Causal Learning: Psychology, Philosophy, and ComputationAlison Gopnik, Laura Schulz Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. |
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... Wellman and David Liu Dynamic Interpretations of Covariation Data 280 Woo-kyoung Ahn, Jessecae K. Marsh, and Christian C. Luhmann Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations 294 Clark Glymour ...
... Wellman and David Liu Dynamic Interpretations of Covariation Data 280 Woo-kyoung Ahn, Jessecae K. Marsh, and Christian C. Luhmann Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations 294 Clark Glymour ...
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... Wellman Department of Psychology Center for Human Growth and Development University of Michigan Ann Arbor, MI 48103 Jim Woodward Division of the Humanities and Social Sciences California Institute of Technology Pasadena, CA 91125 ...
... Wellman Department of Psychology Center for Human Growth and Development University of Michigan Ann Arbor, MI 48103 Jim Woodward Division of the Humanities and Social Sciences California Institute of Technology Pasadena, CA 91125 ...
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... Wellman, 2001). Finally, preschoolers' predictions, causal judgments, and counterfactual inferences are remarkably accurate across a wide range of tasks and content areas (e.g., Flavell, Green, & Flavell, 1995; Gelman & Wellman, 1991 ...
... Wellman, 2001). Finally, preschoolers' predictions, causal judgments, and counterfactual inferences are remarkably accurate across a wide range of tasks and content areas (e.g., Flavell, Green, & Flavell, 1995; Gelman & Wellman, 1991 ...
Page 14
... Wellman, H. M. (1991). Insides and essence: Early understandings of the non-obvious. Cognition, 38,213–244. Gluck, M., & Bower, G. H. (1988). Evaluating an adaptive network model of human learning. Journal of Memory and Language, 27 ...
... Wellman, H. M. (1991). Insides and essence: Early understandings of the non-obvious. Cognition, 38,213–244. Gluck, M., & Bower, G. H. (1988). Evaluating an adaptive network model of human learning. Journal of Memory and Language, 27 ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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actions adults algorithms Bayesian inference Bayesian networks behavior beliefs birth control pills blicket detector Cambridge causal Bayes nets causal chain causal inference causal knowledge causal learning causal Markov condition causal model causal networks causal power causal reasoning causal relations causal relationships causal strength causal structure causal system chapter Cognitive Science common cause computational condition conditional independence conditional probabilities correlation counterfactuals covariation cues deterministic Development Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Hagmayer human independent infants intervention interventionist intuitive theories Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants people’s Piston predictions prior probabilistic probabilistic graphical models probability distribution psychological question Reichenbach represent representation Schulz Sloman Sobel specific statistical stickball Tenenbaum thrombosis tion trials underlying understanding unobserved cause variables Waldmann Wellman Woodward