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
... 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 exposed to a cue ...
... 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 exposed to a cue ...
Page 12
... Glymour, 2001); and (c) that candidate causes are noninteractive (although Novick and Cheng, 2004, have since modified the account to explain inferences about interactive causes). The causal power of a candidate cause is not equivalent ...
... Glymour, 2001); and (c) that candidate causes are noninteractive (although Novick and Cheng, 2004, have since modified the account to explain inferences about interactive causes). The causal power of a candidate cause is not equivalent ...
Page 14
... Glymour, C. (2001). The mind's arrows: Bayes nets and causal graphical models in psychology. Cambridge, MA: MIT Press. Glymour, C., & Cooper, G. F. (1999). Computation, causation, and discovery. Cambridge, MA: MIT/AAAI Press. Gopnik, A ...
... Glymour, C. (2001). The mind's arrows: Bayes nets and causal graphical models in psychology. Cambridge, MA: MIT Press. Glymour, C., & Cooper, G. F. (1999). Computation, causation, and discovery. Cambridge, MA: MIT/AAAI Press. Gopnik, A ...
Page 15
... Glymour, C., & Spirtes, P. (2003). Learning measurement models for unobserved variables. Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence. AAAI Press. pp. 191–246. Sobel, D. M. (2004). Exploring the coherence ...
... Glymour, C., & Spirtes, P. (2003). Learning measurement models for unobserved variables. Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence. AAAI Press. pp. 191–246. Sobel, D. M. (2004). Exploring the coherence ...
Page 21
... Glymour, and Scheines (2000); Pearl (2000); and Woodward (2003). Because the difference between these formulations will not be important for what follows, I focus on the core idea. This is that an intervention I on X with respect to Y ...
... Glymour, and Scheines (2000); Pearl (2000); and Woodward (2003). Because the difference between these formulations will not be important for what follows, I focus on the core idea. This is that an intervention I on X with respect to Y ...
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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