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
... causal judgments as if their learning, behavior, and judgments are guided by principles like TC. The connection between interventions and human (and animal) manipulation is thus important to the empirical psychology of causal judgment ...
... causal judgments as if their learning, behavior, and judgments are guided by principles like TC. The connection between interventions and human (and animal) manipulation is thus important to the empirical psychology of causal judgment ...
Page 23
... causal judgments and inferences. I suggest that it does: It is involved in or connected to our ability to separate out means and ends in causal reasoning. It is also centrally involved in the whole idea of an intervention, which turns ...
... causal judgments and inferences. I suggest that it does: It is involved in or connected to our ability to separate out means and ends in causal reasoning. It is also centrally involved in the whole idea of an intervention, which turns ...
Page 25
... causal learning and understanding. However, rather than trying to explicate the notion of a causal mechanism in terms of notions like force, energy, or generative transmission, interventionists will instead appeal to interventionist ...
... causal learning and understanding. However, rather than trying to explicate the notion of a causal mechanism in terms of notions like force, energy, or generative transmission, interventionists will instead appeal to interventionist ...
Page 26
... causal learning and form many true or correct causal representations of the world. There must be some unified story about this that is both an accurate description of what they do and that enables us to understand how what they do leads ...
... causal learning and form many true or correct causal representations of the world. There must be some unified story about this that is both an accurate description of what they do and that enables us to understand how what they do leads ...
Page 28
... reasoning. But, whatever one's assessment of Lewis's theory, it is important to bear in mind that one of the main everyday uses of counterfactual and causal thinking, by both children and adults, is in planning and in anticipating what ...
... reasoning. But, whatever one's assessment of Lewis's theory, it is important to bear in mind that one of the main everyday uses of counterfactual and causal thinking, by both children and adults, is in planning and in anticipating what ...
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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