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 1
... causal learning that my advisor and yours have cooked up for this year at the center in Stanford. My advisor has gone completely crazy over this causal Bayes nets stuff and is insisting that I go to this conference (on the pittance that ...
... causal learning that my advisor and yours have cooked up for this year at the center in Stanford. My advisor has gone completely crazy over this causal Bayes nets stuff and is insisting that I go to this conference (on the pittance that ...
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... causal Bayes nets (see attached). Yours very truly, Brook Russell Attachment 1: Causal Bayes Nets for Dummies Causal Bayes Nets Causal-directed graphical models, or causal Bayes nets, were developed in the philosophy of science and ...
... causal Bayes nets (see attached). Yours very truly, Brook Russell Attachment 1: Causal Bayes Nets for Dummies Causal Bayes Nets Causal-directed graphical models, or causal Bayes nets, were developed in the philosophy of science and ...
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... Causal Bayes nets provide a kind of logic of inductive inference and discovery. They do so, at least, for one type of inference that is particularly important in scientific theory formation. Many scientific hypotheses involve the causal ...
... Causal Bayes nets provide a kind of logic of inductive inference and discovery. They do so, at least, for one type of inference that is particularly important in scientific theory formation. Many scientific hypotheses involve the causal ...
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... Bayes nets treated the graphs as calculation devices—summaries of the conditional probabilities among events. Bayes Nets and Interventions Why think of these graphs as representations of causal relations among variables, rather than ...
... Bayes nets treated the graphs as calculation devices—summaries of the conditional probabilities among events. Bayes Nets and Interventions Why think of these graphs as representations of causal relations among variables, rather than ...
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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), (b) directly ... causal Bayes nets, interventions systematically alter the nature of the graph they intervene on, and these ...
... causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph), (b) directly ... causal Bayes nets, interventions systematically alter the nature of the graph they intervene on, and these ...
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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 chain 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 Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Hagmayer human independent infants intervention interventionist intuitive theories Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants people’s 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