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 11
... strength of association. Thus, the stronger the 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 ...
... strength of association. Thus, the stronger the 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 ...
Page 12
... cause and sometimes with the other, and then compare the relative strength of each pairing, but this is an ad hoc modification of the theory. The Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an account of human ...
... cause and sometimes with the other, and then compare the relative strength of each pairing, but this is an ad hoc modification of the theory. The Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an account of human ...
Page 13
... causal Bayes net account seems, well, destined for. My learning algorithms, like your sprogs, can infer causal structure rather than just strength; they can appropriately combine information from interventions and observations and ...
... causal Bayes net account seems, well, destined for. My learning algorithms, like your sprogs, can infer causal structure rather than just strength; they can appropriately combine information from interventions and observations and ...
Page 19
... causation in terms of conditional probabilities, they have obvious affinities with associative theories of causal learning and with the use of contingency information (conditional p) as a measure of causal strength (Dickinson & Shanks ...
... causation in terms of conditional probabilities, they have obvious affinities with associative theories of causal learning and with the use of contingency information (conditional p) as a measure of causal strength (Dickinson & Shanks ...
Page 24
... causal, how they assess causal efficacy or strength in such cases, and the ease with which such relationships can be learned. Double prevention cases suggest that energy transmission is not necessary for causal relatedness. Is it ...
... causal, how they assess causal efficacy or strength in such cases, and the ease with which such relationships can be learned. Double prevention cases suggest that energy transmission is not necessary for causal relatedness. Is it ...
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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