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 3
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 these inferences are possible. Although classical logic could provide a ...
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 these inferences are possible. Although classical logic could provide a ...
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was no way of explaining how the evidence itself could generate a hypothesis. Causal Bayes nets provide a kind of logic of inductive inference and discovery. They do so, at least, for one type of inference that is particularly important ...
was no way of explaining how the evidence itself could generate a hypothesis. Causal Bayes nets provide a kind of logic of inductive inference and discovery. They do so, at least, for one type of inference that is particularly important ...
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We can also use this fact to learn causal structure from the evidence of interventions and probabilities. Let us go back to the wine-insomnia example. You could distinguish between these graphs by either intervention or observation.
We can also use this fact to learn causal structure from the evidence of interventions and probabilities. Let us go back to the wine-insomnia example. You could distinguish between these graphs by either intervention or observation.
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Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence accurately from causal structure and to learn causal structure accurately from patterns of evidence. They constitute a kind of ...
Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence accurately from causal structure and to learn causal structure accurately from patterns of evidence. They constitute a kind of ...
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And, they seem to learn those causal structures from patterns of evidence. Plus, even the very smallest sprogs can combine information from observation and intervention. Little babies who learn a new skill—like reaching for ...
And, they seem to learn those causal structures from patterns of evidence. Plus, even the very smallest sprogs can combine information from observation and intervention. Little babies who learn a new skill—like reaching for ...
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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 |