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 10
... Cheng's causal power theory (1997). Associative Learning and Connectionist Accounts of Causal Learning Although not all contingencies are causal, all causal relationships involve contingencies. There is a vast body of literature on ...
... Cheng's causal power theory (1997). Associative Learning and Connectionist Accounts of Causal Learning Although not all contingencies are causal, all causal relationships involve contingencies. There is a vast body of literature on ...
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... Cheng, 1997; Glymour, 2001; Gopnik et al., 2004; Waldmann, 1996, 2000; Waldmann & Holyoak, 1992). In fact, it may not even explain animal learning. The R-W account predicts neither learned irrelevancy (the fact that an animal first ...
... Cheng, 1997; Glymour, 2001; Gopnik et al., 2004; Waldmann, 1996, 2000; Waldmann & Holyoak, 1992). In fact, it may not even explain animal learning. The R-W account predicts neither learned irrelevancy (the fact that an animal first ...
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... 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 causal power (an unobservable entity) and ...
... 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 causal power (an unobservable entity) and ...
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... Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405. Dickinson, A., Shanks, D. R., & Evendon, J. (1984). Judgment of act-outcome contingency: The role of selective attribution ...
... Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405. Dickinson, A., Shanks, D. R., & Evendon, J. (1984). Judgment of act-outcome contingency: The role of selective attribution ...
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... Cheng, P. W. (2004). Assessing interactive causal influence. Psychological Review, 111,455–485. Oakes, L. M., & Cohen, L. B. (1990). Infant perception of a causal event. Cognitive Development, 5, 193–207. Palmer, S. (1999). Vision ...
... Cheng, P. W. (2004). Assessing interactive causal influence. Psychological Review, 111,455–485. Oakes, L. M., & Cohen, L. B. (1990). Infant perception of a causal event. Cognitive Development, 5, 193–207. Palmer, S. (1999). Vision ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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