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 3
... examples of cases in which everyone agrees that X causes Y and then try to find some generalization that will ... example, who, although admittedly handicapped by an American secondary school education, are among the brightest and ...
... examples of cases in which everyone agrees that X causes Y and then try to find some generalization that will ... example, who, although admittedly handicapped by an American secondary school education, are among the brightest and ...
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... example , trying to predict one set of symptoms from another set . Trying to predict all the combinations of conditional probabilities rapidly becomes an exponentially complicated problem . Computer scientists were trying to find a ...
... example , trying to predict one set of symptoms from another set . Trying to predict all the combinations of conditional probabilities rapidly becomes an exponentially complicated problem . Computer scientists were trying to find a ...
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... example, I want to know what I can do to prevent my insomnia. Should I sit in my room alone but continue to drink when I want to or go to parties just the same but stick to Perrier? I can calcu- late the effects of such interventions on ...
... example, I want to know what I can do to prevent my insomnia. Should I sit in my room alone but continue to drink when I want to or go to parties just the same but stick to Perrier? I can calcu- late the effects of such interventions on ...
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... example , when B in the example above is manipulated by changing the com- mon cause A of B and S. I also assume in what follows that the effect of an intervention on X is that X comes entirely under the control of the intervention ...
... example , when B in the example above is manipulated by changing the com- mon cause A of B and S. I also assume in what follows that the effect of an intervention on X is that X comes entirely under the control of the intervention ...
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... example in which the common cause A of B and S is manipulated. Nonetheless, I think that it is plausible (see the Interventions and Voluntary Actions section) that, as a matter of contingent, empirical fact, many voluntary human actions ...
... example in which the common cause A of B and S is manipulated. Nonetheless, I think that it is plausible (see the Interventions and Voluntary Actions section) that, as a matter of contingent, empirical fact, many voluntary human actions ...
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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 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 Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Griffiths Hagmayer human independent infants intervention interventionist intuitive theories Journal of Experimental Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants 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