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Modeling human function learning with Gaussian processes
Tom Griffiths · Chris Lucas · Joseph Jay Williams · Michael Kalish

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @

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. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a Gaussian process model of human function learning that combines the strengths of both approaches.

Author Information

Tom Griffiths (Princeton)
Chris Lucas (UC Berkeley)
Joseph Jay Williams
Michael Kalish (ICS)

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