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 2
... that the philosophers and computationalists seem to be doing, on either side, is to tell us empirical developmental psychologists not to believe our eyes. Actually, I think Gopnik puts it quite well in her book about theory formation ...
... that the philosophers and computationalists seem to be doing, on either side, is to tell us empirical developmental psychologists not to believe our eyes. Actually, I think Gopnik puts it quite well in her book about theory formation ...
Page 7
... that. The visual system assumes that the patterns at the retina were produced by three-dimensional objects in a particular way and then uses those assumptions to infer the objects from the retinal patterns. Your causal Bayes nets assume ...
... that. The visual system assumes that the patterns at the retina were produced by three-dimensional objects in a particular way and then uses those assumptions to infer the objects from the retinal patterns. Your causal Bayes nets assume ...
Page 11
... that the change in associative strength on any trial is proportional to the difference between the maximum possible associative strength between a cue and an outcome and the previous estimate of the strength of association. Thus, the ...
... that the change in associative strength on any trial is proportional to the difference between the maximum possible associative strength between a cue and an outcome and the previous estimate of the strength of association. Thus, the ...
Page 21
... that the intervention on X (or anything that causes the intervention) affects Y via a causal route that does not go through X, as happens, for example, when B in the example above is manipulated by changing the common cause A of B and S ...
... that the intervention on X (or anything that causes the intervention) affects Y via a causal route that does not go through X, as happens, for example, when B in the example above is manipulated by changing the common cause A of B and S ...
Page 24
... that the cue stick has struck the cue ball in some way or other or the fact that there has been transmission of blue chalk dust as causally relevant to whether the eight ball drops. Someone might both fully understand the abstract ...
... that the cue stick has struck the cue ball in some way or other or the fact that there has been transmission of blue chalk dust as causally relevant to whether the eight ball drops. Someone might both fully understand the abstract ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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