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
... cause , then we can further constrain the patterns of conditional probability among causes and effects . This is a common ... common for academics who attend many happens in the latter case that is diagnostic for whether. 4 CAUSAL LEARNING.
... cause , then we can further constrain the patterns of conditional probability among causes and effects . This is a common ... common for academics who attend many happens in the latter case that is diagnostic for whether. 4 CAUSAL LEARNING.
Page 5
... common cause structure I ← P → W. Maybe parties lead me to drink , and wine keeps me up ; maybe parties both keep me up and lead me to drink . The covariation among the vari- ables by itself is consistent with both these structures ...
... common cause structure I ← P → W. Maybe parties lead me to drink , and wine keeps me up ; maybe parties both keep me up and lead me to drink . The covariation among the vari- ables by itself is consistent with both these structures ...
Page 21
... common cause of the reading B of a barometer and a variable S corresponding to the occurrence / nonoccur- rence of a storm but in which B does not cause S or vice versa . If we manipulate the value of B by manipulating the value of A ...
... common cause of the reading B of a barometer and a variable S corresponding to the occurrence / nonoccur- rence of a storm but in which B does not cause S or vice versa . If we manipulate the value of B by manipulating the value of A ...
Page 22
... 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 as well as many ...
... 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 as well as many ...
Page 24
... common in biolog- ical contexts. For example, the presence A of lactose in the environment of Escherichia coli ... cause E if the dependence conditions in TC are not satisfied. Similarly, for the information that something has been ...
... common in biolog- ical contexts. For example, the presence A of lactose in the environment of Escherichia coli ... cause E if the dependence conditions in TC are not satisfied. Similarly, for the information that something has been ...
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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