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 22
... counterfactual claims about what would happen if certain “possible” interventions “were” to be performed. I take it to ... counterfactuals and, as such, are not claims about how causal relationships are learned. However, if SC and NC are ...
... counterfactual claims about what would happen if certain “possible” interventions “were” to be performed. I take it to ... counterfactuals and, as such, are not claims about how causal relationships are learned. However, if SC and NC are ...
Page 23
... counterfactuals connecting X to Y. We may view this more detailed information, which may be captured by such devices as specific functional relationships linking X and Y, as the natural way of spelling out the detailed content of causal ...
... counterfactuals connecting X to Y. We may view this more detailed information, which may be captured by such devices as specific functional relationships linking X and Y, as the natural way of spelling out the detailed content of causal ...
Page 24
... counterfactuals between whether lactose is present and the synthesis (or lack of synthesis) of the enzyme that digests it— manipulating whether lactose is present changes whether the enzyme is synthesized—but no spatiotemporally ...
... counterfactuals between whether lactose is present and the synthesis (or lack of synthesis) of the enzyme that digests it— manipulating whether lactose is present changes whether the enzyme is synthesized—but no spatiotemporally ...
Page 25
... counterfactuals. Simplifying greatly, information about a mechanism connecting C to E will typically be information about a set of dependency relationships, specified by interventionist counterfactuals, connecting C and E to ...
... counterfactuals. Simplifying greatly, information about a mechanism connecting C to E will typically be information about a set of dependency relationships, specified by interventionist counterfactuals, connecting C and E to ...
Page 26
... counterfactuals, to motivate particular approaches. Claims of this sort are of course descriptive claims about the empirical psychology of causal inference and judgment and should be evaluated accordingly. In addition, although ...
... counterfactuals, to motivate particular approaches. Claims of this sort are of course descriptive claims about the empirical psychology of causal inference and judgment and should be evaluated accordingly. In addition, although ...
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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