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 4
Causal Bayes nets provide a kind of logic of inductive inference and discovery. ... a certain power to bring about an effect and that this power leads to a certain likelihood of the effect given the cause, then we can further constrain ...
Causal Bayes nets provide a kind of logic of inductive inference and discovery. ... a certain power to bring about an effect and that this power leads to a certain likelihood of the effect given the cause, then we can further constrain ...
Page 7
Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of conditional probability and ... Still, the merest “sprog,” as you would say, has the computational power to turn that input back into a ...
Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of conditional probability and ... Still, the merest “sprog,” as you would say, has the computational power to turn that input back into a ...
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Covariation Accounts However, the generative transmission view of causation in particular and domain-specific knowledge in ... influential: associative learning or connectionist accounts and Patricia Cheng's causal power theory (1997).
Covariation Accounts However, the generative transmission view of causation in particular and domain-specific knowledge in ... influential: associative learning or connectionist accounts and Patricia Cheng's causal power theory (1997).
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Cheng proposes that people innately treat covariation as an index of causal power (an unobservable entity) and suggests that people reason about causes with respect to particular focal sets, a contextually determined set of events over ...
Cheng proposes that people innately treat covariation as an index of causal power (an unobservable entity) and suggests that people reason about causes with respect to particular focal sets, a contextually determined set of events over ...
Page 13
Much like your Shultz they argue that causation involves the spatiotemporal transmission of some sort of “mark” or “impetus” from cause to effect. Since Hume, the alternative ... From covariation to causation: A causal power theory.
Much like your Shultz they argue that causation involves the spatiotemporal transmission of some sort of “mark” or “impetus” from cause to effect. Since Hume, the alternative ... From covariation to causation: A causal power theory.
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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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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 |