Modeling human function learning with Gaussian processes

Thomas L Griffiths, Christopher Lucas, Joseph Williams, Michael L Kalish

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

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
Title of host publicationAdvances in Neural Information Processing Systems 21
Subtitle of host publication22nd Annual Conference on Neural Information Processing Systems 2008
EditorsD. Koller, D. Schuurmans, Y. Bengio, L. Bottou
PublisherNeural Information Processing Systems
Pages553-560
Number of pages8
ISBN (Print)978-1-60560-949-2
Publication statusPublished - 2009

Fingerprint

Dive into the research topics of 'Modeling human function learning with Gaussian processes'. Together they form a unique fingerprint.

Cite this