Ruarte, G. | bioRxiv Science

bioRxiv2025

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 targets 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 models capacity to reflect cognitive processes underlying response selection and uncertainty judgment.

neuroscience↗

bioRxiv2024

Integrating Ideal Bayesian Searcher and Neural Networks Models for Eye Movement Prediction in a Hybrid Search Task

Visual search, where observers search for a specific item, is a crucial aspect of daily human interaction with the visual environment. Hybrid search extends this by requiring observers to search for any item from a given set of objects. While there are models proficient at simulating human eye movement in visual search tasks within natural scenes, none are able to do so in Hybrid search tasks within similar environments. In this work, we present an enhanced version of the neural network Entropy Limit Minimization (nnELM) model, which is based on a Bayesian framework and decision theory. We also present the Hybrid Search Eye Movements (HSEM) Dataset, comprising several thousands of human eye movements during hybrid search tasks in natural scenes. A key challenge in Hybrid search, absent in visual search, is that participants might search for different objects at different time points. To address this, we developed a strategy based on the posterior probability distribution generated after each fixation. By adjusting the models peripheral visibility, we made early search stages more efficient, aligning it closer to human behaviour. Additionally, limiting the models memory capacity reduced its success in longer searches, mirroring human performance. To validate these improvements, we compared our model against participants from the HSEM dataset and against existing models in a visual search benchmark. Altogether, the new nnELM model not only successfully explains Hybrid search tasks, but also closely replicates human behaviour in natural scenes. This work advances our understanding of complex processes underlying visual and Hybrid search while maintaining model interpretability.

neuroscience↗