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. |
From inside the book
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Page 1
... we could get from that kind of data to those kinds of representations. The philosophers and computationalists keep telling us that the kind of learning we developmentalists see every day is nothing but an illusion! The 1 Introduction.
... we could get from that kind of data to those kinds of representations. The philosophers and computationalists keep telling us that the kind of learning we developmentalists see every day is nothing but an illusion! The 1 Introduction.
Page 2
... philosophers and computationalists seem to be doing, on either side, is to tell us empirical developmental psychologists not to believe our eyes. Actually, I think Gopnik puts it quite well in her book about theory formation (Gopnik ...
... philosophers and computationalists seem to be doing, on either side, is to tell us empirical developmental psychologists not to believe our eyes. Actually, I think Gopnik puts it quite well in her book about theory formation (Gopnik ...
Page 3
... philosophers give examples of cases in which everyone agrees that X causes Y and then try to find some generalization that will capture those examples. Then, other philosophers find examples that fit the definitions but don't seem to be ...
... philosophers give examples of cases in which everyone agrees that X causes Y and then try to find some generalization that will capture those examples. Then, other philosophers find examples that fit the definitions but don't seem to be ...
Page 5
... philosopher of science Hans Reichenbach (1971) long ago pointed out these consistent relations between conditional ... philosophers have argued that this is just what it means for two variables to be causally related (J. Woodward, 2003) ...
... philosopher of science Hans Reichenbach (1971) long ago pointed out these consistent relations between conditional ... philosophers have argued that this is just what it means for two variables to be causally related (J. Woodward, 2003) ...
Page 13
... philosophers, is a relic of a bygone age, surviving, like the monarchy, only because it is erroneously supposed to do no harm.” But, you see recently, and in tandem with all the new maths I told you about in that attachment, there's ...
... philosophers, is a relic of a bygone age, surviving, like the monarchy, only because it is erroneously supposed to do no harm.” But, you see recently, and in tandem with all the new maths I told you about in that attachment, there's ...
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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