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 vii
... Causal Learning: Intervention, Observation, Imitation 37 Andrew N. Meltzoff 3 Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations 48 Jessica A. Sommerville 4 An ...
... Causal Learning: Intervention, Observation, Imitation 37 Andrew N. Meltzoff 3 Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations 48 Jessica A. Sommerville 4 An ...
Page viii
... Causal Reasoning 119 Richard Scheines, Matt Easterday, and David Danks 9 Interactions Between Causal and Statistical ... Relations? 245 Michael Strevens Causal Reasoning as Informed by the Early Development of Explanations 261 Henry M ...
... Causal Reasoning 119 Richard Scheines, Matt Easterday, and David Danks 9 Interactions Between Causal and Statistical ... Relations? 245 Michael Strevens Causal Reasoning as Informed by the Early Development of Explanations 261 Henry M ...
Page 3
... 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 relations, allow one to generate the appropriate ...
... 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 relations, allow one to generate the appropriate ...
Page 4
... causal relations can have many forms; they can be deterministic or probabilistic, generative or inhibitory, linear or nonlinear. The exact specification of the nature of these relations is called the parameterization of the graph. In ...
... causal relations can have many forms; they can be deterministic or probabilistic, generative or inhibitory, linear or nonlinear. The exact specification of the nature of these relations is called the parameterization of the graph. In ...
Page 5
... relations between conditional independence and causal structure and talked about them in terms of “screening off.” When there is a chain going from partying to wine to insomnia, the wine screens off insomnia from the influence of ...
... relations between conditional independence and causal structure and talked about them in terms of “screening off.” When there is a chain going from partying to wine to insomnia, the wine screens off insomnia from the influence of ...
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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