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Psychon Bull Rev. 2015 Oct;22(5):1193-215. doi: 10.3758/s13423-015-0808-5. Epub 2015 Mar 3.

A rational model of function learning.

Author information

1
School of Informatics, University of Edinburgh, 10 Crichton St., Edinburgh, EH8 9AB, UK. c.lucas@ed.ac.uk.
2
Department of Psychology, University of California, Berkeley, USA.
3
HarvardX, Harvard University, Cambridge, USA.
4
Department of Psychology, Syracuse University, Syracuse, USA.

Abstract

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two 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, which provide a probabilistic basis for similarity-based function learning, 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 rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.

KEYWORDS:

Bayesian modeling; Function learning

PMID:
25732094
DOI:
10.3758/s13423-015-0808-5
[Indexed for MEDLINE]

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