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 26
... action and outcome. Both rat behavior and human causal judgment are (independently of temporal relations) highly sensitive to the contingency p between action A and outcome O, that is, to P(O/A) P(O/A). Although there are important 26 ...
... action and outcome. Both rat behavior and human causal judgment are (independently of temporal relations) highly sensitive to the contingency p between action A and outcome O, that is, to P(O/A) P(O/A). Although there are important 26 ...
Page 27
... action” (2000, p. 192). These results suggest that both instrumental learning in rats and human judgments of causal strength (as well as actions based on this) behave as though they track the perceived degree of control or manipulative ...
... action” (2000, p. 192). These results suggest that both instrumental learning in rats and human judgments of causal strength (as well as actions based on this) behave as though they track the perceived degree of control or manipulative ...
Page 28
... action would be (without necessarily performing the actions in question). This is a perfectly ordinary, natural, practically useful activity and (relevantly, to our story) one that even small children appear to be much better at than ...
... action would be (without necessarily performing the actions in question). This is a perfectly ordinary, natural, practically useful activity and (relevantly, to our story) one that even small children appear to be much better at than ...
Page 29
... Actions I noted that in many situations people make more reliable causal inferences when they are able to intervene. From a design viewpoint, one thus might expect that subjects will have more confidence in causal inferences and ...
... Actions I noted that in many situations people make more reliable causal inferences when they are able to intervene. From a design viewpoint, one thus might expect that subjects will have more confidence in causal inferences and ...
Page 30
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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