Optimal Visual Search with Highly Heuristic Decision Rules
This research addresses visual search performance in humans, providing insights into cognitive mechanisms, but it is incremental as it builds on existing models of optimal decision-making.
The study investigated human decision processes in covert visual search and found that humans performed slightly better than Bayesian-optimal predictions, despite limitations like foveal neglect. This was explained by factors including simple heuristic rules, localized foveal effects, and spatially correlated neural noise.
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.