Proceedings of the Annual Meeting of the Cognitive Science Society

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 Maxi- mum Entropy (Jaynes, 1957). Through computational model- ing, we demonstrate that the theory makes two specific predic- tions about peoples’ extrapolation judgments, that we validate through experiments. Moreover, we show that existing Gaus- sian Process models of function learning cannot account for these effects.