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 viii
... Cues to Causal Structure 154 David A. Lagnado , Michael R. Waldmann , York Hagmayer , and Steven A. Sloman 11 Theory Unification and Graphical Models in Human Categorization 173 David Danks 12 Essentialism as a Generative Theory of ...
... Cues to Causal Structure 154 David A. Lagnado , Michael R. Waldmann , York Hagmayer , and Steven A. Sloman 11 Theory Unification and Graphical Models in Human Categorization 173 David Danks 12 Essentialism as a Generative Theory of ...
Page 11
... cues have to be predictive: The probability of the effect given the cue must be greater than the probability of the effect in the absence of the cue. The Rescorla-Wagner theory (R-W theory; 1972) specified that learning occurred on a ...
... cues have to be predictive: The probability of the effect given the cue must be greater than the probability of the effect in the absence of the cue. The Rescorla-Wagner theory (R-W theory; 1972) specified that learning occurred on a ...
Page 12
... cues , people could use data to distinguish causes and effects ( i.e. , to infer whether A causes B or B causes A ) . Put another way , both the R - W account and the Cheng account are explanations of how people judge the strength of ...
... cues , people could use data to distinguish causes and effects ( i.e. , to infer whether A causes B or B causes A ) . Put another way , both the R - W account and the Cheng account are explanations of how people judge the strength of ...
Page 15
... cue competition . Journal of Experi- mental Psychology : General , 121 , 222–236 . Wasserman , E. A. , & Berglan , L. R. ( 1998 ) . Backward blocking and recovery from overshadowing in human causal judgment : The role of within com ...
... cue competition . Journal of Experi- mental Psychology : General , 121 , 222–236 . Wasserman , E. A. , & Berglan , L. R. ( 1998 ) . Backward blocking and recovery from overshadowing in human causal judgment : The role of within com ...
Page 24
... cue stick strikes a cue ball, which in turn strikes the eight ball, causing it to drop into a pocket. The stick has been coated with blue chalk dust, some of which is transmitted to the cue ball and then to the eight ball as a result of ...
... cue stick strikes a cue ball, which in turn strikes the eight ball, causing it to drop into a pocket. The stick has been coated with blue chalk dust, some of which is transmitted to the cue ball and then to the eight ball as a result of ...
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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