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 Jessica A. Sommerville 4 ...
... 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 Jessica A. Sommerville 4 ...
Page 2
... think that it is. There is a long history in philosophy of trying to develop an analytic definition of causation through the method of examples and counterexamples; philosophers give examples of cases 2 CAUSAL LEARNING.
... think that it is. There is a long history in philosophy of trying to develop an analytic definition of causation through the method of examples and counterexamples; philosophers give examples of cases 2 CAUSAL LEARNING.
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... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evi- dence accurately from causal structure and to learn causal structure accurately from patterns of evidence . They constitute a kind of ...
... Causal Bayes net representations and learning algorithms allow learners to predict patterns of evi- dence accurately from causal structure and to learn causal structure accurately from patterns of evidence . They constitute a kind of ...
Page 8
... causal inference. Your computers may or may not be able to solve this causal learning problem, but it's certain that my sprogs can do it. In fact, they might be the most powerful causal learning devices in the universe. Thirty years of ...
... causal inference. Your computers may or may not be able to solve this causal learning problem, but it's certain that my sprogs can do it. In fact, they might be the most powerful causal learning devices in the universe. Thirty years of ...
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... Causal Reasoning Over the past several decades, however—and with the development of new methods for assessing the cognitive abilities of infants and young children— considerable research has suggested that Piaget underestimated the causal ...
... Causal Reasoning Over the past several decades, however—and with the development of new methods for assessing the cognitive abilities of infants and young children— considerable research has suggested that Piaget underestimated the causal ...
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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