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
... the causal structure of the world. Scientists infer causal structure by observing the patterns of conditional probability among events (as in statistical analysis), by examining the consequences of interventions (as in experiments), or ...
... the causal structure of the world. Scientists infer causal structure by observing the patterns of conditional probability among events (as in statistical analysis), by examining the consequences of interventions (as in experiments), or ...
Page 5
... 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 cause ... the graph. The Intervention Assumption A variable I is an intervention on INTRODUCTION 5.
... 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 cause ... the graph. The Intervention Assumption A variable I is an intervention on INTRODUCTION 5.
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... the graph), (b) directly fixes the value of X to x, and (c) does not affect the values of any other variables in the graph except through its influence on X. Given this assumption, we can accurately predict the effects of interventions ...
... the graph), (b) directly fixes the value of X to x, and (c) does not affect the values of any other variables in the graph except through its influence on X. Given this assumption, we can accurately predict the effects of interventions ...
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... the faithfulness assumption. The Faithfulness Assumption In the joint distribution on the variables in the graph, all conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness ...
... the faithfulness assumption. The Faithfulness Assumption In the joint distribution on the variables in the graph, all conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness ...
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... The probability of the 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 ...
... The probability of the 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 ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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