Abstract
How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their behavior. Across two experiments, one assessing function learning and one assessing the search for rewards, we find that our model captures human predictions and sampling behavior better than several alternatives, generates human-like learning curves, and also captures participants’ confidence judgements. Our results extend past models of human function learning to more complex, graph-structured domains.
Footnotes
Charley M. Wu, Harvard University; Eric Schulz, Max Planck Institute for Biological Cybernetics; Samuel J Gershman, Harvard University and the Center for Brains, Minds and Machines. This work was supported by the Dean’s Fund for Competitive Research at Harvard University, the Center for Brains, Minds, and Machines (CBMM), funded by NSF STC award CCF-1231216, the Office of Naval Research (N00014-17-1-2984), and the Alfred P. Sloan Foundation.