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 20
... Interventionism Interventionist accounts take as their point of departure the idea that causes are potentially a means for manipulating their effects: If it is possible to manipulate a cause in the right way, then there would be an ...
... Interventionism Interventionist accounts take as their point of departure the idea that causes are potentially a means for manipulating their effects: If it is possible to manipulate a cause in the right way, then there would be an ...
Page 21
... interventionist theory yields a similar conclusion. This illustrates how, as claimed, interventions play roughly the same role as the similarity metric in Lewis's theory and how they lead, as in Lewis's theory, to non-backtracking ...
... interventionist theory yields a similar conclusion. This illustrates how, as claimed, interventions play roughly the same role as the similarity metric in Lewis's theory and how they lead, as in Lewis's theory, to non-backtracking ...
Page 22
... interventionist framework as follows: (DC) A necessary and sufficient condition for X to be a direct cause of Y with respect to some variable set V is that there be a possible intervention on X that will change Y (or the probability ...
... interventionist framework as follows: (DC) A necessary and sufficient condition for X to be a direct cause of Y with respect to some variable set V is that there be a possible intervention on X that will change Y (or the probability ...
Page 23
... interventionist framework. Such information about detailed manipulability or dependency relationships is often required for tasks involving fine-grained control such as tool use. Additional Features of Interventionism I said that ...
... interventionist framework. Such information about detailed manipulability or dependency relationships is often required for tasks involving fine-grained control such as tool use. Additional Features of Interventionism I said that ...
Page 24
... interventionist account will lead to different causal judgments about particular cases than causal process accounts. Consider cases of double prevention, in which A prevents the occurrence of B, which had it occurred, would have ...
... interventionist account will lead to different causal judgments about particular cases than causal process accounts. Consider cases of double prevention, in which A prevents the occurrence of B, which had it occurred, would have ...
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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 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