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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... framework that captures important regularities in causal facts, just as the mathematical structure of geometry captures important spatial regularities. Causal graphical models capture just the right kind of asymmetries in causal ...
... framework that captures important regularities in causal facts, just as the mathematical structure of geometry captures important spatial regularities. Causal graphical models capture just the right kind of asymmetries in causal ...
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... framework,1 I focus on the general notion of one type of factor being causally relevant (either positively or negatively) to another. There are two more specific causal concepts that may be seen as precifications of this more general ...
... framework,1 I focus on the general notion of one type of factor being causally relevant (either positively or negatively) to another. There are two more specific causal concepts that may be seen as precifications of this more general ...
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... framework as follows: (DC) A necessary and sufficient condition for X to be a direct cause of Y with respect to some variable set V is that there be a possible intervention on X that will change Y (or the probability distribution of Y) ...
... framework as follows: (DC) A necessary and sufficient condition for X to be a direct cause of Y with respect to some variable set V is that there be a possible intervention on X that will change Y (or the probability distribution of Y) ...
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... framework. Such information about detailed manipulability or dependency relationships is often required for tasks involving fine-grained control such as tool use. Additional Features of Interventionism I said that interventionist ...
... framework. Such information about detailed manipulability or dependency relationships is often required for tasks involving fine-grained control such as tool use. Additional Features of Interventionism I said that interventionist ...
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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