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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... developmental and philosophical and computational research and involving many of the authors in this volume. We are ... Psychology Yale University New Haven,
... developmental and philosophical and computational research and involving many of the authors in this volume. We are ... Psychology Yale University New Haven,
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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. 10 11 13 8 Teaching the Normative Theory of ... Development of Explanations 261 Henry M. Wellman and David Liu Dynamic Interpretations of Covariation Data 280 Woo-kyoung ...
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. 10 11 13 8 Teaching the Normative Theory of ... Development of Explanations 261 Henry M. Wellman and David Liu Dynamic Interpretations of Covariation Data 280 Woo-kyoung ...
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... Psychology and Institute for Learning & Brain Sciences University of Washington Seattle, WA 98195 Michael Strevens ... Development University of Michigan Ann Arbor, MI 48103 Jim Woodward Division of the Humanities and Social Sciences ...
... Psychology and Institute for Learning & Brain Sciences University of Washington Seattle, WA 98195 Michael Strevens ... Development University of Michigan Ann Arbor, MI 48103 Jim Woodward Division of the Humanities and Social Sciences ...
Page 1
... developmental psychologist. But, I'm suspicious about whether philosophy and computation have much to offer. The history of cognitive development, and the study of learning more generally, has been a history of theoretical answers that ...
... developmental psychologist. But, I'm suspicious about whether philosophy and computation have much to offer. The history of cognitive development, and the study of learning more generally, has been a history of theoretical answers that ...
Page 2
... developmental psychologists not to believe our eyes. Actually, I think Gopnik puts it quite well in her book about ... psychology if you'll explain those directed acyclic graphs in plain English words? So, how about it? All best, Morgan ...
... developmental psychologists not to believe our eyes. Actually, I think Gopnik puts it quite well in her book about ... psychology if you'll explain those directed acyclic graphs in plain English words? So, how about it? All best, Morgan ...
Other editions - View all
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 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