Inference and Search on Graph-Structured Spaces
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 and reward learning to more complex, graph-structured domains.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 02, 2020
- Source ID
- 10.1007/s42113-020-00091-x
Entities
People
- Charley M Wu
- Eric Schulz
- Samuel J Gershman
Organizations
- Alfred P. Sloan Foundation
- National Science Foundation
- United States Naval Research Laboratory