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
... outcome to causally 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 ...
... outcome to causally 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 ...
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... outcomes, thus allowing us to move back and forth between the two kinds of claims. Arguably (see the section on primate causal cognition), the ability to move smoothly from claims about causal structure that follow from information ...
... outcomes, thus allowing us to move back and forth between the two kinds of claims. Arguably (see the section on primate causal cognition), the ability to move smoothly from claims about causal structure that follow from information ...
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... outcome, as when rats learn an association between pressing a lever and the provision of a food pellet. From an ... outcome. Both rat behavior and human causal judgment are (independently of temporal relations) highly sensitive to the ...
... outcome, as when rats learn an association between pressing a lever and the provision of a food pellet. From an ... outcome. Both rat behavior and human causal judgment are (independently of temporal relations) highly sensitive to the ...
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... outcome, which is what one would expect on an interventionist account on causation. In addition, phenomena such as sensitivity to contingency, backward blocking, and causal discounting show that at least some causal representation and ...
... outcome, which is what one would expect on an interventionist account on causation. In addition, phenomena such as sensitivity to contingency, backward blocking, and causal discounting show that at least some causal representation and ...
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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