- Atlanta, Georgia, United States
Phillip Wolff
Emory University, Psychology, Faculty Member
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Research Interests: Psychology, Cognitive Science, Cognition, Knowledge Structures, Causation, and 14 moreLexical Semantics, Medicine, Counterfactual Thinking, Humans, Concept Formation, Female, Male, Experimental, Adult, Psychological Theory, Psychological Models, Causal Models, Knowledge Structure, and photic stimulation
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Research Interests: Psychology, Cognitive Science, Cognition, Knowledge Structures, Visual perception, and 14 moreCausation, Lexical Semantics, Medicine, Counterfactual Thinking, Humans, Causal Learning, Psychological Theory, Psychological Models, Bayes Theorem, Causal Models, Knowledge Structure, Causal Model, Probabilistic Model, and dynamic model
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Patients with Primary Progressive Aphasia (PPA) are usually subtyped into one of the three canonical subtypes (nfvPPA, svPPA, lvPPA) based on a neurological and cognitive assessment including their language characteristics typically... more
Patients with Primary Progressive Aphasia (PPA) are usually subtyped into one of the three canonical subtypes (nfvPPA, svPPA, lvPPA) based on a neurological and cognitive assessment including their language characteristics typically measured from a battery of confrontational tests. While widely used, this classification system has been criticized, and also the approach makes assumptions about the features of language that are important to measure. Here we use methods from Artificial Intelligence to measure features of speech from naturalistic connected speech samples with the goal of determining how well this data‐driven approach matches independent clinical subtypes and how its results relate to neuroanatomical abnormalities measured from MRI.
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Humans have a remarkable ability to think about the future. Our abilities to think about the future are essential for the level of goal construction, planning, and execution of plans that is only observable in humans. Thinking about the... more
Humans have a remarkable ability to think about the future. Our abilities to think about the future are essential for the level of goal construction, planning, and execution of plans that is only observable in humans. Thinking about the future has also been found to be important for the development of sense of self and for health and well-being. In spite of the importance of future-oriented thought, very little empirical work has been conducted on the nature of future-oriented thought. In this research, we demonstrate how automated methodologies can be used to identify references to the future from natural text (Study 1) and how machine-learning techniques can be used to identify categories of future-oriented thought (Study 2). We also demonstrate how the categories that emerge from these analyses can help us better understand the relation between future-oriented thought and many of the positive outcomes associated with future-oriented thought (Study 3).
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Features of the physical world may be acquired from the statistical properties of language. Here we investigate how the Transformer Language Model T5 is able to gain knowledge of the visual world without being able to see or feel. In a... more
Features of the physical world may be acquired from the statistical properties of language. Here we investigate how the Transformer Language Model T5 is able to gain knowledge of the visual world without being able to see or feel. In a series of four studies, we show that T5 possesses an implicit understanding of the relative sizes of animals, their weights, and their shapes, but not their colors, that aligns well with that of humans. As the size of the models was increased from 60M to 11B parameters, we found that the fit to human judgments improved dramatically, suggesting that the difference between humans and these learning systems might ultimately disappear as the parameter sizes grow even larger. The results imply that knowledge of the perceptual world—and much of semantic memory—might be acquired in dis-embodied learning systems using real-time inferential processes
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Recent models of causation place the concept of CAUSE within a broader framework of concepts that includes the notions of LETTING, HINDERING, HELPING and PREVENTING. In each model, the related concepts are defined in terms of a small set... more
Recent models of causation place the concept of CAUSE within a broader framework of concepts that includes the notions of LETTING, HINDERING, HELPING and PREVENTING. In each model, the related concepts are defined in terms of a small set of conceptual distinctions. This paper examines the
Imagining the future and remembering the past both involve mental time travel. This commonality could indicate shared mental processes, as held by the Constructive Episodic Simulation Hypothesis (Schacter & Addis, 2008), or else... more
Imagining the future and remembering the past both involve mental time travel. This commonality could indicate shared mental processes, as held by the Constructive Episodic Simulation Hypothesis (Schacter & Addis, 2008), or else interactive processes that complement one another, a possibility we call the Complementarity Hypothesis. According to the Complementarity Hypothesis, future thoughts are constructed from schemas making them episodically poor, whereas past thoughts are constructed from schemas and direct retrieval of memory traces, making them relatively episodically rich. We tested these hypotheses using machine learning to data mine mental operations in language, much as a geologist can recover physical processes from the geological record. People’s natural, unprompted talk on web blogs was automatically analyzed for past, present, and future references using a temporal orientation classifier. In Study 1, we found that perceptual details were mentioned more often in past th...
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Using Web Corpus Statistics to Infer Conceptual Structure Brandon M. Lock Emory University Eugene Agichtein Emory University Kevin J. Holmes Emory University Phillip Wolff Emory University Abstract: The basic level is the level of... more
Using Web Corpus Statistics to Infer Conceptual Structure Brandon M. Lock Emory University Eugene Agichtein Emory University Kevin J. Holmes Emory University Phillip Wolff Emory University Abstract: The basic level is the level of conceptual structure at which categories are maximally informative. In this research, we investigated whether the privileged status of the basic level might be captured by the statistical properties of the Web. Using Google’s Web search programming interface, we found that frequency ratios for terms across three levels of abstraction (superordinate, basic, and subordinate) significantly predicted human participants’ spontaneous labeling of images obtained via Mechanical Turk. Specifically, the Web statistics paralleled participants’ preference for superordinate labels for natural kinds (e.g., trees, fish) and basic-level labels for other categories. Further, analyses of genre-specific text from the Corpus of Contemporary American English revealed that chil...
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Causal illusions and occult forces Hadar Naftalovich Emory University Jason Shepard Emory University Phillip Wolff Emory University Abstract: Causal illusions are situations in which a causal relationship is inferred even though a... more
Causal illusions and occult forces Hadar Naftalovich Emory University Jason Shepard Emory University Phillip Wolff Emory University Abstract: Causal illusions are situations in which a causal relationship is inferred even though a physical mechanism is not possible. We offer an explanation of such illusions in terms of a two-process account of causal understanding. According to this view, judgments of causation involve two processes: an initial fast process involving the sensation of force that gives rise to the impression of causation, and a second, more strategic process that determines whether there exists a mechanism for the transmission of force. This account was tested in two lines of research in which participants were shown near-photorealistic animations of possible and impossible causal events. Experiment 1 showed that people attribute causation in the absence of legitimate mechanisms. Experiment 2 showed that impressions of causation, even those without possible mechanisms...
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this article, using Kepler's work as a case study, we argue that analogical reasoning Requests for reprints should be sent to Ded C-enmer, Department of Psychology, Northwestern University, 2029 Sheridan Road, Evnnston. IL... more
this article, using Kepler's work as a case study, we argue that analogical reasoning Requests for reprints should be sent to Ded C-enmer, Department of Psychology, Northwestern University, 2029 Sheridan Road, Evnnston. IL 60208--2710. 4 GENTNER ET AL
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GENES F O R S A L E Privatization as a Conservation Policy Joseph Henry Vogel "All conservationists should read and ponder the contents of this book and hopefully apply m a n y of its ideas." —Ghillean T. Prance, Director, Kew... more
GENES F O R S A L E Privatization as a Conservation Policy Joseph Henry Vogel "All conservationists should read and ponder the contents of this book and hopefully apply m a n y of its ideas." —Ghillean T. Prance, Director, Kew Royal Botanic Garden 1994 176 pp.; 3 illus.; $29.95 EVOLUTION BY ASSOCIATION A History of Symbiosis Jan Sapp In this comprehensive history of symbiosis theory—the first to be written—Jan Sapp masterfully traces its development from modest beginnings in the late nineteenth century to its current status as one of the key conceptual frameworks for the life sciences. 1994 272 pp. paper $24.95/cloth $49.95
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Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by... more
Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people’s ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed.