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 9
... specific modules. In particular, modular, domain-specific accounts of causal reasoning do not seem to explain how we identify particular causal rela- tions within a domain, how we make causal infer- ences that transcend domain ...
... specific modules. In particular, modular, domain-specific accounts of causal reasoning do not seem to explain how we identify particular causal rela- tions within a domain, how we make causal infer- ences that transcend domain ...
Page 10
... specific mechanisms of causal transmis- sion to statistical and covariation information in making causal judgments ( Ahn , Kalish , Medin , & Gelman , 1995 ) . Covariation Accounts However , the generative transmission view of causa ...
... specific mechanisms of causal transmis- sion to statistical and covariation information in making causal judgments ( Ahn , Kalish , Medin , & Gelman , 1995 ) . Covariation Accounts However , the generative transmission view of causa ...
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... specific knowledge and conceptual change. In L. A. Hirschfeld & S. A. Gelman (Eds.), Mapping the mind: Domain specificity in cognition and culture; based on a conference entitled “Cultural Knowledge and Domain Specificity,” held in Ann ...
... specific knowledge and conceptual change. In L. A. Hirschfeld & S. A. Gelman (Eds.), Mapping the mind: Domain specificity in cognition and culture; based on a conference entitled “Cultural Knowledge and Domain Specificity,” held in Ann ...
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... specific causal concepts that may be seen as precifications of this more general notion: total causation and direct causation. X is a total cause of Y if and only if it has a nonnull total effect on Y—that is, if and only if there is ...
... specific causal concepts that may be seen as precifications of this more general notion: total causation and direct causation. X is a total cause of Y if and only if it has a nonnull total effect on Y—that is, if and only if there is ...
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... specific and fine- grained interventionist counterfactuals connecting X to Y. We may view this more detailed information, which may be captured by such devices as specific functional relationships linking X and Y, as the natu- ral way ...
... specific and fine- grained interventionist counterfactuals connecting X to Y. We may view this more detailed information, which may be captured by such devices as specific functional relationships linking X and Y, as the natu- ral way ...
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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