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Evaluating computational models of explanation using human judgments
We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments.
Modeling human function learning with Gaussian processes
Accounts of how people learn functional relationships between continuous variables have tended to focus on two possibilities: that people are estimating explicit functions, or that they are simply performing associative learning supported by similarity.
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