Thinking and Guessing: Bayesian and Empirical Models of How Humans Search.


Searching natural environments, as for example, when foraging or looking for a landmark, combines reasoning under uncertainty, planning and visual search. Existing paradigms for studying search in humans focus on step-by-step information sampling, without examining advance planning. We propose and evaluate a Bayesian model of how people search in a naturalistic maze-solving task. The model encodes environment exploration as a sequential process of acquiring information modeled by a Partially Observable Markov Decision Process (POMDP), which maximizes the information gained. We show that the search policy averaged across participants is optimal. Individual solutions, however, are highly variable and can be explained by two heuristics: thinking and guessing. Self-report and inference, a Gaussian Mixture Model over inverse POMDP, consistently assign most subjects to one style or the other. By analyzing individual participants' decision times we show that individuals solve partial POMDPs and plan their search a limited number of steps in advance.

Proceedings of the 39th Annual Conference of the Cognitive Science Society
Tomer Ullman
Primary Investigator

My research focuses on the structure and origin of knowledge, guided by perspectives and methods from cognitive science, cognitive development, and computational modeling. By combining these, I hope to better understand the form and development of the basic commonsense reasoning that guides our interaction with the world and the people in it.