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 4
... experiments ) , or usually , by combining the two types of evidence . Causal Bayes nets formal- ize these kinds of inferences . In causal Bayes nets , causal hypotheses are rep- resented by directed acyclic graphs like that of Figure I ...
... experiments ) , or usually , by combining the two types of evidence . Causal Bayes nets formal- ize these kinds of inferences . In causal Bayes nets , causal hypotheses are rep- resented by directed acyclic graphs like that of Figure I ...
Page 9
... experiments, Shultz demonstrated that, in their causal judgments, preschoolers privilege evidence for spatially continu- ous processes compatible with the transmission of energy over evidence for covariation. Preschoolers inferred, for ...
... experiments, Shultz demonstrated that, in their causal judgments, preschoolers privilege evidence for spatially continu- ous processes compatible with the transmission of energy over evidence for covariation. Preschoolers inferred, for ...
Page 20
... experiments. Although it is possible to provide a treatment of token causation within a manipulability framework,1 I focus on the general notion of one type of factor being causally relevant (either positively or negatively) to another ...
... experiments. Although it is possible to provide a treatment of token causation within a manipulability framework,1 I focus on the general notion of one type of factor being causally relevant (either positively or negatively) to another ...
Page 21
... experiment in which B is manipulated in this way is a badly designed experiment for the purposes of determining whether B causes S. We need to formulate conditions that restrict the allowable ways of changing B so as to rule out ...
... experiment in which B is manipulated in this way is a badly designed experiment for the purposes of determining whether B causes S. We need to formulate conditions that restrict the allowable ways of changing B so as to rule out ...
Page 28
... experiments that show that infants who actively intervene, for example, to obtain a toy by pulling a cloth on which it rests learn to distinguish relevant causal relationships between the cloth and toy (presence of spatiotemporal ...
... experiments that show that infants who actively intervene, for example, to obtain a toy by pulling a cloth on which it rests learn to distinguish relevant causal relationships between the cloth and toy (presence of spatiotemporal ...
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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 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 Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Griffiths Hagmayer human independent infants intervention interventionist intuitive theories Journal of Experimental Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants 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