Optimal Visual Search with Highly Heuristic Decision Rules

TL;DR

Humans perform near optimally in visual search tasks by using simple heuristic decision rules, outperforming Bayesian models under certain neural noise and neglect conditions, revealing insights into natural search behaviors.

Contribution

This study demonstrates that simple, fixed heuristic rules can achieve near-optimal visual search performance, challenging the assumption that complex Bayesian computations are necessary.

Findings

Humans outperform Bayesian-optimal models in certain visual search tasks.

Heuristic decision rules can explain near-optimal performance.

Foveal neglect affects only central target detection.

Abstract

Visual search is a fundamental natural task for humans and other animals. We investigated the decision processes humans use in covert (single-fixation) search with briefly presented displays having well-separated potential target locations. Performance was compared with the Bayesian-optimal decision process under the assumption that the information from the different potential target locations is statistically independent. Surprisingly, humans performed slightly better than optimal, despite humans' substantial loss of sensitivity in the fovea (foveal neglect), and the implausibility of the human brain replicating the optimal computations. We show that three factors can quantitatively explain these seemingly paradoxical results. Most importantly, simple and fixed heuristic decision rules reach near optimal search performance. Secondly, foveal neglect primarily affects only the central potential target location. Finally, spatially correlated neural noise can cause search performance to exceed that predicted for independent noise. These findings have broad implications for understanding visual search tasks and other identification tasks in humans and other animals.