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 11
... effect given the cue must be greater than the probability of the effect in the absence of the cue. The Rescorla-Wagner theory (R-W theory; 1972) specified that learning occurred on a trial-by-trial basis and predicted that early trials ...
... effect given the cue must be greater than the probability of the effect in the absence of the cue. The Rescorla-Wagner theory (R-W theory; 1972) specified that learning occurred on a trial-by-trial basis and predicted that early trials ...
Page 12
... effects and then calculates the associative strength between each cause and each effect. However, the model cannot determine whether variables are causes or effects (i.e., it cannot decide whether A causes B, B causes A, or neither) ...
... effects and then calculates the associative strength between each cause and each effect. However, the model cannot determine whether variables are causes or effects (i.e., it cannot decide whether A causes B, B causes A, or neither) ...
Page 19
... effects, in comparison with some appropriately chosen alternative. Difference making can be explicated in a variety of ... effect, conditional on some suitable set of background conditions. When probabilistic theories attempt to define ...
... effects, in comparison with some appropriately chosen alternative. Difference making can be explicated in a variety of ... effect, conditional on some suitable set of background conditions. When probabilistic theories attempt to define ...
Page 20
... effects: If it is possible to manipulate a cause in the right way, then there would be an associated change in its effect. Conversely, if under some appropriately characterized manipulation of one factor, there is an associated change ...
... effects: If it is possible to manipulate a cause in the right way, then there would be an associated change in its effect. Conversely, if under some appropriately characterized manipulation of one factor, there is an associated change ...
Page 23
... effect variable to a single intervention on the cause variable. The notion of direct causation turns out to be ... effects. Given this assumption, both structures 1-1 and 1-2 imply exactly the same conditional and unconditional ...
... effect variable to a single intervention on the cause variable. The notion of direct causation turns out to be ... effects. Given this assumption, both structures 1-1 and 1-2 imply exactly the same conditional and unconditional ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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