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1610An improved probabilistic account of counterfactual reasoningPsychological Review 122 (4): 700-734. 2015.When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a …Read more
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119Theory-based Bayesian models of inductive learning and reasoningTrends in Cognitive Sciences 10 (7): 309-318. 2006.
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81Learning to Learn Causal ModelsCognitive Science 34 (7): 1185-1243. 2010.Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models.…Read more
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77Color Naming Reflects Both Perceptual Structure and Communicative NeedTopics in Cognitive Science 11 (1): 207-219. 2019.Systems for color naming across languages have been a fascinating topic for decades. Zaslavsky and colleagues challenge Gibson's argument that color names are shaped by patterns of communicative need. Using an information‐theoretic analysis, they show that color naming is shaped by both perceptual structure (as is usually argued) but also by communication need.
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56The imaginary fundamentalists: The unshocking truth about Bayesian cognitive scienceBehavioral and Brain Sciences 34 (4): 194-196. 2011.If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlighten…Read more
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27People learn other people’s preferences through inverse decision-makingCognition 168 (C): 46-64. 2017.
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24Learning causal schemataIn McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society, Cognitive Science Society. pp. 389--394. 2007.
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14Corrigendum to “People learn other people’s preferences through inverse decision-making” [Cognition 168 (2017) 46–64]Cognition 175 (C): 201. 2018.
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9None of the above: A Bayesian account of the detection of novel categoriesPsychological Review 124 (5): 643-677. 2017.
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6“Structured statistical models of inductive reasoning”: CorrectionPsychological Review 116 (2): 461-461. 2009.
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3A Computational Approach to Identifying Cultural Keywords Across LanguagesCognitive Science 48 (1). 2024.Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian, hati (heart) in Indonesian and Malay, and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful linguistic analyses (Peeters, 2020b; Wierzbicka, 1990). Because keywords are expected to have different statistical propert…Read more
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Carnegie Mellon UniversityRegular Faculty
Pittsburgh, Pennsylvania, United States of America