A rational model of function learning

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


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Association Learning*
  • Bayes Theorem
  • Concept Formation
  • Humans
  • Linear Models
  • Machine Learning*
  • Models, Psychological*
  • Normal Distribution
  • Problem Solving*
  • Transfer, Psychology