Modeling human function learning with Gaussian processes

Thomas L. Griffiths, Christopher G. Lucas, Joseph J. Williams, Michael L. Kalish

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

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 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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages553-560
Number of pages8
StatePublished - Dec 1 2009
Externally publishedYes
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Other

Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CountryCanada
CityVancouver, BC
Period12/8/0812/11/08

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Griffiths, T. L., Lucas, C. G., Williams, J. J., & Kalish, M. L. (2009). Modeling human function learning with Gaussian processes. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 553-560). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).