Proceedings of the Annual Meeting of the Cognitive Science Society

Function learning research has highlighted the importance ofhuman inductive biases that facilitate long-range extrapola-tions. However, most previous research is focused on aggre-gate errors or single-criterion extrapolations. Thus, little isknown about the underlying psychological space in which con-tinuous relationships are represented. We ask whether peoplecan learn the distributional properties of new classes of rela-tionships, using Markov Chain Monte Carlo with People, andfind that (1) people are able to track not just the expected pa-rameters of a linear function, but information about the vari-ability of functions in a specific context and (2) in many casesthese spaces over parameters exhibit multiple modes.