95
Köhler, W., 13, 97, 113, 456,
457F
Koivisto, M., 151
Kok, A., 290, 292
Koko (gorilla), 431
Kolb, B., 51, 129, 432
Kolb, I., 80
Kolodner, J. L., 253
Kolomyts, Y., 480
Komatsu, L. K., 326, 336, 337
Kontogiannis, T., 175
Kopelman, M. D., 225
Kornblum, H. I., 75
Kornilov, S. A., 483
Koscik, T., 294
Kosslyn, S. M., 85, 276, 277, 280,
283, 287, 288, 292, 293,
294, 295, 296, 297, 298,
299, 300, 301, 315
Kotovsky, K., 452
Kounios, J., 459
Koustanai, A., 131
Kozlowski, L., 110
Kraemer, D. J. M., 303
Krampe, R. T., 472
Krantz, L., 494
597
Kreuz, R. J., 397
Krieger, J. L., 175
Kringelbach, M. L., 75
Krishnan, R., 74
Krueger, J., 523
Kruschke, J. K., 322
Kuhl, P. K., 370
Kuiper, N. A., 201
Kulik, J., 255
Kurby, C. A., 276
Kutas, M., 433
Kyllonen, P. C., 208
LaBerge, D., 142, 170
Ladavas, E., 163
Ladefoged, P., 365
Lakoff, G., 423
Laland, K., 13
Lander, K., 118
Langer, E. J., 175, 176
Langley, L. K., 148
Langley, P., 480
Lansman, M., 155
Lanze, M., 110
LaPointe, L. L., 31
Large, M.-E., 99
Larkin, J. H., 446, 467, 469
Larson, G. E., 79
Lashley, K. S., 14, 52, 78, 223
Lau, I., 410
Lawrence, E., 518
Lawson, A. E., 507
Leahey, T. H., 7
Lederer, R., 367, 368
LeDoux, J. E., 56
Lee, D., 198, 505
Lee, F. L., 348
Lee, K. H., 80
Legg, S., 17
Lehman, D. R., 5, 331, 518
Leicht, K. L., 235
Leighton, J. P., 507, 510
Leiman, A. L., 42
Lemmon, K., 395
Lempert, R., 518
Lempert, R. O., 498
Lennie, P., 105
Lennox, B. R., 288
Leonardo da Vinci, 479, 481
Leopold, D. A., 118
Lerner, A. J., 46
Lesgold, A. M., 469, 471, 474
Levin, D. T., 177
Levine, B., 218
Levine, D. N., 306
Levinson, K. L., 129
Levy, J., 51, 54, 56
Lewandowsky, S., 323
Lewis, C., 263
Lewis, M. P., 361
Lewis, R. L., 361
Lewis, S. J. G., 46
598
Name Index
Liberman, A. M., 369, 372, 373
Lichtenberger, E. O., 18
Lichtenstein, S., 498
Lickel, B., 497
Lindem, K., 314
Lindeman, J., 391
Lindsay, D. S., 137
Lindsey, D. T., 408
Linton, M., 253
Lipshitz, R., 502
Little, D. R., 323
Liu, K. P. Y., 277
Llinas, R. R., 143
Locke, J., 7, 38
Lockhart, R. S., 187, 189, 200
Lodi, R., 48
Loebell, H., 378, 384, 419
Loftus, E. F., 199, 253, 257, 258,
260, 261, 262, 334, 345,
406
Loftus, G. R., 199
Logan, G., 170, 173
Logie, R. H., 205, 208, 298
Logothetis, N. K., 75
Lohman, D. F., 467
Longoni, A. M., 294
Lonner, W. J., 404
Lorincz, A., 106–7
Loth, E., 339
Lou, H. C., 163
Louwerse, M. M., 300, 312
Love, B. C., 322
Love, T., 215
Lowenstein, J. A., 261
Lu, C., 472
Lubart, T. I., 479
Lucas, T. H., 436
Luchins, A. S., 460, 461
Luck, S. J., 163, 198
Luka, B. J., 378
Luo, J., 459
Lupton, L., 370
Luria, A., 214
Luria, A. R., 161
Luus, C. A. E., 258
Luzzatti, C., 166
Lycan, W., 11
Lynch, J., 323
M., H. (amnesiac), 218
M., H. (patient), 48, 335–36
Ma, J. E., 518
McAfoose, J., 193
McArthur, T., 418
McBride, D., 190
McCall, L., 99
McCarthy, G., 73, 100
McCarthy, R. A., 376
McClelland, J. L., 46, 212, 237,
349, 351, 352, 353F, 355,
371, 388, 389, 390
McCloskey, M., 259
McCloskey, M. E., 259
McConkie, G., 411
McCormick, C. B., 201
McCormick, D. A., 48, 224
McDaniel, M. A., 78
McDermott, J., 117
McDermott, K. B., 24, 256, 262
McDermott, M. A., 24
MacDonald, J., 373
McDonough, L., 409
McDowd, J. M., 156
Mace, W. M., 98
McGarry-Roberts, P. A., 79
McGarva, A. R., 159
McGaugh, J. L., 224
McGee, S., 455
McGuire, P. K., 289
McGurk, H., 373
McIntosh, A. R., 210
McIntyre, C. K., 63
Mack, M. L., 324
MacKay, D. G., 218
McKenna, J., 259
McKenzie, K. J., 294
McKeown, M. G., 394
McKhann, G. M., 436
McKinley, S. C., 327
Macknik, S. L., 89
McKone, E., 190
McKoon, G., 212, 311, 397
Mackworth, N. H., 142
MacLean, K. A., 142, 160
MacLeod, C. M., 174, 263
MacLin, O. H., 120
McMullen, P. A., 112
McNamara, D. S., 397, 468
McNamara, T. P., 310, 311, 344
McNamara, T. R., 311
McNaughton, B. C., 237, 352
McNaughton, B. L., 237
McNeil, J. E., 129
McNeill, D., 180
Macquet, A. C., 502
McRorie, M., 162
MacSweeney, M., 437
Madden, D. J, 147, 148
Maddieson, I., 365
Maddox, K. B., 347
Maguire, E. A., 310
Makel, M. C., 480
Makovski, T., 248
Malakis, S., 175
Malgady, R., 420
Malpass, R. S., 120, 259
Malsbury, C. W., 48
Malt, B., 325, 325T
Mandler, G., 219
Mangun, G. R., 43, 48, 57, 66,
74, 76, 153
Mani, K., 302
Mankoff, R., 446
Manktelow, K. I., 512
Manns, J. R., 46, 223
Mantyla, T., 265
Maratsos, M. P., 414
Marcel, A. J., 178, 181
Marcus, D., 409
Maril, A., 181
Markman, A. B., 328, 331
Markman, E. M., 331, 367
Markovits, H., 515, 523
Markowitz, H., 488
Marr, D., 16, 27, 85
Marrero, M. Z., 414
Marsh, B., 501
Marsh, R. L., 151
Marsolek, C. J., 112
Martin, L., 404
Martin, M., 104
Martinez-Conde, S., 89
Mascolo, M. F., 313
Massaro, D. W., 370, 374
Masson, M. E. J., 388
Masuda, T., 5
Matarazzo, J. D., 78
Matlin, M. W., 195, 264, 283
Matsui, M., 339
Matsuzawa, T., 311
Matthews, R. J., 214
Mattingley, J. B., 108
Mattingly, I. G., 372
Maunsell, J. H., 111
Maxwell, R. J., 513
May, E., 517
Mayer, R. E., 444F, 453F, 454F
Mazaheri, A., 67
Meacham, J. A., 241
Meade, M. L., 263
Meador-Woodruff, J. H., 48
Mechelli, A., 413, 417
Medin, D. L., 323, 326, 331
Medina, J. H., 63
Meerlo, P., 237
Meeter, M., 157
Meinzer, M., 414
Mejia-Arauz, R., 13
Melnyk, L., 261
Melrose, R. J., 524
Melton, R. J., 518
Memon, A., 262
Merikle, P., 137
Mervis, C. B., 325, 326
Metcalfe, J., 234, 456, 457, 458F
Metcalfe, S., 118
Metzger, W., 89
Metzler, J., 287, 289, 290, 291,
298, 300
Meyer, A. S., 361
Meyer, D., 157
Meyer, D. E., 390, 391
Meyer, M., 314
Meyer, R. E., 389
Micheau, J., 63
Micheyl, C., 148
Middleton, F. A., 51
Miesler, L., 501
Mignot, E., 48
Milani, I., 103
Miller, B., 387
Miller, D. G., 257, 258
Miller, G., 16
Miller, G. A., 16, 198, 354, 367,
371, 420
Miller, J., 155
Miller, M. B., 54
Miller, M. D., 200
Mills, C. J., 201
Milner, A. D., 95, 96, 130
Milner, B., 218, 221
Minagawa-Kawai, Y., 365
Mirman, D., 371
Mirochnic, S., 221
Mishkin, M., 95
Miyamoto, Y., 5
Moar, I., 312
Modafferi, P. A., 257
Modell, H. I., 303
Moettoenen, R., 373
Mohammed, A. K., 66
Monaco, A. P., 387
Monnier, C., 264
Monsell, S., 251
Montague, L., 367
Montello, D. R., 300, 312
Montgomery, K., 120
Mooney, A., 426
Moore, C. M., 259
Moore, K. S., 238
Moran, S., 480, 481
Morawski, J., 7
Moray, N., 151, 153
Morey, R., 378, 384, 419
Mori, M., 79
Morris, C. D., 202
Morrison, T., 479
Morton, J., 388
Morton, T. A., 331
Morton, T. U., 427
Moscovitch, M., 128, 223,
265
Motter, A. E., 366
Motter, B., 142
Mouchiroud, C., 479
Moulton, S. T., 276
Mueller, H. J., 144
Mufwene, S. S., 365
Mulder, A. B., 66
Mulford, M., 523
Mulligan, N. W., 190
Munhall, K. G., 365
Münte, T. F., 71, 150
Murdock, B. B., 193
Murdock, B. B., Jr., 247
Murphy, K., 190
Murray, J., 290
Murray, M. D., 502
Name Index
Naglieri, J. A., 161
Nahmias, C., 120
Naigles, L., 367, 410
Nakayama, Y., 131
Naples, A. J., 444
Nathan, P. W., 218
Nation, P., 394
Naus, M. J., 244
Navalpakkam, V., 144, 145
Navon, D., 103, 104, 156
Neely, J. H., 178
Neisser, U., 16, 38, 99, 152, 153,
154, 160, 200, 255, 266
Nelson, T. O., 232
Neto, F., 460
Nettelbeck, T., 19, 20, 162, 246
Neubauer, A. C., 162
Neville, H. J., 433
New, A. S., 26
Newell, A., 16, 341, 449, 484
Newell, B. R., 421
Newman, A. J., 436
Newman, M. L., 428
Newman, R. S., 149
Newman, S. D., 79, 466
Newton, I., 481
Nichelli, P., 121
Nicholls, M. E. R., 374
Nick, A. M., 248
Nickerson, R. S., 33, 517
Nigg, J. T., 163
Nigro, G., 420
Niki, K., 459
Ninio, J., 126
Nisbett, R. E., 5, 177, 494, 518,
520, 521
Nishino, S., 48
Noble, K., 47
Nolen-Hoeksema, S., 254, 264
Norman, D. A., 152, 174, 175,
176, 193, 241, 248, 287,
308, 474
Nosofsky, R. M., 327
Novick, L. R., 464
Nuerk, H. C., 174
Nyberg, L., 210
Oakhill, J., 395, 397
Oakhill, J. V., 301
Obel, C., 163
Obler, L., 414
O’Brien, D. P., 510
O’Grady, W., 366
Ojemann, G. A., 414, 435, 436
O’Kane, G., 336
O’Keefe, J., 46
Oken, B. S., 143
Okubo, M., 289
O’Leary, D. S., 72
Olesen, P. J., 103
Olivers, C. N. L., 157
Oller, D. K., 412
Öllinger, M., 347
Olsen, T. S., 438
Olseth, K. L., 465
Olsson, M. J., 230
Oppenheimer, D. M., 489
Orasanu, J., 502
Orban, G. A., 127
O’Regan, J. K., 165
O’Reilly, R. C., 237, 352
Ormerod, T. C., 465
Ornstein, P. A., 244
Ortony, A., 336
Osherson, D. N., 85
O’Toole, A. J., 120
Over, D. E., 501, 512
Overton, R., 235
Overton, W., 409
Owen, A. M., 466
Oxelson, E., 412
Ozonoff, S., 439
P., V. (mnemonist), 215
Paap, K. R., 390
Pachur, T., 501
Page, S. E., 443
Paivio, A., 277, 315
Palermo, R., 118
Pallanti, S., 74
Paller, K. A., 219
Palmer, J. C., 406
Palmer, S. E., 109, 110, 113, 115,
116
Palmeri, T. J., 170, 324, 327
Palmiero, M., 276
Paolillo, J. C., 428
Paracchini, S., 387
Paradis, M., 414
Paradise, R., 13
Park, C. R., 234
Park, Y. S., 291
Parker, A., 99
Parker, A. J., 125, 127
Parker, J. D. A., 256
Parron, C., 116
Pascual-Leone, A., 74
Pashler, H., 155
Passafiume, D., 335
Patel, V. L., 502
Patterson, J. C., 72
Pavlov, I., 11, 38
Payne, J., 492
Payne, J. D., 237, 259
PDP Research Group, 349
Pearlstone, Z., 244
Pearson, B. Z., 412
Pearson, D. G., 208
Pecenka, N., 276
Pedersen, P. M., 438
Peigneux, P., 237
Pellizzer, G., 293
Penfield, W., 199, 209
Pennebaker, J. W., 262
Penrod, S., 259
Penrod, S. D., 257
Pepperberg, I. M., 432
Perfetti, C. A., 390, 391, 394
Perlmutter, D., 381
Persaud, K. C., 270
Peru, A., 298
Pesciarelli, F., 343
Peters, E., 518
Peters, M., 290
Petersen, S. E., 163, 390
Peterson, L. R., 247
Peterson, M. A., 92, 286, 287
Peterson, M. J., 247
Pezdek, K., 255, 259
Phaf, R. H., 174
Phelps, E. A., 46, 68, 142, 235
Phillipson, R., 417
Piaget, J., 526
Pichert, J. W., 396
Pickell, H., 436
Picton, T. W., 67
Pierce, K., 438
Piercy, M., 80
Pike, R., 210
Pines, J. M., 518
Pinker, S., 43, 271, 286, 298,
377, 378, 380, 425
Pisoni, D. B., 371
Piven, J., 46
Pizzighello, S., 165
Platek, S. M., 54
Platko, J. V., 476
Plato, 6, 38, 39
Platt, B., 63
Platt, M. L., 505
Plaut, D. C., 351, 389
Plucker, J. A., 480
Poggio, T., 92
Poitrenaud, S., 327
Polanczyk, G., 164
Policastro, E., 482
Polk, T. A., 100
Polkczynska-Fiszer, M., 433
Pollack, M. E., 393
Pollard, P., 518
Pollatsek, A., 157, 386, 387, 388,
390
Pollatsek, S., 411
Pomerantz, J. R., 85, 109, 277
Poortinga, Y. H., 331
Posner, M. I., 68, 72, 73, 143,
161, 163, 170, 231, 344,
390
Postle, B. R., 248
Postma, A., 225
Potter, M. C., 119
Pouget, A., 145
Poulin, R. M., 524
Powell, J. S., 395
Prabhu, V., 481
Pradere, D., 262
599
Pretz, J. E., 444, 482
Pribram, K. H., 16, 354
Prince, S. E., 210, 232
Prinzmetal, W. P., 113
Proffitt, D. R., 126
Proffitt, J. B., 323
Provenzale, J. M, 148
Puetz, P., 90
Pugalee, D. K., 471
Pullum, G. K., 404
Pursglove, R. C., 263
Pyers, J. E., 180
Pylyshyn, Z., 214, 281, 283, 287,
298, 299
Quayle, J. D., 515
Qui, J., 526
Quillian, M. R., 333–34
Quinn, J. J., 31
Quinn, P. C., 116
Rabin, C. S., 276
Radvansky, G. A., 364
Rafal, R., 181
Ragland, J. D., 200
Rahm, E., 337
Raichle, M. E., 68, 72
Rainbow Project Collaborators,
22
Raine, A., 526
Rajah, M. N., 210
Rajan, K., 61
Ralph, M. A. L., 351
Ramachandra, P., 74
Ramirez-Esparza, N., 27
Ramsey, M., 159
Ramus, F., 174
Ranga, K., 74
Rao, H., 47
Rao, R. P. N., 137
Ratcliff, R., 212, 311, 327,
352, 397
Raymond, J. E., 119
Rayner, K., 386, 388, 390,
411
Raz, A., 170, 172
Raz, N., 192
Read, J. D., 253, 255, 394
Reason, J., 175, 176
Reber, P. J., 224
Reed, L. J., 225
Reed, S., 283, 300
Reed, S. K., 443, 449
Reed, T. E., 162
Reeder, G. D., 201
Rees, E., 469
Rees, G., 181
Rees-Miller, J., 366
Regier, T., 407, 408
Reicher, G. M., 110, 390
Reichle, E., 411
Reinholdt-Dunne, M. L., 159
600
Name Index
Reisberg, D., 276, 283, 284,
284F, 285, 286, 300
Reiser, B. J., 297, 300
Reitman, J. S., 248
Remez, R. E., 374
Rensink, R. A., 131
Rescorla, R. A., 11, 429, 430
Reverberi, C., 525
Revonsuo, A., 151
Rey, G., 375
Rhodes, G., 118, 120
Richardson, P., 210
Richardson-Klavehn, A. R., 193
Riedel, G., 63
Riggs, L. A., 89
Rijswijk-Prins, H., 174
Riley, D., 46
Rips, L. J., 329, 330, 334, 507,
510, 513, 524
Ritchie, W. C., 412
Ritter, A., 11
Ritter, F. E., 341
Ro, T., 181
Robbins, S. E., 218
Robbins, T. W., 205
Roberson, D., 408
Robert, N. D., 462
Roberts, A. C., 205
Roberts, J. E., 293
Robins, A., 119
Robinson, S. R., 189
Roca, I. M., 365
Rock, I., 107, 113, 346
Rockland, K. S., 42, 46, 48, 50,
64
Rodman, R., 385, 418
Rodrigue, K. M., 192
Rodriguez, A., 163
Roediger, H. L., III, 24, 240, 241,
256, 262, 263
Rofe, Y., 262
Rogers, R. D., 505
Rogers, T. B., 201
Rogers, T. T., 349, 351
Rogoff, B., 13
Roney, C. J. R., 499
Roozendaal, B., 224, 234
Rosch, E., 325, 356
Rosch, E. H., 323, 324, 325, 326
Rosch Heider, K. G., 407
Rosen, G. D., 51
Rosenberg, K., 434
Rosenzweig, M. R., 42
Ross, B. H., 327, 331, 376, 465
Ross, L., 494, 523
Ross, M., 495
Rostad, K., 162
Rostain, A. L., 164
Roswarski, T. E., 502
Rothbart, M., 161
Rothbart, R., 232
Rothwell, J. C ., 74
Rouder, J. N., 327
Rovee-Collier, C., 264
Rubin, D. C., 253
Rubin, Z., 427
Rudkin, S. J., 208
Rudner, M., 205
Rudolph, J. W., 502
Rugg, M. D., 43
Rumain, B., 510
Rumbaugh, D. M., 430
Rumelhart, D. E., 212, 287, 308,
336, 349, 388, 389, 390,
474
Runco, M. A., 479, 480
Russell, J. A., 310
Russell, W., 303
Russell, W. R., 218
Rychkova, S. I., 126
Rychlak, J. E., 14
Ryle, G., 271
S. (mnemonist), 214–15
Saarinen, T. F., 300, 312
Sabini, J. P., 518
Sabsevitz, D. S., 433
Sacks, H., 426
Saito, S., 418
Salas, E., 504
Salat, D. H., 218
Salmon, D. P., 224
Salthouse, T. A., 474
Samanez-Larkin, G. R., 73
Samuel, A. G., 371
Samuel, A. L., 478
Sapir, E., 404, 410
Sarkar, S., 370, 474
Sarter, M., 50
Sasaki, T., 189
Satterlee-Cartmell, T., 453
Savage-Rumbaugh, S., 431
Savary, F., 515
Scaggs, W. E., 237
Scerri, T., 387
Schacter, D. L., 46, 179, 181,
210, 212, 219, 221, 224,
235, 256, 262, 352
Schaeken, W., 301, 397, 517
Schaller, M., 5, 331
Schank, R. C., 337, 476
Schegloff, E. A., 426
Scheibehenne, B., 501
Schiano, D. J., 312
Schienle, A., 276
Schirduan, V., 165
Schmid, J., 290
Schmidt, H. G., 200
Schmiedek, F., 162
Schneider, W., 142, 170, 187,
234
Schnider, A., 256
Schoenfeld, A. H., 473
Schonbein, W., 14
Schooler, J. W., 262, 406
Schunk, D. H., 425
Schvaneveldt, R. W., 390, 391
Schwartz, H. C., 323
Schwarz, N., 446, 518
Scott, L. S., 324
Scoville, W. B., 218
Seal, M. L., 289
Searle, D. A., 374
Searle, J. R., 420, 423
Sears, L., 46
Seguino, S., 460
Sehulster, J. R., 254
Seidenberg, M. S., 390
Seizova-Cajic, T., 312
Sejnowski, T., 61
Selfridge, O. G., 99, 101–2, 103
Selkoe, D. J., 62
Sells, S. B., 514
Semin, G. R., 403
Seo, D. C., 159
Sera, M. D., 409
Serpell, R., 18
Shah, A. K., 489
Shahin, A. J., 371
Shakespeare, W., 367, 525
Shallice, T., 221, 376, 438
Shannon, C., 15
Shanock, L., 481
Shapiro, K., 436
Shapiro, P., 259
Shapley, R., 105
Sharpe, S. A., 495
Shastri, L., 212, 345
Shatz, M., 365, 410
Shaw, G. B., 387
Shaw, J. C., 16
Shaywitz, B. A., 387
Shaywitz, S. E., 387, 434
Shear, J., 138
Shear, S. A., 159
Sheard, D. E., 255
Shelton, S. T., 504
Shepard, R., 290, 291
Shepard, R. N., 287, 289, 290,
298, 300
Shepherd, A. J., 381
Shepherd, G., 42
Shepherd, G. M., 61
Shiffrin, R., 193, 194, 203
Shiffrin, R. M., 170, 248
Shin, N., 447, 455
Shinoura, N., 166
Shipley, M. T., 50
Shoben, E. J., 209, 334
Shohamy, D., 224
Shulman, H. G., 231
Sicoly, F., 495
Sidner, C. L., 393
Silver, E. A., 471
Silverman, I., 387
Simion, F., 103
Simon, H. A., 16, 177, 263, 341,
354, 443, 449, 450T, 452,
468, 469, 470F, 484, 491
Simons, D. J., 2, 4, 97, 131
Simonton, D. K., 481, 482
Simpson, B. D., 149
Singer, J., 241
Sio, U. N., 465
Sita (legendary woman), 42
Skagerberg, E. M., 259
Skinner, B. F., 11–12, 14, 38
Skotko, B. G., 218
Skurnik, I., 446, 518
Slee, J., 190
Sloboda, J. A., 474
Sloman, S. A., 512, 523, 524
Slovic, P., 489, 498
Smith, A. D., 312
Smith, C., 237, 255
Smith, E. E., 53, 325, 325T, 326,
327, 334, 459
Smith, F., 386
Smith, J., 474
Smith, J. D., 327
Smith, J. K., 483
Smith, L. B., 501
Smith, L. F., 483
Smith, M., 361
Smolensky, P., 351
Snow, D., 290
Snow, J. C., 108
Snyder, C. R. R., 170
Soechting, J. F., 370
Sohn, M. H., 483
Solomon, H., 323
Solso, R., 305, 315
Solstad, T., 224
Sommer, I. E., 435
Sommer, R., 422
Sommers, S. R., 504
Sook Lee, J., 412
Sotak, C., 74
Spalding, T. L., 327, 376
Sparr, S. A., 130
Spear, N. E., 218
Spear-Swerling, L., 386
Spelke, E., 153, 154, 160
Spellman, B. A., 521
Spencer, C., 260
Sperling, G., 194–97
Sperry, R., 53
Sperry, R. W., 304
Squire, L. R., 46, 205, 210, 218,
221, 223, 224, 234, 342
Srinivasan, N., 138
Stacy, E. W., 109
Staller, A., 512
Standing, L., 189
Stankiewicz, B. J., 101
Stankov, L., 161
Stanovich, K. E., 444, 449, 476,
501
Name Index
Stanovich, R. F., 444
Stapel, D. A., 403
Stark, H. A., 219
Starr, C., 93
Starr, L., 93
Steedman, M., 362, 515, 516
Steffanaci, L., 46
Steif, P. S., 471
Stein, B. S., 444
Stein, M., 73
Steinmetz, J. E., 224
Stelmack, R. M., 79
Steriade, M., 48, 143
Stern, C. E., 524
Sternberg, R. J., 4, 18, 20, 21, 22,
44, 45, 57, 60, 91, 105, 170,
193, 263, 333, 386, 395,
420, 443, 444, 446, 447,
448F, 449, 451F, 452F,
454, 456F, 462, 464, 464F,
466, 467, 473, 474, 476,
479, 481, 482, 494, 507,
514F, 521, 522
Sternberg, S., 242, 243, 244,
252
Stevens, A., 312
Stevens, C., 153
Stevens, K. A., 85
Stevens, K. N., 370
Sticht, T., 369
Stickgold, R., 237, 459
Stiles, J., 433
Stine, M., 453
Storms, G., 327
Strayer, D. L., 157, 158, 159
Strogatz, S. H., 365
Stroop, J. R., 174
Strough, J., 500
Struckman, A., 14
Sturt, P., 378
Stuss, D. T., 56
Stylianou, D. A., 471
Sugrue, K., 262
Suh, S., 397
Sullivan, A., 360
Sullivan, E. V., 248
Sun, R., 212
Sun, Y., 491
Sundgren, P. C., 74
Surian, L., 426
Sussman, A. L., 293
Sutton, J., 253
Swanson, J. M., 163
Syssau, A., 264
Szechtman, H., 120
Taatgen, N. A., 348
Taheri, S., 48
Takano, Y., 289
Takeda, K., 290
Talasli, U., 282
Tamsay, J. R., 164
Tan (patient), 66
Tan, M., 483
Tanaka, J. W., 117, 259, 323
Tanaka, K., 105
Tannen, D., 427, 428
Tardif, T., 410
Tarr, M. J., 92, 112, 117, 289
Tartaglia, E. M., 277
Taylor, H., 314
Taylor, J., 138
Taylor, J. R., 31
Taylor, M., 323
Taylor, M. J., 68, 387
Temple, C. M., 210
Terrace, H., 431
Terras, M. M., 386
Tesch-Römer, C., 472
Teuber, H. L., 218, 221
Thagard, P., 323, 443
Thomas, J. C., Jr., 449
Thomas, M. S. C., 352
Thomas, N. J. T., 276
Thomas, S. J., 174
Thompson, P. M., 80
Thompson, R. B., 427
Thompson, R. F., 223, 224
Thompson, W. L., 276
Thomsen, T., 294
Thomson, D. M., 265
Thomspon, W. L., 288
Thorndike, E., 10–11, 38
Thorndyke, P. W., 310, 311, 336
Thorpe, S. J., 323
Thurstone, L. L., 19, 165, 292
Thurstone, T. G., 292
Tian, B., 453
Timothy (acquitted man), 257
Tinazzi, M., 290
Titchener, E., 8
Titchener, E. B., 108
Todd, P. M., 501
Toichi, M., 232
Tolman, E., 12–13, 38
Tolman, E. C., 308, 309
Tomlinson, T. D., 178
Tooby, J., 512
Torabi, M. R., 159
Torff, B., 22
Toro, R., 51
Torrance, E. P., 479, 480
Torrance, P., 484
Torregrossa, M. M., 31
Toth, J. P., 137
Tottenham, N., 46
Tourangeau, R., 420
Towle, B., 476
Townsend, J. T., 244
Trabasso, T., 397
Tranel, D., 121
Treadway, M., 259, 352
Treisman, A., 151, 153
Treisman, A. M., 144, 145
Treit, D., 46
Treue, S., 111
Triandis, H. C., 18
Trick, L. M., 499
Troche, S. J., 67
Tronsky, L. N., 473
Troth, A. C., 446
Tsujii, T., 523
Tsukiura, R., 210
Tugade, M. M., 523
Tulving, E., 46, 187, 191, 200,
201, 209, 210, 219, 235,
238, 244, 265
Turing, A., 14, 476
Turkington, T. G., 148
Turner, T. J., 338
Turtle, J., 216
Turvey, M. T., 99, 372, 373
Tversky, A., 225, 488, 489, 491,
493, 494, 496, 497, 498,
499, 500
Tversky, B., 300, 301, 311, 312,
314
Twain, M., 368
Umiltà, C., 103
Underhill, W. A., 264
Underwood, B. J., 247, 248
Ungerleider, L. G., 95
Unsworth, N., 203
Unterrainer, J. M., 466
Uy, D., 495
Vakil, S., 340
Valentin, D., 468
Valian, V., 378
Vallone, R., 499
Van Daalen-Kapteijns, M., 395
Vandenbulcke, M., 433
Van der Leij, A., 174
Van de Vijver, F. J. R., 331
Van Dijk, T. A., 393, 395, 396
Van Elslande, P., 131
Van Essen, D. C., 95
Van Gogh, V., 479
VanLehn, K., 342, 473
Van Marle, H. J. F., 142
Vanpaemel, W., 327
Van Patten, C., 433
VanRullen, R., 323
Van Selst, M., 289
Van Voorhis, S., 153
Van Zoest, W., 111
Vargha-Khadem, F., 210
Vecchi, T., 294
Venselaar, K., 480
Verdolini-Marston, K., 191
Verfaellie, M., 262
Vernon, P. A., 78, 79
Vikan, A., 180
Vinson, D. P., 419
Vinter, K., 438
601
Vogel, D. S., 54
Vogel, E. K., 198
Vogel, J. J., 54
Vogels, R., 106
Vogels, T. P., 61
Vollmeyer, R., 474
Voltaire, 374
Von Bohlen und Halbach, O.,
62, 64
Von Eckardt, B., 33
Von Frisch, K., 310
Von Helmholtz, H., 465
Von Helmholtz, H. L. F., 107
Voon, V., 64
Voss, J. L., 219
Wackermann, J., 90
Wagenaar, W, 253
Wagner, A., 430
Wagner, A. D., 181
Wagner, A. R., 11, 429
Wagner, D. A., 192
Wagner, M., 300, 308, 311, 312
Wagner, R. K., 22, 476
Wagner, U., 459
Walker, M., 459
Walker, M. P., 238
Walker, P. M., 259
Wall, D. P., 438
Walpurger, V., 169
Walsh, V., 74
Walter, A., 52
Walter, A. A., 285, 285F
Wang, C., 416
Wang, L., 453
Ward, L. M., 129, 310
Ward, T. B., 480
Warner, J., 159
Warren, R. M., 369, 371
Warren, R. P., 371
Warren, T., 388
Warrington, E., 219, 221, 376,
438
Warrington, E. K., 129, 376
Wason, P. C., 507, 509, 510
Wasow, T., 381, 383
Wasserman, D., 498
Waterman, A. H., 260
Waters, D., 437
Waters, H. S., 234
Watkins, K. E., 373
Watkins, M. J., 265
Watson, D. G., 145
Watson, J., 11, 15, 38
Watson, J. M., 263
Watson, O. M., 422
Waugh, N. C., 193, 248
Weaver, C. A., 255
Weaver, G., 200
Weaver, R., 345
Weaver, W., 15
Weber, M., 499
602
Name Index
Webster, M. A., 118
Wedderburn, A. A. I., 151
Wegner, D. M., 177, 178
Weidner, R., 122, 144
Weinberger, D. R., 74
Weingartner, H., 225
Weinshall, D., 92
Weisberg, R. W., 480
Weiskrantz, L., 127, 181, 205,
219
Weisstein, N., 110
Welbourne, S. R., 351
Wells, G. L., 257, 258, 259, 261
Welsh, M. C., 453
Wenke, D., 449, 467
Werner, H., 395
Wernicke, C., 52, 53, 432
Wertheimer, M., 13, 113, 456
Westwood, D. A., 95
Wheeldon, L. R., 361
Wheeler, D. D., 390
Whishaw, B., 80
Whishaw, I. Q., 51, 129, 432
Whitaker, H. A., 414
Whitten, S., 443, 468
Whorf, B. L., 404, 410
Wickens, D. D., 231
Wickett, J. C., 78, 79
Widner, S. C., 460
Wiebe, D., 456, 458F
Wiedenbauer, G., 290
Wiener, S. I., 66
Wiesel, T., 66, 105, 145
Wiesel, T. N., 105
Wilcox, L. M., 423
Williams, A. M., 472
Williams, J. E., 460
Williams, M., 129
Williams, R. N., 507
Williams, S. E., 162
Williams, W. M., 443
Willis, F. N., 422
Wilson, B. A., 65
Wilson, C., 162
Wilson, M. A., 199, 237
Wilson, T. D., 177
Wilt, J. K., 126
Winawer, J., 408
Windham, G. C., 438
Windschitl, P. D., 258
Winocur, G., 128
Wisco, B. E., 254, 264
Wise, R. A., 259
Wisniewski, E. J., 327
Witelson, S. F., 52, 78
Wittgenstein, L., 99, 324
Wittlinger, R. P., 199
Woldorff, M. G., 153
Wolf, O. T., 73
Wolfe, J. M., 111, 146, 157
Wolford, G., 54
Wood, J. V., 254
Wood, N., 151
Woodman, G. F., 198
Woodward, A. L., 367
Woodworth, R. S., 514
Wright, D. B., 259
Wu, L., 465
Wundt, W., 8, 24, 38
Xu, F., 404
Xu, Y., 120
Yamauchi, T., 331
Yang, D., 472
Yang, R., 370, 474
Yang, Y., 526
Yantis, S., 142, 144, 156
Yendrikhovskij, S. N., 408
Yeo, R. A., 78
Yi, D.-J., 119
Yokoyama, S., 417
Yoshikawa, S., 277
Young, A. W., 129
Young, R., 19, 20
Yovel, G., 354
Yuille, J., 216
Yuille, J. C., 261
Zacks, J. M., 289, 293, 294, 300
Zapparoli, P., 298
Zaragoza, M. S., 256, 259
Zaromb, F., 24
Zhang, L. F., 481
Zhang, M., 303
Zhao, L., 118
Zinchenko, P. I., 200
Zola, S. M., 223
Zola-Morgan, S. M., 223
Zoltan, B., 129
Zuidema, L. A., 417
Zumbach, J., 336
Zurif, E. B., 433
Zurowski, B., 483
Zwaan, R. A., 300, 312, 364
Subject Index
Page numbers followed by F indicate figures; T, tables.
A
ACT-R (adaptive control of
thought-rational) model,
344–48, 346F
Adaptation to environment. See
also ADHD (attention
deficit hyperactivity
disorder); Cognitive errors;
Flexibility; Habituation;
Nervous system; Sensory
adaptation
by animals, 112
and brainstem, 50
and change blindness, 165
as evolutionary advantage, 512
in expertise, 472, 475T
intelligence as,
17, 18, 80, 292, 314
knowledge organization as, 340
by limbic systems, 46
via conscious attention,
138, 142
ADHD (attention deficit hyperactivity disorder), 163–65,
169, 182
Agnosia. See Visual agnosia
Alzheimer’s disease
acetylcholine deficit in, 64, 82
applied vs. basic research in,
225
cognitive dysfunction in,
62, 161, 335
diagnosis of, 221–23
and hippocampus, 66, 82, 221
PET scans for, 72–73
Amnesia, 171, 217–21, 226, 343
Amygdala
and anger and aggression,
45, 46, 49
and emotion, 26, 224, 225
and fear, 118
vigilance regulation by, 142
Analogical codes, 281, 310
Angiograms, 68, 69–70
Animal research, 11, 66, 429–32
Aphasia, 52, 379, 436–38, 437F
Apraxia, 54
Arousal response, 64, 160, 161,
169
Artificial intelligence (AI), 14,
33, 337, 476–78
Associationism, 9–10, 38, 97. See
also Functionalism;
Structuralism
Attention. See also Attentionalresources theory; Schizophrenia; Selective attention theories; Task-specific
attention theories
automatic vs. controlled
processes in, 169–70,
172–75, 172T
and brain areas, 57, 160–61
consciousness compared to,
138, 160, 177–81, 182–83
deficits in, 163–66
defined, 137, 137F
functions of, 138, 139T
influences on, 159–60
and intelligence, 161–62
and learning, 119
Attentional blink phenomena,
119, 155
Attentional-resources theory,
155–57, 156F, 183
Attention deficit hyperactivity
disorder. See ADHD
(attention deficit hyperactivity disorder)
Attentive processes.
See Controlled processes
Attenuation model.
See Treisman’s model
Autism
emotional impairment in, 46,
120
language impairment in, 232,
426
orienting dysfunction in, 161
savant ability in, 320
theories of, 438–39
Automatic processes. See also
ACT-R (adaptive control
of thought-rational) model;
Habituation; Preconscious
processing; Unconscious
processing
and attention, 172–75
vs. controlled processes,
169–70, 172T
defined, 183
by experts, 473, 475T, 485
mental rotation as, 291
as preattentive, 152, 169–70
and slips, 175, 176T
and task types, 173
Automatization. See Automatic
processes
Axons, 61–62
B
Base-rate information, 494, 527
Behaviorism, 11–13, 15, 38
Beowulf (epic poem), 364
Biases, 497–99, 514–15, 518,
523, 527. See also
Expectations, influence of;
Heuristics
Bilingualism
advantages vs. disadvantages,
412, 415
and age factors, 413, 417
and brain studies, 436
single- vs. dual-system
hypotheses, 414–15, 415F
Binocular depth cues. See Depth
cues
Blindsight phenomenon, 181, 182
Boredom. See Habituation
Bottom-up perception theories,
96–97, 110, 133. See also
Direct perception theory;
Feature-matching theories;
Recognition-by-components
(RBC) theory; Template
theories
Brain. See also Brain lesions;
Brain research; Cerebral
cortex; Cerebral hemispheres; Localization of
brain functions; Neurons;
Prefrontal cortex (PFC);
Primary motor cortex;
Primary visual cortex;
Visual pathways in brain
as cognition metaphor, 351
death determination of, 50
development of, 44F, 51
disorders of, 46, 64, 75–78
energy used by, 42
and intelligence, 16, 78–80
nurturance effect on, 47
views of, 43F
Brain lesions. See also Lesioning
techniques
and attentional dysfunction,
165–66
and behavioral dysfunction,
65, 66
in blindsight phenomenon, 181
and cognitive deficits,
30, 46, 304
and color perception deficits,
130
and inconclusiveness of study
findings, 435
and memory, 209, 256
MRI detection of, 71
and object recognition, 376
and speech dysfunction, 52,
53F, 432
Brain mapping. See Localization
of brain functions
Brain research, 66, 70T, 75,
81–82, 161, 179. See also
Brain; Brain lesions;
Research methods; Splitbrain patients; Treatment
methods; individual techniques
Brainstem, 50
Brain tumors, 76–77
Broadbent’s model, 150–51,
150F
Broca’s area
and aphasia, 436–38, 437F
and autism, 232
defined, 52
and language, 73, 431
and speech, 53F, 66
Brodmann’s areas, 483
C
Canterbury Tales (Chaucer), 364
Capacity models of attention.
See Attentional-resources
theory
Car accidents
and attention deficits, 136,
157
cell phone use in, 159, 160F
change blindness in, 131
cognitive research on, 34
head injuries from, 77
perceptual distortion in, 3
603
604
Subject Index
Categorical inferences, 521
Categorical perception.
See Speech perception
Categorization of knowledge
exemplar-based, 327
feature-based (defining),
324–25
as organization of concepts,
322–24
prototype-based, 325–26
synthetic theory of, 327–28
as theory-based view of
meaning, 328–31
Causal inferences
vs. correlational evidence, 30,
74, 75, 78, 521
vs. ecological validity, 25,
37–38, 182, 266
via experimental method,
26, 39
Central executive, 204, 205, 208.
See also Executive functions
Central nervous system (CNS).
See Nervous system
Cerebral cortex. See also Cerebral
hemispheres
as cognitive basis, 45, 51, 82
functional areas of, 53F
and language, 414–15
and memory, 223, 226
and neural pathways, 95
and working memory, 206F
Cerebral hemispheres. See also
Brain; Cerebral cortex;
Localization of brain
functions; specific lobes
differences between, 51–56,
82, 304–5, 317, 441
lobe anatomy in, 57F
and mapping disparities, 433
Change blindness, 131, 165
Children. See also ADHD
(Attention Deficit Hyperactivity Disorder); Autism;
Reading
and categorical learning,
325, 327–28, 330–31, 355,
372
as eyewitnesses, 259–61
and language,
14, 408–9, 410, 412, 413,
452
memory errors in, 211
mental rotation automatization in, 291
poverty effects on, 47, 153
stereotype awareness in, 460
Classical decision theory,
489–90, 527. See also
Decision making
Closed-head injuries, 339–40
Closure principle, 113–15
Coarticulation, 369–70
Cocktail party effect, 2–3, 148, 183
Cognition-driven theories of
perception. See Top-down
perception theories
Cognitive disorders. See individual
disorders
Cognitive errors, 34–35, 175–76,
176T, 211, 263, 303. See
also Brain lesions; Deductive reasoning; Fallacies;
Heuristics; Language;
Problem solving; Slips of
the tongue
Cognitive maps, 308–15
Cognitive neuroscience. See
Neuroscience
Cognitive processes. See also
Attention; Brain; Cognitive errors; Cognitive
structures; Context, effect
of; Functionalism; Gestalt
psychology; Information
processing; Localization of
brain functions; Memory,
models of; Nervous system;
Perception
age-related effects on, 147–48,
161
altering via study of, 8, 32
computer models of,
33, 213, 337, 348
vs. consciousness, 177–81,
182–83
cultural influences on,
5, 18, 34, 192–93, 331–32,
402
and emotion,
99, 118, 120, 224, 255, 446
interactivity of, 35
subjects of study,
3, 9, 12, 17, 19, 39
Cognitive psychology. See also
Causal inferences; Cognitive processes; Cognitive
structures; Dialectical
thinking; Intelligence;
Modularity of Mind; Nature
vs. nurture; Neuroscience;
Rationalism vs. empiricism
applications of,
14–15, 33–34, 81, 132,
356, 503
behaviorism compared to,
12, 13, 14, 15, 38
defined, 3–4, 16, 38
philosophical antecedents of,
6–7
psychoanalysis compared to,
418
related fields of, 33–34, 39, 42,
276–77, 479–480, 502
schema for, 336, 337
Cognitive science, 33
Cognitive structures, 35, 37.
See also Brain; Cognitive
processes; Declarative
knowledge; Gestalt psychology; Language; Memory,
models of; Nature vs. nurture; Neural-network models; Procedural knowledge;
Schemas; Semantic-network
models; Structuralism
Cognitivism, 13. See also
Cognitive psychology
Color perception, 89, 95, 108,
129, 130–31, 407–8. See
also Stroop effect
Communication, 361
Computed tomography scans. See
CT (computed tomography) scans
Concepts, 322, 323–24, 326–27,
331, 332, 336. See also
Categorization of knowledge; Declarative knowledge; Language;
Propositions; Rationalism
vs. empiricism; Reasoning;
Schemas
Conditioning, 11, 12, 14, 264,
429–30
Configural-superiority effect, 109,
109F
Configurational system, 116–17,
118, 121
Conjunction search processes,
144–45
Connectionist model, 349–53,
353F, 357, 524
Consciousness. See also Attention; Automatic processes;
Brain; Controlled processes; Introspection; Preconscious processing;
Unconscious processing
of cognitive processes, 177–78
defined, 138, 182
in hypnosis, 171
and memory retrieval,
220, 237, 263–64, 267
Constructive memory, 252–53,
267. See also Eyewitness
testimony, validity of;
Memory
Constructive perception theories,
107–10, 133
Context, effect of. See also
Creativity; Dyslexia;
Encoding; Heuristics;
Pragmatics; Retrieval
on comprehension, 371, 373,
393, 440
on intelligence, 192, 332
on learning, 344, 386,
390–91, 394, 395
on meaning, 323, 336, 377,
396, 397, 399
on memory,
202, 209, 263–65, 267
on perception, 97–99,
109–10, 126–27, 133
on reasoning, 511, 512, 527
on Westerners vs. Asians, 5
Continuity principle, 113–14
Contralateralism, 52, 54–56, 60,
71, 82
Controlled processes
vs. automatic processes, 153,
172–75, 172T
as conscious, 169–70
defined, 183
and mistakes, 175
and object recognition, 152
and task types, 173
Corpus callosum, 52, 53–54. See
also Split-brain patients
Correlational studies, 28–30
Creativity, 364, 479–83, 479F, 485
Cross-disciplinary studies, 33–34,
38–39
CT (computed tomography)
scans, 68–69, 68F, 71, 77
Cultural intelligence (CQ), 18
D
Data-driven theories of perception. See Bottom-up
perception theories
Decay theory, 233–34, 246, 267.
See also Interference theory;
Memory
Decision making. See also
Classical decision theory;
Deductive reasoning;
Heuristics; Localization of
brain functions; Unconscious processing
biases in, 497–99
costs of, 502, 525
fallacies in, 499–501
in groups, 502, 504–5
in natural environments
(naturalistic), 502
and risk communication, 503
Declarative knowledge
ACT-R (adaptive control of
thought-rational) model,
344–48, 346F
basic level, 323–24
defined, 219, 271, 320
exemplar-based categorization, 327
feature-based (defining)
categorization, 324–25
Subject Index
prototype-based categorization, 325–26
schematic representations,
336–40
semantic-network models,
332–36
as theory-based view of
meaning, 328–31
Deductive reasoning
and adaptive schemas, 511–12
in conditional reasoning,
507–9, 508T
connectionist model of, 524
defined, 507, 527
error avoidance in, 518–19
in syllogistic reasoning,
513–17, 514F, 515T, 516F
and Wason “selection task,”
509–11, 510T
Dendrites, 61, 62, 221, 224
Depression, 63T, 64, 81, 264, 501
Depth cues, 124–26, 125F, 126T,
127, 128F, 132–33
Depth perception, 124–30
Dialectical thinking. See also
Nature vs. nurture;
Rationalism vs. empiricism
in linguistic relativity, 410
in selective attention theories,
150
in structuralism vs. functionalism, 7
as synthesis of thesis and
antithesis, 4–5, 13, 36–38
as theory vs. data, 34
Dichotic presentation, 149, 149F
Direct perception theory, 97–99,
133
Discourse, comprehension of,
392–98, 399
Dishabituation. See Habituation
Display-size effect, 143–44, 143F
Distal objects, 88, 88T, 122
Distracter (nontarget) stimuli,
143–44
Divided attention theories, 138,
153–59, 155–57, 158F, 183
Domain specificity.
See Modularity of mind
Dopamine (DA), 63, 64, 161,
163, 164
Dual-code theory, 277–81, 308,
316
Dual-process theory, 523–24, 528
Dyslexia, 174, 351, 372, 386–87.
See also Reading
E
Ebbinghaus Forgetting Curve,
10F
Ecological model. See Direct
perception theory
Ecological validity, 32–33, 39,
316, 356, 439, 484. See also
Causal inferences
Economic model, 489–90
Electroencephalograms (EEGs),
67–68
Electromagnetic spectrum, 92F,
93
Emotional intelligence, 20, 99,
446. See also Autism
Empiricism vs. rationalism. See
Rationalism vs. empiricism
Encoding. See also Encoding
specificity
acoustic vs. semantic,
202, 230, 231, 266
in analogy solving,
467, 467F, 522
context effect on, 235, 263
defined, 187, 230
elaboration of,
200, 202, 226, 255
forms of, 230–33
and hippocampus, 223
semantic, 393–94
stimuli binding during, 211
Encoding specificity, 241, 265,
267. See also Memory;
Retrieval of memory
Environmental cues, 86, 98, 98F,
121–24, 165. See also
Context, effect of; Depth
cues
Epilepsy, 67, 74
Episodic buffer, 204, 205
Episodic memory, 171, 209–10,
223, 226
Essentialism, 330–31
Event-related potential (ERP)
techniques, 67–68, 153, 307
Executive functions, 47, 161,
412, 439. See also Central
executive
Exemplars, 327
Expectations, influence of, 97,
108, 142, 299–301, 412,
521. See also Biases;
Stereotypes
Experimental method, 22–23,
24–25, 28–30, 520
Experimenter bias. See Biases;
Expectations, influence of
Expert-individuation hypothesis,
120
Expertise. See also Artificial intelligence (AI); Biases
characteristics of, 475T
defined, 468
knowledge organization in,
468–71, 473, 485
and practice activity, 472, 477
and talent, 474, 476
Explicit memory, 190, 192, 209.
See also Amnesia; Implicit
memory
External objects. See Distal objects
Extinction phenomenon, 166
Eye, composition of, 93–95, 93F
Eyewitness testimony, validity of,
257–61, 405–6
F
Face recognition, 117–21, 117F,
354
Fallacies, 489, 499–501. See also
Biases; Deductive reasoning; Heuristics
False memories. See Constructive
memory; Decay theory; Interference theory; Memory
Feature analysis system, 116–17,
121
Feature-integration theory, 145,
153
Feature-matching theories, 101–5
Feature search processes, 144–45,
144F. See also Speech
perception
Figure-ground perception, 113–15,
114F
Filter and bottleneck theories.
See Selective attention
theories
Flashbulb memory, 255
Flexibility. See also Adaptation to
environment; Autism;
Functional fixedness
vs. automatic expertise, 473–74
in creative people, 480
and intelligence, 20, 79
in learning, 331, 351
in problem solving, 445, 471
Forcing functions, 175–76, 241
Forebrain, 43, 44, 45–46, 48, 82
Forgetting. See Amnesia; Decay
theory; Interference theory;
Memory
Form and pattern perception. See
Bottom-up perception theories; Gestalt psychology;
Top-down perception
theories
Frontal lobe
and attention deficits, 163,
165
executive functions in, 205, 439
high-level cognitive and motor processes in,
56, 57–58, 82, 459, 466,
525
and intelligence, 80
and lobotomy, 12
and script generation and use,
339
605
Functional-equivalence hypothesis, 287, 288–89, 288T,
293, 317
Functional fixedness, 460, 484.
See also Flexibility
Functionalism, 8–9, 38. See also
Associationism;
Structuralism
Functional magnetic resonance
imaging (fMRI) scans, 73–
74, 119, 211
Fusiform gyrus, 117, 119–21, 129
G
Ganglion cells, 93, 94, 95
Ganzfeld effect, 89–90
Gender differences, 78, 79, 164,
293–94, 409, 434–36
Geons, 106–7, 107F
Gestalt psychology. See also
Speech perception
defined, 13
figure-ground effect, 114F
form perception principles in,
113–16, 113F, 115T, 133
and insightful problem
solving, 455–57, 457F
and mental image manipulation, 286
Global precedence effect, 103, 103F
Glucose metabolism, 79–80
Gray matter, 51, 78, 417
Groupthink, 504–5
Guided search theory, 146–47,
147F
H
Habituation, 167–69, 168T, 177,
183
Head injuries, 77
Hemispheres. See Cerebral
hemispheres
Heuristics. See also Biases;
Fallacies
anchoring, 495
availability, 4, 494–95
in cognitive map manipulation, 310–14, 313F, 317
defined, 490
elimination by aspects, 491–93
fast-and-frugal class of,
501, 503
framing, 496–97
overextension errors, 518
in problem solving,
449, 450T, 451F, 469, 484,
488
representativeness,
493–94, 523, 527
satisficing, 491
Hierarchical models. See
Semantic-network models
606
Subject Index
Hindbrain, 43, 44, 50–51, 82
Hippocampus. See also
Korsakoff’s syndrome
and Alzheimer’s disease,
66, 82, 221
and cognitive maps, 310
and insightful problem
solving, 459
and learning, 81, 237, 336
and memory, 46, 66, 223–24,
226
and stress regulation, 47–48
Hypermnesia, 216–17
Hypotheses. See also specific
hypotheses
in constructive perception, 108
defined, 23
vs. direct perception, 99
formulation of, 36
in inductive reasoning, 520
pattern recognition in, 54
testing of, 28, 31, 127
I
Identity. See Object recognition
Ill-structured problems. See
Problems
Implicit memory, 171, 190–92.
See also Explicit memory
Inattentional blindness, 119,
165. See also Preconscious
processing; Unconscious
processing
Indirect speech theory, 425–26
Inductive reasoning, 519–23,
524, 527–28
Information processing. See also
Connectionist model;
Intelligence; Knowledge
representation; Memory;
“Turing test”
components of, 21
serial vs. parallel, 152, 170,
172T, 213, 341, 356–57
and systematic errors, 35
Information theory, 15, 348
Innate ideas, 7, 36
Insight, 454–59, 457F, 458F, 484
Intelligence. See also Adaptation
to environment; Artificial
intelligence (AI); Multiple
intelligences theory;
Three-stratum model of
intelligence; Triarchic
intelligence theory
assessment of, 18, 21–22, 80
and brain size, 78
as culturally relative,
18, 192–93, 331–32
defined, 17–18
and divided attention, 155
and information processing, 162
and mental rotation, 292
and neural efficiency, 79–80
and perceptual processes,
107–10
and problem solving, 466–68
and working memory, 208–9
Intelligence theory, 161
Intelligent perception theories.
See Constructive perception theories
Interference theory, 233–34,
246, 247–51, 267. See also
Memory
Introspection, 6, 8, 108, 271
Ipsilateralism, 52, 60, 166
K
Knowledge representation, 271,
321–22, 342, 351. See also
ACT-R (adaptive control
of thought-rational) model;
Conditioning; Connectionist model; Declarative
knowledge; Habituation;
Priming effect; Procedural
knowledge
Korsakoff’s syndrome, 46, 48, 225
Kpelle tribe, 331
L
Language. See also Aphasia;
Autism; Bilingualism;
Color perception; Reading;
Sapir-Whorf hypothesis;
Semantics; Speech perception; Syntax
in animals, 429–32
cognitive influence by, 403
components of, 365–68
defined, 360
dialectical differences in,
416–17
and gender differences, 426–29,
434–36
impaired acquisition of, 47
and memory impacts of, 405–6
metaphorical, 419–21
properties of, 361–65, 398–99
psychobiological basis of, 14
psychology of, 380
relativity vs. universality of,
407–10
slips of the tongue, 418–19
social use of (pragmatics),
421, 440
and speech acts,
422–26, 424T, 425T, 427T
syntactical-lexical relationships, 383–85
Late filter model, 152, 152F
Law of Prägnanz, 113
Learning. See also Biases
and attention, 119
from context, 98, 344, 386,
390–91, 394, 395
and distractions, 157
distributed vs. massed practice, 235–36, 237, 238,
240, 267
effects of on brain, 35, 71–72
flexibility in, 331
as new neuronal connections,
14, 61, 62
practice effect on, 173F
and REM sleep, 237
and repetition, 10
by social observations, 13
speed of information processing compared with, 246
Lesioning techniques, 66, 81,
310. See also Brain lesions
Lexical access. See Word
recognition
Lexicon, 367, 376, 383–84, 399,
403
Limbic system, 46, 49
Linguistic-relativity hypothesis.
See Sapir-Whorf hypothesis
Lobes of cerebral hemispheres.
See specific lobes
Localization of brain functions.
See also Attention;
Dyslexia; Working memory
and awake surgical tests, 77
and causal inferences, 75
in cerebral cortex, 48, 51–56
and creativity, 483
in decision making, 505–6
defined, 43
and emotion, 119–20, 142
and gender differences, 434–36
and general applicability, 435
in insight processes, 459
in language differences, 360
and memory, 223–25, 232
in neurons, 61, 62, 63, 63T
in problem solving, 466
in reasoning, 524–26
and semantic processing, 433
in speech, 373, 374
by structure, 45F, 49T–50T,
53F, 57F, 58F–59F, 60F
summary of, 82
in tic disorders, 375
Local precedence effect, 103–4,
104F
M
Macbeth (Shakespeare), 525
Magnetic resonance imaging
scans. See MRI (magnetic
resonance imaging) scans
Magnetoencephalography
(MEG), 74–75
Medulla oblongata, 50–51
Memory. See also Alzheimer’s
disease; Amnesia;
Encoding; Memory, models
of; Retrieval of memory;
Storage of memory;
Working memory
consolidation of, 234, 235,
236–38, 267
as context dependent, 263–65
defined, 187
dysfunctions of, 48, 210, 211,
220–21, 256–61
outstanding cases of,
214–17, 253–56, 472
processing of, 243, 243–44,
243F, 246, 267
repression of, 261–63
retrospective vs. prospective,
241–42
tasks for measuring, 187,
188T, 189–92, 226
and temporal lobe, 186
Memory, models of. See also
Memory
connectionist model,
212–14, 213F, 349–52
levels-of-processing (LOP)
model, 200, 201–2, 201T
multiple memory systems,
209–12, 212F
process-dissociation model,
190, 192
“three-store model,”
193–200, 194F, 203T
working memory model,
203–5, 206F, 207–9, 207F
Mental images
applications of, 276–77
defined, 276
rotations of, 289–94, 290F,
291F
scaling of, 294–96
scanning of, 296–98, 297F
Mental models, 301–4, 515–17,
516F
Mental processes. See Cognitive
processes
Mental representations. See also
Categorization of knowledge;
Dual-code theory; Encoding;
Heuristics; Language; Mental
images; Mental models;
Recognition-by-components
(RBC) theory
ambiguity of, 283–84, 284F
as images vs. words vs. propositions, 273–75, 281–82,
314–15, 317
as organized percepts, 90, 92
as vantage-centric, 111–13
Mental rotations, 289–94, 290F
Subject Index
Merriam-Webster’s Collegiate
Dictionary (2003), 14
Merriam-Webster’s Online
Dictionary (2010), 274
Metabolic imaging techniques,
72–75
Metacognition, 18, 21, 234
Metamemory, 234, 241
Metaphorical language, 419–21
Methodologies. See Research
methods
Midbrain, 43, 48, 82
Mind. See also Localization of
brain functions; Modularity
of mind
nature of, 36–38
as nonobservable black box,
12
philosophical vs. physiological
understanding of, 6
as scientific object of study, 24
structures vs. processes (functions) of, 7–9, 37, 225
Mnemonic devices, 238, 239T,
240T
Modularity of mind
defined, 19
vs. domain generality, 16, 37,
39, 132, 354, 357
in hemispheric differences,
51–56
and specialization of tasks, 130
Modularity of Mind, The (Fodor),
354
Monocular depth cues. See
Depth cues
Morphemes, 365–67, 399
Motor theory of speech perception, 372–73, 374
MRI (magnetic resonance
imaging) scans, 68, 70–71,
71F, 77
Multiple intelligences theory,
19–20, 20T, 165
Myelin sheath, 61–62
N
Naloxone (drug), 64–65
Nature vs. nurture, 4–5, 36, 81,
473–74, 476, 526
Neoplasms. See Brain tumors
Nervous system
central (CNS), 81
chemical activity of, 63
and cognitive correlations,
42, 43, 61
in embryo, 44
peripheral (PNS), 57, 81
Network models. See Neuralnetwork models; Semanticnetwork models
Neural-network models, 355
Neurons
action potential of, 350
binocular, 127
as feature detectors, 145
and information processing,
95
of primary visual cortex,
104–5
in retina, 93–94
stimuli effects on, 66
structure of, 61–62, 62F
viewpoint sensitivity of,
106–7
Neuroscience. See also Brain;
Brain lesions; Cerebral
cortex; Feature-matching
theories; Localization of
brain functions; Recognition-by-components
(RBC) theory; Template
theories; specific brain structures and functions
of aging, 147–48
of attention, 153, 160–61
of childhood poverty, 47
defined, 42
of depth perception, 99, 127
of face recognition, 119–21
of intelligence, 78–80
and neural mapping limits,
351
of vigilance, 142–43
and working memory, 205
Neurotransmitters, 62–64, 63T,
224–25, 226, 350
O
Object recognition. See also
Gestalt psychology; Visual
agnosia
context effect on,
5, 97–99, 109–10
as continuous identity,
89, 95, 138, 408–9
by humans vs. computers, 87
as knowledge-driven, 96
perceptual constancies in,
121–24, 132
perceptual processing in,
88, 88T
via controlled processes, 152
as viewpoint-invariant,
106–7
Object-superiority effect, 110
Occipital lobe, 57, 80, 82, 129,
307, 459. See also Primary
visual cortex
Optical illusions, 90, 91F, 92,
116F, 122, 122F. See also
Perception
Optic pathways. See Visual
pathways in brain
P
Pandemonium model, 101–3,
102F, 104
Parallel distributed processing
(PDP). See Connectionist
model
Parietal-frontal integration
theory (P-FIT), 80
Parietal lobe, 56–57, 58, 80, 82,
161, 221. See also Autism;
Gender differences; Scripts
PASS (Planning, Attention, and
Simultaneous-Successive)
Process Model of Human
Cognition, 161
Pattern recognition. See Feature
analysis system; Gestalt
psychology
Perception. See also Bottom-up
perception theories; Color
perception; Constructive
perception theories; Direct
perception theory; Object
recognition; Optical illusions; Speech perception;
Top-down perception
theories
cognitive role in, 86, 92
deficits of, 127–31, 133
defined, 85
and intelligence, 107–10
as vantage-centric, 111–13
Percepts, 90, 108, 110, 284–85,
287
Peripheral nervous system
(PNS). See Nervous system
PET (positron emission tomography) scans, 68, 72–73
Phonemes, 365, 366T, 399
Phonemic-restoration effect,
371
Phonological loop, 204, 205
Photoreceptors, 94–95
Phrase-structure grammar,
379–81
Positron emission tomography
(PET) scans. See PET
(Positron emission tomography) scans
Postmortem studies, 26, 30,
65–66
Practice effects, 173F, 234
Pragmatics, 421
Pragmatism, 9
Preattentive processes. See
Automatic processes
Preconscious processing, 178–81,
182–83, 391, 466. See also
Automatic processes; Inattentional blindness; Unconscious processing
Prefrontal cortex (PFC), 56, 211
607
Premises
in categorical syllogisms,
513–15, 515T
conclusive errors from, 508,
518, 527
defined, 507
mental models for, 517, 519
Primacy effect, 250, 251F
Primary motor cortex, 57–59,
58F–59F. See also Brain
Primary somatosensory cortex,
58–59
Primary visual cortex, 60, 95,
100, 104–5, 105F, 127. See
also Blindsight; Brain;
Visual pathways in brain
Priming effect
and connectionist model,
212–13, 226
defined, 182–83
and implicit memory, 190–91,
219
in post-hypnotic subjects,
171
and preconscious processing,
178–79
semantic and repetition types,
343–44
Principles of Psychology (James), 9,
137
Proactive interference
(inhibition), 248
Probability, 490, 492–93, 492T,
501, 527. See also Fallacies;
Heuristics
Problems. See also Problem
solving
algorithms, 449
insight, 454–56, 454F, 456F
isomorphic, 450, 451Fb
move, 447, 448F, 450T
Tower of Hanoi, 452–54, 452F
Problem solving. See also
Creativity; Localization of
brain functions; Problems
analogy (structural) recognition in, 462–65, 467F
analysis in, 443–44
by experts vs. novices, 468–71,
470F, 473–74, 475T
incubation in, 465–66, 485
insight role in,
454–59, 457F, 458F, 484
intelligence-glucose ratio
during, 79
mental sets (entrenchment)
in, 460–61
planning in, 466–68
problem representation in,
450, 452–54, 455
problem space model, 449,
451Fa
608
Subject Index
Problem solving (continued)
steps in cycle of, 444–46,
445F, 484
verbal protocol use in, 32
Proceduralization processes. See
Automatic processes
Procedural knowledge. See also
Amnesia
and ACT-R model, 347
and brain, 224, 225
and connectionist models, 213
vs. declarative knowledge, 321
defined, 219, 271, 320
and memory tasks for measuring, 188T, 191
production of, 340–42
tasks for measuring, 191–92
three stages in, 348T
Process-dissociation model, 190,
192
Productive thinking, 456
Propositional codes, 283, 285,
286, 310
Propositional theory, 281–82,
282T, 286
Propositions, 345, 395–96, 507
Prosopagnosia, 121, 129, 133
Prototypes, 325–26, 327
Proxemics, 422–23
Proximity principle, 113–14, 116
PRP (psychological refractory
period) effect. See Attentional blink phenomena
Psycholinguistics, 361, 374
R
Random sample, 25, 28
RAS. See Reticular activating
system
Rationalism vs. empiricism
cognitive psychology as synthesis of, 6–7, 6F, 36–37, 39
in court cases, 266
in deductive vs. inductive
reasoning, 526
in feature-based vs. prototype
theories, 355–56
in functional-equivalence vs.
propositional hypothesis, 301
in perception theories, 132
in philosophical tradition,
6–7, 38
Reaction time, 25, 159, 162, 391,
478, 522. See also ADHD
(attention deficit hyperactivity disorder); Mental rotations; Subtraction methods
Reading, 386–91, 394, 396–98, 399
Reasoning, 507, 523–24. See
also Deductive reasoning;
Inductive reasoning; Localization of brain functions
Recall tasks, 187, 189
Recency effect, 250, 251F
Recognition-by-components
(RBC) theory, 106–7, 133
Recognition tasks, 187, 189
Rehearsal, 234–35, 241, 247, 251
REM sleep, 236–37, 236F
Representational neglect, 298–99
Research methods. See also
Alzheimer’s disease; Brain
lesions; Brain research;
Cross-disciplinary studies;
Postmortem studies; Treatment methods
basic vs. applied,
35, 38, 39, 81, 132, 484
biological vs. behavioral,
38, 81, 182, 225, 439
comparison of, 26T–27T
computer simulations and
artificial intelligence, 33
experimental, 24–25, 28
goals of, 22–23, 36
psychobiological techniques, 30
self-reports, case studies, and
naturalistic observation,
30–33
in vivo techniques, 65, 66
Resource theories. See Attentional-resources theory
Reticular activating system
(RAS), 48, 50
Retina, 93–95, 94F
Retrieval of memory. See also
Encoding specificity;
Hypermnesia; Memory,
models of; Mnemonic
devices; Priming effect
as constructive, 252–53
context effect on, 202, 263–65
defined, 187, 230
emotionality effect on, 224
from long-term memory,
244–46
from short-term memory,
242–44, 243F, 267
Rods and cones. See
Photoreceptors
S
Sapir-Whorf hypothesis, 404–7
Schemas, 248–49, 263, 323,
336–37, 473. See also
Scripts
Schizophrenia
brain activity studies of, 73–74
and dopamine, 63T, 64
as executive attention
dysfunction, 48, 161
memory impairments in, 200
perception vs. imagery in,
288–89
Scripts, 337–40
Search processes (active looking), 138, 143–48, 183
Selective attention, 138, 148–50,
153, 174, 183
Selective attention theories,
150–53, 150F, 152F
Semantic memory, 209–10
Semantic-network models,
332–36, 353, 353F
Semantics, 368, 374–77. See also
Encoding; Syntax
Sensory adaptation, 89, 90, 132,
168, 168T
Septum, 46, 49T
Sequence recall, 229, 241
Serial-position curve, 250, 251F,
254
Serotonin, 63, 63T, 64, 81, 224,
225, 226
Signal (stimulus) detection, 138,
139–40
Signal-detection theory (SDT),
140–42, 141T, 153, 183
Signs and symptoms (2009), 77
Similarity principle, 113–14
Similarity theory, 145–46, 146F
Simultagnosia, 129, 129F, 133
Single- vs. dual-system hypotheses, 414–15, 415F
Sleep, 48, 67, 142–43, 192, 236–
37, 236F. See also Insight;
Neurotransmitters; Reticular
activating system (RAS)
Slips, 175–76, 176T
Slips of the tongue, 418–19
Soma cells, 61
Spacing effect, 235–36
Spatial cognition, 20, 46, 54, 56,
308, 309. See also what/how/
where hypotheses
Spatial neglect (hemi-neglect),
165–66, 166F, 298
Speech perception, 369–74, 399
Split-brain patients, 54–56, 55F,
82, 304. See also Corpus
callosum
Spreading-activation theories,
344, 345, 347, 357
Static imaging techniques,
68–71, 68F–69F
Statistical significance, 23, 28
Stereotypes, 460–61, 497–98
Storage of memory, 187, 223–25.
See also Encoding; Memory;
Memory, models of
Stress, 47, 234, 259
Strokes, 75–76
Stroop effect, 174, 183
Structuralism, 7–8, 38, 108. See
also Associationism;
Functionalism
Subjective expected utility
theory, 490
Subliminal perception. See
Inattentional blindness
Subsidiary “slave systems,” 205
Subtraction methods, 28, 72,
391
Symbolic codes, 278, 281, 296,
308
Symmetry principle, 113–15
Synapses, 62, 224, 350, 355,
357
Syntax. See also Semantics
defined, 367, 399
as descriptive grammar, 377
and lexical structures, 383–85
phrase-structure grammar,
379–81, 382F
and syntactical priming,
378–79
transformational grammar,
381–83
T
Task-specific attention theories,
145–47, 146F, 183
Template theories, 99–100,
101F. See also Speech
perception
Temporal lobe
auditory and language processing in,
57, 59, 82, 376, 433, 434
and color perception deficits,
130
face recognition in, 117, 121
and memory, 99, 186, 256
Texture gradients, 98, 124, 125F,
133
Thalamus, 48, 50, 95
Theories, 23, 34, 331. See also
Rationalism vs. empiricism;
specific theories and models
Theory of multiple intelligences.
See Multiple intelligences
theory
Three-stratum model of intelligence, 19
Tip-of-the-tongue phenomena,
179–81, 183, 256
Top-down perception theories,
96–97, 110, 133. See also
Constructive perception
theories
Tower of Hanoi problem,
452–54, 452F
Transcranial magnetic stimulation (TMS), 69F, 74
Transfer, negative and positive,
462–65, 467F, 485
Transformational grammar,
381–83
Subject Index
Treatment methods. See also
Brain research; Research
methods
for ADHD, 165, 182
for brain tumors, 77
dopamine, 64
gender differences in, 164
repeated magnetic impulses
(rTMS), 74
for substance abuse, 11, 65
Treisman’s model, 145, 150F,
151–52, 153
Triarchic intelligence theory,
20–22, 21F, 170
“Turing test,” 14, 476–77
Two-step model, 152–53
“Two-string” problem, 444F,
453F, 454
U
Unconscious processing. See also
Automatic processes; Inattentional blindness; Preconscious processing
and advertising, 177
in attention, 137
in blindsight phenomenon,
181, 182
in decision making, 136
in implicit memory, 190
as incubation, 465–66
in inferences and judgments,
108
of repressed memories, 261–63
V
Variables, 24–25, 28
Vascular disorders. See Strokes
Vigilance, 138, 139, 142–43,
183
Visual agnosia, 46, 128–30, 133
Visual cortex. See Primary visual
cortex
Visual disabilities: Color-blindness
(2004), 131
Visual imagery
vs. perceptions, 280
principles of, 287, 288T, 300T
vs. spatial imagery,
305, 306F, 307, 316
Visual pathways in brain, 60F,
93, 95–96, 99, 127–28.
See also Primary visual
cortex
Visual perception theories. See
Bottom-up perception
theories; Top-down
perception theories
Visuospatial sketchpad, 204, 205
W
Well-structured problems. See
Problems
Wernicke’s area, 52, 431, 436–38,
437F
What/how/where hypotheses,
95–96, 128
What is traumatic brain injury
(2009), 77
What you need to know about brain
tumors (2009), 76
White matter, 51, 76
Word recognition, 388–91,
389F, 393
Word-superiority effect, 110
609
Working memory. See also
Memory
and acoustical encoding, 231
and ACT-R model, 346F
and attentive, controlled
processes, 152
and brain, 73, 206F
and connectionist model,
212–13, 226
and deductive reasoning
errors, 517
and executive function, 225
and forgetfulness, 246
integrative model of, 203–9
and intelligence, 208–9
as limited and temporary,
182, 194F
and macropropositions,
395–96
and reasoning, 453, 524
and script generation, 339
tasks to assess, 207F
X
X-ray techniques, 68–69
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