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 4
... tion of the nature of these relations is called the para- meterization of the graph . In most applications of the formalism , we assume that the graphs are acyclic — an arrow cannot feed back on itself . However , there are some ...
... tion of the nature of these relations is called the para- meterization of the graph . In most applications of the formalism , we assume that the graphs are acyclic — an arrow cannot feed back on itself . However , there are some ...
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... tion generalizes this screening - off principle to all acyclic causal graphs . Thus , if we know the structure of the graph and know the values of some of the variables in the graph , we can make consistent predictions about the ...
... tion generalizes this screening - off principle to all acyclic causal graphs . Thus , if we know the structure of the graph and know the values of some of the variables in the graph , we can make consistent predictions about the ...
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... tion on the variables in the graph , all conditional independencies are consequences of the Markov assumption applied to the graph . Given the faithfulness assumption , it is possible to infer complex causal structure from patterns of ...
... tion on the variables in the graph , all conditional independencies are consequences of the Markov assumption applied to the graph . Given the faithfulness assumption , it is possible to infer complex causal structure from patterns of ...
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... tion between events is determined by the covariation of cause and effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms of generative transmission” of force and energy (1982, p. 46). In a ...
... tion between events is determined by the covariation of cause and effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms of generative transmission” of force and energy (1982, p. 46). In a ...
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... tion: A multidisciplinary debate. Symposia of the Fyssen Foundation; Fyssen Symposium, 6th January 1993, Pavillon Henri IV, St-Germain-en-Laye, France (pp. 79–115). New York: Clarendon Press/Oxford University Press. Baker, A., Mercier ...
... tion: A multidisciplinary debate. Symposia of the Fyssen Foundation; Fyssen Symposium, 6th January 1993, Pavillon Henri IV, St-Germain-en-Laye, France (pp. 79–115). New York: Clarendon Press/Oxford University Press. Baker, A., Mercier ...
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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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