Modeling human function learning with Gaussian processes
Thomas L. Griffiths, Chris Lucas, Joseph Jay Williams, Michael L. Kalish · 2008
Accounts of how people learn functional relationships between continuous vari-ables 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 regres-sion in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, we show that learning explicit rules and us-ing 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. 1