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 viii
... Explanations 261 Henry M. Wellman and David Liu 17 Dynamic Interpretations of Covariation Data 280 Woo - kyoung Ahn , Jessecae K. Marsh , and Christian C. Luhmann 18 Statistical Jokes and Social Effects : Intervention and Invariance in ...
... Explanations 261 Henry M. Wellman and David Liu 17 Dynamic Interpretations of Covariation Data 280 Woo - kyoung Ahn , Jessecae K. Marsh , and Christian C. Luhmann 18 Statistical Jokes and Social Effects : Intervention and Invariance in ...
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... explanations of the world around them. And, they seem to learn those causal structures from patterns of evidence ... explanations of nat- ural phenomena. Piaget found that children's early explanations of physical events were ...
... explanations of the world around them. And, they seem to learn those causal structures from patterns of evidence ... explanations of nat- ural phenomena. Piaget found that children's early explanations of physical events were ...
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... explanations (at least of famil- iar, everyday events) that respect domain boundaries (Hickling & Wellman, 2001). Finally, preschoolers' predictions, causal judgments, and counterfactual inferences are remarkably accurate across a wide ...
... explanations (at least of famil- iar, everyday events) that respect domain boundaries (Hickling & Wellman, 2001). Finally, preschoolers' predictions, causal judgments, and counterfactual inferences are remarkably accurate across a wide ...
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... explanations of how people judge the strength of different causal variables . These theo- ries do not explain how people make judgments about causal structure . In addition , neither the R - W nor the power PC theory provides a unified ...
... explanations of how people judge the strength of different causal variables . These theo- ries do not explain how people make judgments about causal structure . In addition , neither the R - W nor the power PC theory provides a unified ...
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... explanations and theories : Evidence from everyday conversation . Developmental Psychology , 37 , 668–684 . Kalish , C. ( 1996 ) . Causes and symptoms in preschoolers ' conceptions of illness . Child Development , 67 , 1647–1670 . Kant ...
... explanations and theories : Evidence from everyday conversation . Developmental Psychology , 37 , 668–684 . Kalish , C. ( 1996 ) . Causes and symptoms in preschoolers ' conceptions of illness . Child Development , 67 , 1647–1670 . Kant ...
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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