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 1
... variables in S conditional on any set of values of the variables that are parents of R! Let me give you a brief sense of where I'm coming from, as we say in mellow Arcadia (though I'm a New Yorker myself). I went to Public School 164 ...
... variables in S conditional on any set of values of the variables that are parents of R! Let me give you a brief sense of where I'm coming from, as we say in mellow Arcadia (though I'm a New Yorker myself). I went to Public School 164 ...
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... variables. The variables can be discrete (like school grade) or continuous (like weight); they can be binary (like “having eyes” or “not having eyes”) or take a range of values (like color). Similarly, the direct causal relations can ...
... variables. The variables can be discrete (like school grade) or continuous (like weight); they can be binary (like “having eyes” or “not having eyes”) or take a range of values (like color). Similarly, the direct causal relations can ...
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... variables by itself is consistent with both these structures. You can discriminate between these two graphs by looking at the patterns of conditional probability among the three variables. Suppose you keep track of all the times you ...
... variables by itself is consistent with both these structures. You can discriminate between these two graphs by looking at the patterns of conditional probability among the three variables. Suppose you keep track of all the times you ...
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... variable X in a causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph), (b) directly fixes the value of X to x, and (c) does not affect the values of any other variables in the ...
... variable X in a causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph), (b) directly fixes the value of X to x, and (c) does not affect the values of any other variables in the ...
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... variables that are common causes of the observed variables (Richardson & Spirtes, 2003; Silva, Scheines, Glymour, & Spirtes, 2003). Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence ...
... variables that are common causes of the observed variables (Richardson & Spirtes, 2003; Silva, Scheines, Glymour, & Spirtes, 2003). Causal Bayes net representations and learning algorithms allow learners to predict patterns of evidence ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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