A rational model of function learning

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

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalPsychonomic Bulletin & Review
Early online date3 Mar 2015
Publication statusPublished - Mar 2015


  • Function learning
  • Bayesian modeling


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