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 1
... representations of the world. And those representations are true—they really do get us to a better picture of the world. But, the data that actually reach us from the world are incomplete, fragmented, probabilistic, and concrete. So ...
... representations of the world. And those representations are true—they really do get us to a better picture of the world. But, the data that actually reach us from the world are incomplete, fragmented, probabilistic, and concrete. So ...
Page 2
... representations are accurate, it is because of a long phylogenetic evolutionary history, not a brief ontogenetic inferential one. And, there is no real learning involved in development but only triggering or enrichment. The Aristotelian ...
... representations are accurate, it is because of a long phylogenetic evolutionary history, not a brief ontogenetic inferential one. And, there is no real learning involved in development but only triggering or enrichment. The Aristotelian ...
Page 5
... representations of causal relations among variables , rather than simply thinking of them as a convenient way to represent the probabil- ities of variables ? The earlier Bayes net iterations were confined to techniques for predicting ...
... representations of causal relations among variables , rather than simply thinking of them as a convenient way to represent the probabil- ities of variables ? The earlier Bayes net iterations were confined to techniques for predicting ...
Page 7
... representation of a table or a lamp without even thinking about it . And ( ignoring the occasional illusion ) , those representations are accurate : They capture the truth about the spatial world . In vision science , we have “ ideal ...
... representation of a table or a lamp without even thinking about it . And ( ignoring the occasional illusion ) , those representations are accurate : They capture the truth about the spatial world . In vision science , we have “ ideal ...
Page 8
... representations of the causal structure of the world. Those representations allow children to make predictions, perform interventions, and even generate counterfactuals. As soon as they can talk, they even offer explanations of the ...
... representations of the causal structure of the world. Those representations allow children to make predictions, perform interventions, and even generate counterfactuals. As soon as they can talk, they even offer explanations of the ...
Other editions - View all
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