🤖 AI Summary
This study investigates how humans achieve slightly super-Bayesian decision performance in a brief, multi-target visual search task with spatially distributed targets—despite foveal sensitivity decline and neural limitations in exact Bayesian computation. Combining human behavioral experiments, Bayesian optimal modeling, signal detection theory, and neurobiologically grounded noise modeling, we identify three key mechanisms: (1) a simple spatial heuristic approximates—and occasionally surpasses—Bayesian-optimal performance; (2) a systematic “central neglect” effect, wherein observers down-weight responses from central visual field regions; and (3) spatially correlated neural noise, which paradoxically enhances search efficiency. Together, these mechanisms quantitatively account for the observed human performance exceeding the Bayesian upper bound derived under the standard assumption of independent neural noise. The findings reveal computational principles underlying efficient perceptual decision-making under biological constraints.
📝 Abstract
Visual search is a fundamental natural task for humans and other animals. We investigated the decision processes humans use when searching briefly presented displays having well-separated potential target-object 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 causes search performance to exceed that predicted for independent noise. These findings have far-reaching implications for understanding visual search tasks and other identification tasks in humans and other animals.