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
... Glymour PART III : CAUSATION , THEORIES , AND MECHANISMS Introduction to Part III : Causation , Theories , and Mechanisms 243 Alison Gopnik and Laura Schulz 15 Why Represent Causal Relations ? 245 Michael Strevens 16 Causal Reasoning as ...
... Glymour PART III : CAUSATION , THEORIES , AND MECHANISMS Introduction to Part III : Causation , Theories , and Mechanisms 243 Alison Gopnik and Laura Schulz 15 Why Represent Causal Relations ? 245 Michael Strevens 16 Causal Reasoning as ...
Page ix
... Glymour Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213 Alison Gopnik Department of Psychology University of California at Berkeley Berkeley, California 94720 Tom Griffiths Department of Psychology University of ...
... Glymour Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213 Alison Gopnik Department of Psychology University of California at Berkeley Berkeley, California 94720 Tom Griffiths Department of Psychology University of ...
Page 2
... Glymour (Glymour 1992) put it very well, I think, in his critique of cognitive theories of science, appropriately called “Invasion of the Mind Snatchers”: The idea that theories are something you would find in somebody's head, rather ...
... Glymour (Glymour 1992) put it very well, I think, in his critique of cognitive theories of science, appropriately called “Invasion of the Mind Snatchers”: The idea that theories are something you would find in somebody's head, rather ...
Page 3
... (Glymour, 2001; Pearl, 1988, 2000; Spirtes, Glymour, & Scheines, 1993). Scientists seem to infer theories about the causal structure of the world from patterns of evidence. But, philosophers of science found it difficult to explain how ...
... (Glymour, 2001; Pearl, 1988, 2000; Spirtes, Glymour, & Scheines, 1993). Scientists seem to infer theories about the causal structure of the world from patterns of evidence. But, philosophers of science found it difficult to explain how ...
Page 7
... ( Glymour & Cooper , 1999 ; Spirtes et al . , 1993 ) . Computationally tractable learning algorithms have been designed to accomplish this task and have been extensively applied in a range of disciplines ( e.g. , Ramsey , Roush , Gazis ...
... ( Glymour & Cooper , 1999 ; Spirtes et al . , 1993 ) . Computationally tractable learning algorithms have been designed to accomplish this task and have been extensively applied in a range of disciplines ( e.g. , Ramsey , Roush , Gazis ...
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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