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. Learning Psychology , Philosophy , and Computation Edited by Alison Gopnik Laura Schulz Causal Learning This page intentionally left blank Causal Learning Psychology.
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. Learning Psychology , Philosophy , and Computation Edited by Alison Gopnik Laura Schulz Causal Learning This page intentionally left blank Causal Learning Psychology.
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... Schulz . p . cm . Includes bibliographical references and index . ISBN 978-0-19-517680-3 1. Learning , Psychology of . 2. Causation . I. Gopnik , Alison . II . Schulz , Laura . BF318.C38 2007 153.1'5 - dc22 2006018902 9 8 7 6 5 4 3 2 1 ...
... Schulz . p . cm . Includes bibliographical references and index . ISBN 978-0-19-517680-3 1. Learning , Psychology of . 2. Causation . I. Gopnik , Alison . II . Schulz , Laura . BF318.C38 2007 153.1'5 - dc22 2006018902 9 8 7 6 5 4 3 2 1 ...
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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. This page intentionally left blank Contents Contributors ix Introduction 1 Alison Gopnik and Laura Schulz.
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. This page intentionally left blank Contents Contributors ix Introduction 1 Alison Gopnik and Laura Schulz.
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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. Contents Contributors ix Introduction 1 Alison Gopnik and Laura Schulz PART I : CAUSATION AND INTERVENTION 1 Interventionist Theories of Causation in Psychological ...
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. Contents Contributors ix Introduction 1 Alison Gopnik and Laura Schulz PART I : CAUSATION AND INTERVENTION 1 Interventionist Theories of Causation in Psychological ...
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... Schulz , and Alison Gopnik 14 Learning the Structure of Deterministic Systems 231 Clark Glymour PART III : CAUSATION ... Schulz 15 Why Represent Causal Relations ? 245 Michael Strevens 16 Causal Reasoning as Informed by the Early ...
... Schulz , and Alison Gopnik 14 Learning the Structure of Deterministic Systems 231 Clark Glymour PART III : CAUSATION ... Schulz 15 Why Represent Causal Relations ? 245 Michael Strevens 16 Causal Reasoning as Informed by the Early ...
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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