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 4
... effect and that this power leads to a certain likelihood of the effect given the cause, then we can further constrain the patterns of conditional probability among causes and effects. This is a common assumption in studies of human ...
... effect and that this power leads to a certain likelihood of the effect given the cause, then we can further constrain the patterns of conditional probability among causes and effects. This is a common assumption in studies of human ...
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... effects on your insomnia. If Graph 1 is correct, then you should observe that you are more likely to have insomnia when you ... effect on their insomnia. Only intervening on partying will do that. The Bayes net formalism captures these ...
... effects on your insomnia. If Graph 1 is correct, then you should observe that you are more likely to have insomnia when you ... effect on their insomnia. Only intervening on partying will do that. The Bayes net formalism captures these ...
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... effect on your sleep. If drinking affects your sleep when partying is held constant, but partying has no effect on your sleep when drinking is held constant, then you could conclude that Graph 1 is correct. Such reasoning underlies the ...
... effect on your sleep. If drinking affects your sleep when partying is held constant, but partying has no effect on your sleep when drinking is held constant, then you could conclude that Graph 1 is correct. Such reasoning underlies the ...
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... effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms of generative transmission” of force and energy (1982, p. 46). In a series of experiments, Shultz demonstrated that, in their causal ...
... effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms of generative transmission” of force and energy (1982, p. 46). In a series of experiments, Shultz demonstrated that, in their causal ...
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... effects by transfer of causal impetus” is “central to the psychological definition of cause-effect relations” (1982). Consistent with this view, psychologists have shown that even adults prefer information about plausible, domain ...
... effects by transfer of causal impetus” is “central to the psychological definition of cause-effect relations” (1982). Consistent with this view, psychologists have shown that even adults prefer information about plausible, domain ...
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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 |