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
... Imitation 37 Andrew N. Meltzoff 3 Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations 48 Jessica A. Sommerville 4 An Interventionist Approach to Causation in ...
... Imitation 37 Andrew N. Meltzoff 3 Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations 48 Jessica A. Sommerville 4 An Interventionist Approach to Causation in ...
Page viii
10 11 13 8 Teaching the Normative Theory of Causal Reasoning 119 Richard Scheines, Matt Easterday, and David Danks 9 ... to Part III: Causation, Theories, and Mechanisms 243 Alison Gopnik and Laura Schulz Why Represent Causal Relations?
10 11 13 8 Teaching the Normative Theory of Causal Reasoning 119 Richard Scheines, Matt Easterday, and David Danks 9 ... to Part III: Causation, Theories, and Mechanisms 243 Alison Gopnik and Laura Schulz Why Represent Causal Relations?
Page 3
Then, other philosophers find examples that fit the definitions but don't seem to be causal or vice versa. ... Causal graphical models capture just the right kind of asymmetries in causal relations, allow one to generate the appropriate ...
Then, other philosophers find examples that fit the definitions but don't seem to be causal or vice versa. ... Causal graphical models capture just the right kind of asymmetries in causal relations, allow one to generate the appropriate ...
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The graphs consist of variables, representing types of events or states of the world and directed edges (arrows) representing the direct causal relations between those variables. The variables can be discrete (like school grade) or ...
The graphs consist of variables, representing types of events or states of the world and directed edges (arrows) representing the direct causal relations between those variables. The variables can be discrete (like school grade) or ...
Page 5
Partying P and insomnia I covary and so do wine W and insomnia I. There are at least two possibilities about the relations among these variables, which I can represent by two simple causal graphs: Graph 1 is a chain P → W → I; ...
Partying P and insomnia I covary and so do wine W and insomnia I. There are at least two possibilities about the relations among these variables, which I can represent by two simple causal graphs: Graph 1 is a chain P → W → I; ...
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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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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 |