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 vii
... Causal Inference 67 Laura Schulz, Tamar Kushnir, and Alison Gopnik 6 Causal Reasoning Through Intervention 86 York Hagmayer, Steven Sloman, David Lagnado, and Michael R. Waldmann 7 On the Importance of Causal Taxonomy 101 Christopher ...
... Causal Inference 67 Laura Schulz, Tamar Kushnir, and Alison Gopnik 6 Causal Reasoning Through Intervention 86 York Hagmayer, Steven Sloman, David Lagnado, and Michael R. Waldmann 7 On the Importance of Causal Taxonomy 101 Christopher ...
Page viii
... Causal Reasoning 119 Richard Scheines, Matt Easterday, and David Danks 9 Interactions Between Causal and Statistical ... Inference 301 Joshua B. Tenenbaum, Thomas L. Griffiths, and Sourabh Niyogi 20 Two Proposals for Causal Grammars 323 ...
... Causal Reasoning 119 Richard Scheines, Matt Easterday, and David Danks 9 Interactions Between Causal and Statistical ... Inference 301 Joshua B. Tenenbaum, Thomas L. Griffiths, and Sourabh Niyogi 20 Two Proposals for Causal Grammars 323 ...
Page 2
... causal inference I can't imagine. He is also insisting that I attend these workshops. I can't say I caught all your references. Plato certainly, but Spelke? Gopnik? (And what ghastly names.) However, I completely agree with you about ...
... causal inference I can't imagine. He is also insisting that I attend these workshops. I can't say I caught all your references. Plato certainly, but Spelke? Gopnik? (And what ghastly names.) However, I completely agree with you about ...
Page 8
... causal inference. Your computers may or may not be able to solve this causal learning problem, but it's certain that my sprogs can do it. In fact, they might be the most powerful causal learning devices in the universe. Thirty years of ...
... causal inference. Your computers may or may not be able to solve this causal learning problem, but it's certain that my sprogs can do it. In fact, they might be the most powerful causal learning devices in the universe. Thirty years of ...
Page 11
... causal inference (1997). When an effect always occurs (i.e., whether the candidate cause is present or not), the R-W equation predicts that we should conclude that the candidate generative cause is ineffective. In contrast, human ...
... causal inference (1997). When an effect always occurs (i.e., whether the candidate cause is present or not), the R-W equation predicts that we should conclude that the candidate generative cause is ineffective. In contrast, human ...
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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