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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... Sobel and Natasha Z. Kirkham 10 Beyond Covariation : Cues to Causal Structure 154 David A. Lagnado , Michael R. Waldmann , York Hagmayer , and Steven A. Sloman 11 Theory Unification and Graphical Models in Human Categorization 173 David ...
... Sobel and Natasha Z. Kirkham 10 Beyond Covariation : Cues to Causal Structure 154 David A. Lagnado , Michael R. Waldmann , York Hagmayer , and Steven A. Sloman 11 Theory Unification and Graphical Models in Human Categorization 173 David ...
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... Sobel Causality and Mind Lab Brown University Providence , RI 02912 Jessica Sommerville Department of Psychology and Institute for Learning & Brain Sciences University of Washington Seattle , WA 98195 structure of a causal graph ...
... Sobel Causality and Mind Lab Brown University Providence , RI 02912 Jessica Sommerville Department of Psychology and Institute for Learning & Brain Sciences University of Washington Seattle , WA 98195 structure of a causal graph ...
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... Sobel, 2004). To account for the early emergence of structured, coherent, causal knowledge, some psychologists have suggested that children's early causal representations might be largely innate rather than learned. Following Kant's ...
... Sobel, 2004). To account for the early emergence of structured, coherent, causal knowledge, some psychologists have suggested that children's early causal representations might be largely innate rather than learned. Following Kant's ...
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... (Sobel, Tenenbaum, & Gopnik, 2004). If two candidate causes A and B together produce an effect and it is also the case that A by itself is sufficient to produce the effect, then human reasoners (including young children) are less likely ...
... (Sobel, Tenenbaum, & Gopnik, 2004). If two candidate causes A and B together produce an effect and it is also the case that A by itself is sufficient to produce the effect, then human reasoners (including young children) are less likely ...
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... Sobel , D. M. , Schulz , L. , Kushnir , T. , & Danks , D. ( 2004 ) . A theory of causal learning in children : Causal maps and Bayes nets . Psychological Review , 111 , 1–31 . Gopnik , A. , & Wellman , H. M. ( 1994 ) . The theory theory ...
... Sobel , D. M. , Schulz , L. , Kushnir , T. , & Danks , D. ( 2004 ) . A theory of causal learning in children : Causal maps and Bayes nets . Psychological Review , 111 , 1–31 . Gopnik , A. , & Wellman , H. M. ( 1994 ) . The theory theory ...
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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 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 Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Griffiths Hagmayer human independent infants intervention interventionist intuitive theories Journal of Experimental Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants 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