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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... prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Causal learning : psychology, philosophy, and computation / edited by Alison Gopnik and Laura Schulz. p. cm. Includes bibliographical ...
... prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Causal learning : psychology, philosophy, and computation / edited by Alison Gopnik and Laura Schulz. p. cm. Includes bibliographical ...
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... prior association is, the less learning there will be on any given trial. The model can be applied to human causal learning by substituting causes for the conditioned stimulus and effects for the unconditioned stimulus. The associative ...
... prior association is, the less learning there will be on any given trial. The model can be applied to human causal learning by substituting causes for the conditioned stimulus and effects for the unconditioned stimulus. The associative ...
Page 12
... prior knowledge or temporal cues, people could use data to distinguish causes and effects (i.e., to infer whether A causes B or B causes A). Put another way, both the R-W account and the Cheng account are explanations of how people ...
... prior knowledge or temporal cues, people could use data to distinguish causes and effects (i.e., to infer whether A causes B or B causes A). Put another way, both the R-W account and the Cheng account are explanations of how people ...
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... prior events and then perhaps forward again, as when one reasons that if the barometer reading were low (high), then this would mean that the atmospheric pressure would be low (high), which in turn would mean that the storm would (would ...
... prior events and then perhaps forward again, as when one reasons that if the barometer reading were low (high), then this would mean that the atmospheric pressure would be low (high), which in turn would mean that the storm would (would ...
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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 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