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 4
... causal relations among the variables, then we can constrain the kinds of inferences we make still further. For example, if we assume that each cause independently has a certain power to bring about an effect and that this power leads to ...
... causal relations among the variables, then we can constrain the kinds of inferences we make still further. For example, if we assume that each cause independently has a certain power to bring about an effect and that this power leads to ...
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... causal structure from patterns of conditional probability and intervention ... Causal Bayes net representations and learning algorithms allow learners to ... power to turn that input back into a three-dimensional representation of a ...
... causal structure from patterns of conditional probability and intervention ... Causal Bayes net representations and learning algorithms allow learners to ... power to turn that input back into a three-dimensional representation of a ...
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... causal learning, as opposed to principles about mechanisms. Two accounts of causal learning have been particularly influential: associative learning or connectionist accounts and Patricia Cheng's causal power theory (1997). Associative ...
... causal learning, as opposed to principles about mechanisms. Two accounts of causal learning have been particularly influential: associative learning or connectionist accounts and Patricia Cheng's causal power theory (1997). Associative ...
Page 12
... Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an account of human causal learning that resolves some of the difficulties with the R-W account. Cheng proposes that people innately treat covariation as an index of ...
... Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an account of human causal learning that resolves some of the difficulties with the R-W account. Cheng proposes that people innately treat covariation as an index of ...
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... causal learning—the things you say your sprogs are so good at doing and the theories are so bad at explaining—well, they're just the sort of things that the interventionist/causal ... power theory. Psychological Review, 104, 367–405.
... causal learning—the things you say your sprogs are so good at doing and the theories are so bad at explaining—well, they're just the sort of things that the interventionist/causal ... power theory. Psychological Review, 104, 367–405.
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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