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 ix
... Department of Psychology New York University New York, NY 10003 Thomas Richardson Department of Statistics University of Washington Seattle, WA 98195 Michael Strevens Department of Philosophy New York University New York ix Contributors.
... Department of Psychology New York University New York, NY 10003 Thomas Richardson Department of Statistics University of Washington Seattle, WA 98195 Michael Strevens Department of Philosophy New York University New York ix Contributors.
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... statistics is aware of that. So, how could mere sprogs of 3 or 4 years be expected to use anything like Bayes net ... statistical literature (Glymour, 2001; Pearl, 1988, 2000; Spirtes, Glymour, & Scheines, 1993). Scientists seem to ...
... statistics is aware of that. So, how could mere sprogs of 3 or 4 years be expected to use anything like Bayes net ... statistical literature (Glymour, 2001; Pearl, 1988, 2000; Spirtes, Glymour, & Scheines, 1993). Scientists seem to ...
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... statistical analysis ) , by examining the consequences of interven- tions ( as in experiments ) , or usually , by combining the two types of evidence . Causal Bayes nets formal- ize these kinds of inferences . In causal Bayes nets ...
... statistical analysis ) , by examining the consequences of interven- tions ( as in experiments ) , or usually , by combining the two types of evidence . Causal Bayes nets formal- ize these kinds of inferences . In causal Bayes nets ...
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... statistical view of causal relations, in which the causal connec- tion between events is determined by the covariation of cause and effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms ...
... statistical view of causal relations, in which the causal connec- tion between events is determined by the covariation of cause and effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms ...
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... statistical and covariation information in making causal judgments ( Ahn , Kalish , Medin , & Gelman , 1995 ) . Covariation Accounts However , the generative transmission view of causa- tion in particular and domain - specific knowledge ...
... statistical and covariation information in making causal judgments ( Ahn , Kalish , Medin , & Gelman , 1995 ) . Covariation Accounts However , the generative transmission view of causa- tion in particular and domain - specific knowledge ...
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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