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
... Woodward 2 Infants' 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 ...
... Woodward 2 Infants' 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 ...
Page x
... Woodward Division of the Humanities and Social Sciences California Institute of Technology Pasadena, CA 91125 Introduction Alison Gopnik & Laura Schulz From: mherskovits@psych.ucarcadia.arcadia.edu To: brook_russell@turing.carnegietech ...
... Woodward Division of the Humanities and Social Sciences California Institute of Technology Pasadena, CA 91125 Introduction Alison Gopnik & Laura Schulz From: mherskovits@psych.ucarcadia.arcadia.edu To: brook_russell@turing.carnegietech ...
Page 5
... Woodward, 2003). Predictions about probabilities may be quite different from predictions about interventions. For example, in a common cause structure like Graph 2, we will indeed be able to predict something about the value of insomnia ...
... Woodward, 2003). Predictions about probabilities may be quite different from predictions about interventions. For example, in a common cause structure like Graph 2, we will indeed be able to predict something about the value of insomnia ...
Page 9
... Woodward, 1998; A. L. Woodward, Phillips, & Spelke, 1993). Thus, for instance, babies expect physical objects to move through contact (Leslie & Keeble, 1987; Oakes & Cohen, 1990) but do not expect the same of human agents (A. L. ...
... Woodward, 1998; A. L. Woodward, Phillips, & Spelke, 1993). Thus, for instance, babies expect physical objects to move through contact (Leslie & Keeble, 1987; Oakes & Cohen, 1990) but do not expect the same of human agents (A. L. ...
Page 13
... 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 even ...
... 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 even ...
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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
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 |