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 7
... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence accurately from causal structure and to learn causal structure accurately from patterns of evidence. They constitute a kind of inductive ...
... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence accurately from causal structure and to learn causal structure accurately from patterns of evidence. They constitute a kind of inductive ...
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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 Bayes net account seems, well, destined for. My learning ...
... 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 Bayes net account seems, well, destined for. My learning ...
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... causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 1–31. Gopnik, A., & Wellman, H. M. (1994). The theory theory. In S. A. Gelman & L. A. Hirschfeld (Eds.), Mapping the mind: Domain specificity in ...
... causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 1–31. Gopnik, A., & Wellman, H. M. (1994). The theory theory. In S. A. Gelman & L. A. Hirschfeld (Eds.), Mapping the mind: Domain specificity in ...
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... causal process theories. The former rely on the guiding idea that causes ... causal learning and with the use of contingency information (conditional p) as a measure of ... Bayes net representations, these may be given an interventionist ...
... causal process theories. The former rely on the guiding idea that causes ... causal learning and with the use of contingency information (conditional p) as a measure of ... Bayes net representations, these may be given an interventionist ...
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... causal mechanisms and to formulate a plausible relationship between ... causal judgments and inferences. I suggest that it does: It is involved in or connected to ... Bayes net assumption connecting causation and probabilities: the causal ...
... causal mechanisms and to formulate a plausible relationship between ... causal judgments and inferences. I suggest that it does: It is involved in or connected to ... Bayes net assumption connecting causation and probabilities: the causal ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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