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
... unobserved variables that are common causes of the observed variables (Richardson & Spirtes, 2003; Silva, Scheines, Glymour, & Spirtes, 2003). Causal Bayes net representations and learning algorithms allow learners to predict patterns ...
... unobserved variables that are common causes of the observed variables (Richardson & Spirtes, 2003; Silva, Scheines, Glymour, & Spirtes, 2003). Causal Bayes net representations and learning algorithms allow learners to predict patterns ...
Page 8
... reason about unobserved causes, learn complex causal structure. . . . Laura Schulz, Tamar Kushnir, and that Gopnik woman whose name you like so much will also show you all that on Saturday. When it comes to grown-ups, York Hagmayer ...
... reason about unobserved causes, learn complex causal structure. . . . Laura Schulz, Tamar Kushnir, and that Gopnik woman whose name you like so much will also show you all that on Saturday. When it comes to grown-ups, York Hagmayer ...
Page 12
... cause (see Gopnik et al., 2004). Similarly, the R-W equation assumes that all the variables have already been categorized as causes or effects and then calculates the associative strength between each cause ... unobserved common causes of the ...
... cause (see Gopnik et al., 2004). Similarly, the R-W equation assumes that all the variables have already been categorized as causes or effects and then calculates the associative strength between each cause ... unobserved common causes of the ...
Page 13
... causation have been similarly divided. Some accounts, like those of Dowe (2000) or ... cause to effect. Since Hume, the alternative account, usually phrased in ... unobserved variables from evidence. So, if the two actually were conjoined ...
... causation have been similarly divided. Some accounts, like those of Dowe (2000) or ... cause to effect. Since Hume, the alternative account, usually phrased in ... unobserved variables from evidence. So, if the two actually were conjoined ...
Page 15
... Cause and correlation in biology. Oxford, England: Oxford University Press. Silva, R., Scheines, R., Glymour, C., & Spirtes, P. (2003). Learning measurement models for unobserved variables. Proceedings of the 18th Conference on ...
... Cause and correlation in biology. Oxford, England: Oxford University Press. Silva, R., Scheines, R., Glymour, C., & Spirtes, P. (2003). Learning measurement models for unobserved variables. Proceedings of the 18th Conference on ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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