Causal Learning: Psychology, Philosophy, and ComputationUnderstanding 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
Contents PART I: Contributors ix Introduction 1 Alison Gopnik and Laura Schulz CAUSATION AND INTERVENTION 1 Interventionist Theories of Causation in Psychological Perspective 19 Jim Woodward 2 Infants' Causal Learning: Intervention, ...
Contents PART I: Contributors ix Introduction 1 Alison Gopnik and Laura Schulz CAUSATION AND INTERVENTION 1 Interventionist Theories of Causation in Psychological Perspective 19 Jim Woodward 2 Infants' Causal Learning: Intervention, ...
Page x
... 02139 Henry Wellman Department of Psychology Center for Human Growth and Development University of Michigan Ann Arbor, MI 48103 Jim Woodward Division of the Humanities and Social Sciences California Institute of Technology Pasadena, ...
... 02139 Henry Wellman Department of Psychology Center for Human Growth and Development University of Michigan Ann Arbor, MI 48103 Jim Woodward Division of the Humanities and Social Sciences California Institute of Technology Pasadena, ...
Page 5
Indeed, philosophers have argued that this is just what it means for two variables to be causally related (J. Woodward, 2003). Predictions about probabilities may be quite different from predictions about interventions.
Indeed, philosophers have argued that this is just what it means for two variables to be causally related (J. Woodward, 2003). Predictions about probabilities may be quite different from predictions about interventions.
Page 9
Specifically, infants seem to interpret human, but not mechanical, action as goal directed and self-initiated (Meltzoff, 1995; A. L. Woodward, 1998; A. L. Woodward, Phillips, & Spelke, 1993). Thus, for instance, babies expect physical ...
Specifically, infants seem to interpret human, but not mechanical, action as goal directed and self-initiated (Meltzoff, 1995; A. L. Woodward, 1998; A. L. Woodward, Phillips, & Spelke, 1993). Thus, for instance, babies expect physical ...
Page 13
Jim Woodward will tell you all about it on Saturday, and Chris Hitchcock will show you how it helps explain even those cases of quadruple countervailing prevention you find so amusing. And, John Campbell will tell you how it applies to ...
Jim Woodward will tell you all about it on Saturday, and Chris Hitchcock will show you how it helps explain even those cases of quadruple countervailing prevention you find so amusing. And, John Campbell will tell you how it applies to ...
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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 |
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
Bibliographic information
| Title | Causal Learning: Psychology, Philosophy, and Computation Oxford Series in Cognitive Development |
| Editors | Alison Gopnik, Laura Schulz |
| Publisher | Oxford University Press, 2007 |
| ISBN | 019803928X, 9780198039280 |
| Length | 384 pages |
| Subjects | › Psychology / Cognitive Psychology & Cognition |
| Export Citation | BiBTeX EndNote RefMan |