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
... about this series of workshops on causal learning that my advisor and yours have cooked up for this year at the center in Stanford. My advisor has gone completely crazy over this causal Bayes nets stuff and is insisting that I go to this ...
... about this series of workshops on causal learning that my advisor and yours have cooked up for this year at the center in Stanford. My advisor has gone completely crazy over this causal Bayes nets stuff and is insisting that I go to this ...
Page 2
... about theory formation (Gopnik & Meltzoff, 1997) (she does tend to let her conclusions outstrip her data, but she sure has an ear for a ... the method of examples and counterexamples; philosophers give examples of cases 2 CAUSAL LEARNING.
... about theory formation (Gopnik & Meltzoff, 1997) (she does tend to let her conclusions outstrip her data, but she sure has an ear for a ... the method of examples and counterexamples; philosophers give examples of cases 2 CAUSAL LEARNING.
Page 3
... 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 causal or vice versa. I was ...
... 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 causal or vice versa. I was ...
Page 4
... the evidence itself could generate a hypothesis. Causal Bayes nets provide a ... about the parameterization of the graph, that is, about the particular ... that I notice that I often cannot. 4 CAUSAL LEARNING.
... the evidence itself could generate a hypothesis. Causal Bayes nets provide a ... about the parameterization of the graph, that is, about the particular ... that I notice that I often cannot. 4 CAUSAL LEARNING.
Page 5
... about the relations among these variables, which I can represent by two simple causal graphs: Graph 1 is a chain P → W → I; Graph 2 is a common ... the graph. The Intervention Assumption A variable I is an intervention on INTRODUCTION 5.
... about the relations among these variables, which I can represent by two simple causal graphs: Graph 1 is a chain P → W → I; Graph 2 is a common ... the graph. The Intervention Assumption A variable I is an intervention on INTRODUCTION 5.
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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