P01: Maximum Entropy Function Learning

Authors: Simon Segert, Jonathan Cohen

Abstract: Understanding how people generalize and extrapolate from limited amounts of data remains an outstanding challenge. We study this question in the domain of scalar function learning, and propose a simple model based on the Principle of Maximum Entropy (Jaynes, 1957). Through computational modeling, we demonstrate that the theory makes two specific predictions about peoples’ extrapolation judgments, that we validate through experiments. Moreover, we show that existing Gaussian Process models of function learning cannot account for these effects.