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 19
... by directed graphs as in Bayes net representations, these may be given an interventionist interpretation (Gopnik & Shulz, 2004; Woodward, 2003). It 19 1 Interventionist Theories of Causation in Psychological Perspective.
... by directed graphs as in Bayes net representations, these may be given an interventionist interpretation (Gopnik & Shulz, 2004; Woodward, 2003). It 19 1 Interventionist Theories of Causation in Psychological Perspective.
Page 25
... representations encode facts about conditional probabilities, and that causal learning consists of learning facts about conditional probabilities. Similarly, if a theorist claims, as some adherents of causal process/mechanistic ...
... representations encode facts about conditional probabilities, and that causal learning consists of learning facts about conditional probabilities. Similarly, if a theorist claims, as some adherents of causal process/mechanistic ...
Page 26
... representations of the world. There must be some unified story about this that is both an accurate description of what they do and that enables us to understand how what they do leads, often enough, to normatively correct outcomes.7 ...
... representations of the world. There must be some unified story about this that is both an accurate description of what they do and that enables us to understand how what they do leads, often enough, to normatively correct outcomes.7 ...
Page 27
... representation and judgment are sensitive not only to information about the rates of occurrence of cause and effect and the processes that connect them but also to information about what would or does happen in the absence of the cause ...
... representation and judgment are sensitive not only to information about the rates of occurrence of cause and effect and the processes that connect them but also to information about what would or does happen in the absence of the cause ...
Page 29
... representation of the full technical definition of the notion of an intervention. Taken together, (a) and (b) suggest one way in which it is nonetheless possible for such subjects to use their interventions (note: not their explicit ...
... representation of the full technical definition of the notion of an intervention. Taken together, (a) and (b) suggest one way in which it is nonetheless possible for such subjects to use their interventions (note: not their explicit ...
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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