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 structure from patterns of conditional probability and intervention (Glymour & Cooper, 1999; Spirtes et al ... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence accurately from ...
... causal structure from patterns of conditional probability and intervention (Glymour & Cooper, 1999; Spirtes et al ... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence accurately from ...
Page 8
... causal learning devices in the universe. Thirty years of work on the “theory theory” shows that children have abstract, coherent representations of the causal structure of the world. Those representations allow children to make ...
... causal learning devices in the universe. Thirty years of work on the “theory theory” shows that children have abstract, coherent representations of the causal structure of the world. Those representations allow children to make ...
Page 12
... 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 ... structure. In addition, neither the R-W nor the power PC theory provides a unified ...
... 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 ... structure. In addition, neither the R-W nor the power PC theory provides a unified ...
Page 13
... causal Bayes net account seems, well, destined for. My learning algorithms, like your sprogs, can infer causal structure rather than just strength; they can appropriately combine information from interventions and observations and ...
... causal Bayes net account seems, well, destined for. My learning algorithms, like your sprogs, can infer causal structure rather than just strength; they can appropriately combine information from interventions and observations and ...
Page 22
... causal process. So, regardless of what one makes of the circularity of TC, it is certainly not vacuous or empty. Let me now turn to the notion of direct causation. Consider a causal structure in which taking birth control pills B ...
... causal process. So, regardless of what one makes of the circularity of TC, it is certainly not vacuous or empty. Let me now turn to the notion of direct causation. Consider a causal structure in which taking birth control pills B ...
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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