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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Page 3
... causal facts, just as the mathematical structure of geometry captures important spatial regularities. Causal graphical models capture just the right kind of asymmetries in causal relations, allow one to generate the appropriate ...
... causal facts, just as the mathematical structure of geometry captures important spatial regularities. Causal graphical models capture just the right kind of asymmetries in causal relations, allow one to generate the appropriate ...
Page 4
... causal structure of the world . Scientists infer causal structure by observing the patterns of con- ditional probability among events ( as in statistical analysis ) , by examining the consequences of interven- tions ( as in experiments ) ...
... causal structure of the world . Scientists infer causal structure by observing the patterns of con- ditional probability among events ( as in statistical analysis ) , by examining the consequences of interven- tions ( as in experiments ) ...
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... causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph) ... structure (soli- tary drinking will lead to insomnia, and sober partying will not for Graph 1; sober partying will ...
... causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph) ... structure (soli- tary drinking will lead to insomnia, and sober partying will not for Graph 1; sober partying will ...
Page 7
... causal structure from patterns of condi- tional probability and intervention ( Glymour & Cooper , 1999 ; Spirtes et ... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evi- dence ...
... causal structure from patterns of condi- tional probability and intervention ( Glymour & Cooper , 1999 ; Spirtes et ... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evi- dence ...
Page 8
... causal learning devices in the universe. Thirty years of work on the “theory theory” shows that children have abstract, coherent representations of the causal structure of the world. Those representations allow children to make ...
... causal learning devices in the universe. Thirty years of work on the “theory theory” shows that children have abstract, coherent representations of the causal structure of the world. Those representations allow children to make ...
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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