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

Searching natural environments, as for example, when foraging or looking for a landmark in a city, combine reasoning under uncertainty, planning and visual search. Existing paradigms for studying search in humans focus on its isolated aspects, such as step-by-step information sampling or visual search, 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 modelled by a Partially Observable Markov Decision Process (POMDP), which maximises 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 using a Gaussian Mixture Model over inverse POMDP consistently assign most subjects to one style or the another. By analysing individual participants’ decision times during the task we show that individuals often solve partial POMDPs and plan their search a limited number of steps in advance.