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 21
... on the sorts of changes in X that count as interventions or ideal manipulations. Consider a system in which A atmospheric pressure is a common cause of the reading B of a barometer and a variable S corresponding to the occurrence ...
... on the sorts of changes in X that count as interventions or ideal manipulations. Consider a system in which A atmospheric pressure is a common cause of the reading B of a barometer and a variable S corresponding to the occurrence ...
Page 25
... on the lamp and the appearance of a spot on the wall, the orientation of the lamp and the position of the spot, the effect of inserting a mirror in the path of transmission, and so on. Similarly, in the cue ball example, the relevant ...
... on the lamp and the appearance of a spot on the wall, the orientation of the lamp and the position of the spot, the effect of inserting a mirror in the path of transmission, and so on. Similarly, in the cue ball example, the relevant ...
Page 28
... On the other hand, there is considerable evidence that the answer to Question 2 is yes, for many people at least some of the time. To begin, there is evidence that, in a substantial range of situations, adults learn causal relationships ...
... On the other hand, there is considerable evidence that the answer to Question 2 is yes, for many people at least some of the time. To begin, there is evidence that, in a substantial range of situations, adults learn causal relationships ...
Page 30
... on the position of the switch with respect to the state of the light. Similarly for a baby whose leg is attached by a string to a mobile and who observes a correlation between leg movements and the motion of the mobile. In both cases ...
... on the position of the switch with respect to the state of the light. Similarly for a baby whose leg is attached by a string to a mobile and who observes a correlation between leg movements and the motion of the mobile. In both cases ...
Page 35
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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