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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... correlated , we mean that they are dependent in probability . When we say that x and y are correlated but that that correlation disappears when z is partialed out , we mean that x and y are independent in probability conditional on z ...
... correlated , we mean that they are dependent in probability . When we say that x and y are correlated but that that correlation disappears when z is partialed out , we mean that x and y are independent in probability conditional on z ...
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... correlation in biology . Oxford , England : Oxford University Press . Silva , R. , Scheines , R. , Glymour , C. , & Spirtes , P. ( 2003 ) . Learning measurement models for unobserved variables . Proceedings of the 18th Conference on ...
... correlation in biology . Oxford , England : Oxford University Press . Silva , R. , Scheines , R. , Glymour , C. , & Spirtes , P. ( 2003 ) . Learning measurement models for unobserved variables . Proceedings of the 18th Conference on ...
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... correlated with Y does not imply that manipulating X is a way of changing Y, while the claim that X causes Y does ... correlated, then X causes Y. (NC) If X causes Y, then (a) there are possible interventions that change the value ...
... correlated with Y does not imply that manipulating X is a way of changing Y, while the claim that X causes Y does ... correlated, then X causes Y. (NC) If X causes Y, then (a) there are possible interventions that change the value ...
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... correlated with the value of B , and ( NC ) will judge , correctly , that B does not cause S. Note that , in this case , merely observing the values of B and S that are generated by the ABS structure without any intervention is a ...
... correlated with the value of B , and ( NC ) will judge , correctly , that B does not cause S. Note that , in this case , merely observing the values of B and S that are generated by the ABS structure without any intervention is a ...
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... correlations that will be observed in the absence of these interventions. Although an interventionist account does not attempt to reduce causal claims to information about conditional probabilities, it readily agrees that such ...
... correlations that will be observed in the absence of these interventions. Although an interventionist account does not attempt to reduce causal claims to information about conditional probabilities, it readily agrees that such ...
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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