A rational model of function learning.
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.
School of Informatics, University of Edinburgh, 10 Crichton St., Edinburgh, EH8 9AB, UK. c.lucas@ed.ac.uk.
Christopher G
CG
Lucas
Department of Psychology, University of California, Berkeley, USA.
Thomas L
TL
Griffiths
HarvardX, Harvard University, Cambridge, USA.
Joseph J
JJ
Williams
Department of Psychology, Syracuse University, Syracuse, USA.
Michael L
ML
Kalish
D001245:Association Learning; D001499:Bayes Theorem; D003210:Concept Formation; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008960:Models, Psychological; D016011:Normal Distribution; D011340:Problem Solving; D014163:Transfer, Psychology
Bayesian modeling; Function learning