Uncertainty during visual search: Insights from a computational model and behavioral experiment in natural stimuli

Visual search, driven by bottom-up and top-down processes, offers a unique framework for investigating decision-making. This study examines individuals’ awareness of their own visual search by combining computational modeling with behavioral experiments. Fifty-seven participants performed a classical visual search task in which the goal was to find an object in a natural scene. Crucially, in some trials, the search was interrupted by clearing the screen before the gaze reached the target object. Participants had to report their best guess of the target’s location and the uncertainty on their response. We show that a modified version of the Entropy-Limit Minimization (ELM) model captures scanpaths and perceived target locations, while also revealing that uncertainty is influenced by scanpath length, the distance between the perceived and true target location, and the entropy of decision maps. These findings highlight the model’s capacity to reflect cognitive processes underlying response selection and uncertainty judgment.