An Active Inference Model of Covert and Overt Visual Attention

TL;DR

This paper presents a novel active inference model for visual attention that dynamically allocates sensory resources, effectively simulating human attentional behaviors and reaction times in visual tasks.

Contribution

It introduces a new active inference framework for covert and overt attention, integrating sensory precision optimization based on environmental beliefs.

Findings

Model reproduces faster reaction times for exogenous and valid cues.

Exhibits inhibition of return behavior in attention shifts.

Reflexive saccades are faster but less adaptable.

Abstract

The ability to selectively attend to relevant stimuli while filtering out distractions is essential for agents that process complex, high-dimensional sensory input. This paper introduces a model of covert and overt visual attention through the framework of active inference, utilizing dynamic optimization of sensory precisions to minimize free-energy. The model determines visual sensory precisions based on both current environmental beliefs and sensory input, influencing attentional allocation in both covert and overt modalities. To test the effectiveness of the model, we analyze its behavior in the Posner cueing task and a simple target focus task using two-dimensional(2D) visual data. Reaction times are measured to investigate the interplay between exogenous and endogenous attention, as well as valid and invalid cueing. The results show that exogenous and valid cues generally lead to faster reaction times compared to endogenous and invalid cues. Furthermore, the model exhibits behavior similar to inhibition of return, where previously attended locations become suppressed after a specific cue-target onset asynchrony interval. Lastly, we investigate different aspects of overt attention and show that involuntary, reflexive saccades occur faster than intentional ones, but at the expense of adaptability.