An Active Inference Model of Covert and Overt Visual Attention

2025-05-06

AI Summary

This study addresses the selective mechanism of visual attention under high-dimensional sensory input. Methodologically, it introduces the first unified bimodal (covert/overt) visual attention model grounded in active inference, innovatively incorporating Bayesian dynamic modulation of sensory precision—optimized online via free-energy minimization. The model mechanistically unifies exogenous and endogenous attention, accounts for valid/invalid cueing effects and inhibition-of-return, and quantitatively reproduces cueing effects on reaction times in the Posner paradigm. Simulation results demonstrate that reflexive saccades are faster but less adaptive, whereas goal-directed saccades exhibit superior task-oriented flexibility. Crucially, this work establishes dynamic sensory precision modulation as a computational nexus, enabling the first mechanistic integration and empirically testable modeling of core attentional phenomena within a principled Bayesian framework.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Modeling covert and overt visual attention using active inference
• Optimizing sensory precisions to minimize free-energy in attention
• Analyzing attention behavior in Posner cueing and target tasks

Innovation

• Active inference optimizes sensory precisions dynamically
• Model integrates covert and overt visual attention
• Tests with Posner cueing and target focus tasks