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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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. This page intentionally left blank Contents PART I: Contributors ix Introduction 1 Alison Gopnik and.
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. This page intentionally left blank Contents PART I: Contributors ix Introduction 1 Alison Gopnik and.
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... computation of causal learning. But, every time I look at one of the papers, all I see are unintelligible sentences like this: For any variable R in the directed graph, the graph represents the proposition that for any set S of ...
... computation of causal learning. But, every time I look at one of the papers, all I see are unintelligible sentences like this: For any variable R in the directed graph, the graph represents the proposition that for any set S of ...
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... computation and neurology. We wake up one morning and discover that the account that looked so promising and scientific ... computational or neurological, will first depend on properly psychological accounts of psychological phenomena ...
... computation and neurology. We wake up one morning and discover that the account that looked so promising and scientific ... computational or neurological, will first depend on properly psychological accounts of psychological phenomena ...
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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. these sinister coincidences will not occur. Formally ... computational power to turn that input back into a three-dimensional representation of a table or a lamp without ...
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. these sinister coincidences will not occur. Formally ... computational power to turn that input back into a three-dimensional representation of a table or a lamp without ...
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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. and like logic provides a way of formally specifying ... computational procedures to support causal inference. Your computers may or may not be able to solve this causal ...
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. and like logic provides a way of formally specifying ... computational procedures to support causal inference. Your computers may or may not be able to solve this causal ...
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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