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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... causal chain that goes from parties to wine to insomnia, then I⊥P | W; the probability of insomnia occurring is independent (in probability) of the probability of party going occurring conditional on the occurrence of wine drinking. If ...
... causal chain that goes from parties to wine to insomnia, then I⊥P | W; the probability of insomnia occurring is independent (in probability) of the probability of party going occurring conditional on the occurrence of wine drinking. If ...
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... causal inference. Your computers may or may not be able to solve this causal ... chain of causal events and reason accurately about intervening causal mechanisms. Nativist and Modular Views of Causal Reasoning Over the past 8 CAUSAL LEARNING.
... causal inference. Your computers may or may not be able to solve this causal ... chain of causal events and reason accurately about intervening causal mechanisms. Nativist and Modular Views of Causal Reasoning Over the past 8 CAUSAL LEARNING.
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... causal analysis of an outcome” (p. 136). This conclusion may seem surprising ... causal thinking, by both children and adults, is in planning and in anticipating ... chain structure in which X causes Y which causes Z, people choose to ...
... causal analysis of an outcome” (p. 136). This conclusion may seem surprising ... causal thinking, by both children and adults, is in planning and in anticipating ... chain structure in which X causes Y which causes Z, people choose to ...
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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