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Abstract Research on statistical learning in adults and infants has shown that humans are particularly sensitive to statistical properties of the input. Early experiments in artificial grammar learning, for instance, show a sensitivity... more
Abstract Research on statistical learning in adults and infants has shown that humans are particularly sensitive to statistical properties of the input. Early experiments in artificial grammar learning, for instance, show a sensitivity for transitional n-gram probabilities.
Are comprehension and production a single, integrated skill, or are they separate processes drawing on a shared abstract knowledge of language? We argue that a fundamental constraint on memory, the Now-or-Never bottleneck, implies that... more
Are comprehension and production a single, integrated skill, or are they separate processes drawing on a shared abstract knowledge of language? We argue that a fundamental constraint on memory, the Now-or-Never bottleneck, implies that language processing is incremental and that language learning occurs on-line. These properties are difficult to reconcile with the ‘abstract knowledge’ viewpoint, and crucially suggest that language comprehension and production are facets of a unitary skill. This viewpoint is exemplified in the Chunk-Based Learner, a computational acquisition model that processes incrementally and learns on-line. The model both parses and produces language; and implements the idea that language acquisition is nothing more than learning to process. We suggest that the Now-or-Never bottleneck also provides a strong motivation for unified perception-production models in other domains of communication and cognition.
Research Interests: Psychology, Cognitive Psychology, Cognitive Science, Languages and Linguistics, Language Acquisition, and 8 moreMemory (Cognitive Psychology), Computational Modeling, Psycholinguistics, Developmental Psycholinguistics, First Language Acquisition, Language Production, Language Comprehension, and Learning and Memory
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ABSTRACT Rational theories of decision making under risk typically assume that outcomes can be associated, either directly or implicitly, with utilities; and that decision makers choose options which exhibit the greatest utility. That is,... more
ABSTRACT Rational theories of decision making under risk typically assume that outcomes can be associated, either directly or implicitly, with utilities; and that decision makers choose options which exhibit the greatest utility. That is, the different choice options are assigned a value, independent of comparison with each other, and higher value options are chosen preferentially. This chapter argues for a local comparative theory of choice, and against value-based approaches (whether value or utility is interpreted psychologically or instrumentally), in the light of current psychological and neuroscientific data. Local comparison, not valuation, is a cognitively fundamental operation. Options can be compared with respect to each other, in the light of a specific local choice context; but not valued individually, on an internal utility scale. Section 4.1 explores what can be learned from related debates in psychophysics: specifically, it considers the debate concerning the existence of internal scales for perceptual magnitudes. Section 4.2 outlines a specific theory of choice. Sections 4.3 and 4.4 briefly consider the behavioural and neuroscientific evidence for comparative vs. value-based accounts. Section 4.5 looks at the relevance of these issues for wider issues in neuroscience, psychology, economics, and ethics. © The International Association for the study of Attention and Performance, 2011. All rights reserved.
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Research Interests: Psychology, Cognitive Science, Field Theory, Decision Making, Adolescent, and 14 moreProbability, Humans, Prospect Theory, Female, Male, Context Effect, Expected Utility, Expected Utility Theory, Adult, Psychological Theory, Psychological Models, Choice Behavior, Rank Dependent Utility, and Random Allocation
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Page 1. CHAPTER 6 Probabilities and Pragmatics in Conditional Inference: Suppression and Order Effects Mike Oaksford Cardiff University, UK and Nick Chater University of Warwick, UK INTRODUCTION Over the last few years ...
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... reasoning 335 Valerie A. Thompson 19 A multi-layered dual-process approach to conditional reasoning 355 Niki Verschueren and ... Contributors Nilufa Ali Cognition, Perception and Brain Sciences, University College London, UK David... more
... reasoning 335 Valerie A. Thompson 19 A multi-layered dual-process approach to conditional reasoning 355 Niki Verschueren and ... Contributors Nilufa Ali Cognition, Perception and Brain Sciences, University College London, UK David O'Brien Baruch College and the Graduate ...
... inference and constraint satisfaction: Reconciling mental models and the probabilistic approach Mike Oaksford and Nick Chater Introduction ... will only happen under certain circum-stances (familiar from suppression experiments... more
... inference and constraint satisfaction: Reconciling mental models and the probabilistic approach Mike Oaksford and Nick Chater Introduction ... will only happen under certain circum-stances (familiar from suppression experiments [Byrne, 1989; Cummins, Lubarts, Alksnis, & Rist ...
Chapter 21 Open issues in the cognitive science of conditionals Nick Chater and Mike Oaksford The chapters in this book have provided an ... there is currently little empirical support for dis-sociable processes in human learning in a... more
Chapter 21 Open issues in the cognitive science of conditionals Nick Chater and Mike Oaksford The chapters in this book have provided an ... there is currently little empirical support for dis-sociable processes in human learning in a range of domains (eg, Boakes, 1989, Brewer ...
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ABSTRACT
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Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing... more
Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.
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Chapter 7 Causal discounting and conditional reasoning in children Nilufa Ali, Anne Schlottmann, Abigail Shaw, Nick Chater, and Mike Oaksford Introduction Conditional reasoning is widely viewed as central to human inference and a paradigm... more
Chapter 7 Causal discounting and conditional reasoning in children Nilufa Ali, Anne Schlottmann, Abigail Shaw, Nick Chater, and Mike Oaksford Introduction Conditional reasoning is widely viewed as central to human inference and a paradigm case of logical thought. ...
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An essential element of goal-directed decision-making in social contexts is that agents' actions may be mutually interdependent. However, the most well-developed approaches to such strategic interactions, based on the Nash equilibrium... more
An essential element of goal-directed decision-making in social contexts is that agents' actions may be mutually interdependent. However, the most well-developed approaches to such strategic interactions, based on the Nash equilibrium concept in game theory, are sometimes too broad and at other times 'overlook' good solutions to fundamental social dilemmas and coordination problems. The authors propose a new theory of social decision-making-virtual bargaining-in which individuals decide among a set of moves on the basis of what they would agree to do if they could openly bargain. The core principles of a formal account are outlined (vis-à-vis the notions of 'feasible agreement' and explicit negotiation) and further illustrated with the introduction of a new game, dubbed the 'Boobytrap game' (a modification on the canonical Prisoner's Dilemma paradigm). In the first empirical data of how individuals play the Boobytrap game, participants' experiment...
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... in Eliciting Cooperation in Dilemma Situations: Experimental Manipulations Robyn At Dawes, Mphons JC Van de Kragt and John At ... Joseph Henrich, Robert Boyd, Samuel Bowles, Colin Camerer, Ernst Fehr, Herbert Gintis, Richard... more
... in Eliciting Cooperation in Dilemma Situations: Experimental Manipulations Robyn At Dawes, Mphons JC Van de Kragt and John At ... Joseph Henrich, Robert Boyd, Samuel Bowles, Colin Camerer, Ernst Fehr, Herbert Gintis, Richard McElreath, Michael ALvard, Abigail Ban; Jean ...
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Learnability theory is a body of mathematical and computational results concerning questions such as: when is learning possible? What prior information is required to support learning? What computational or other resources are required... more
Learnability theory is a body of mathematical and computational results concerning questions such as: when is learning possible? What prior information is required to support learning? What computational or other resources are required for learning to be possible? It is therefore complementary both to the computational project of building machine learning systems and to the scientific project of understanding learning in people and animals through observation and experiment. Learnability theory includes work within a variety of theoretical frameworks, including, for example, identification in the limit, and Bayesian learning, which idealize learning in different ways. Learnability theory addresses one of the foundational questions in cognitive science: to what extent can knowledge be derived from experience? WIREs Cogn Sci 2013, 4:299-306. doi: 10.1002/wcs.1228 For further resources related to this article, please visit the WIREs website.
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Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive... more
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.
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Herding is a form of convergent social behaviour that can be broadly defined as the alignment of the thoughts or behaviours of individuals in a group (herd) through local interaction and without centralized coordination. We suggest that... more
Herding is a form of convergent social behaviour that can be broadly defined as the alignment of the thoughts or behaviours of individuals in a group (herd) through local interaction and without centralized coordination. We suggest that herding has a broad application, from intellectual fashion to mob violence; and that understanding herding is particularly pertinent in an increasingly interconnected world. An integrated approach to herding is proposed, describing two key issues: mechanisms of transmission of thoughts or behaviour between agents, and patterns of connections between agents. We show how bringing together the diverse, often disconnected, theoretical and methodological approaches illuminates the applicability of herding to many domains of cognition and suggest that cognitive neuroscience offers a novel approach to its study.
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