Attention when you need

2025-01-13

AI Summary

This study investigates how mice optimize decision efficiency by balancing attentional costs against benefits during an auditory sustained-attention–value task. Method: We developed a normative reinforcement learning model integrating behavioral analysis, optimal control theory, and dynamic accumulation of sensory evidence. Contribution/Results: We propose, for the first time, that attentional resources are deployed in rhythmic, alternating high–low blocks—a “blockwise” allocation strategy. Attentional policy is jointly determined by task utility, stimulus statistics, and attentional gain modulation of sensory evidence; low-attention states fully suppress sensory input, while high-attention states activate periodically. The model successfully reproduces and explains mice’s attentional allocation patterns across varying trial durations and reward contingencies. Our work establishes a novel theoretical framework—grounded in economic principles—for understanding attentional resource optimization and provides empirical support for rhythmically gated, cost-sensitive attentional control.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Being attentive to task-relevant features can improve task performance, but paying attention comes with its own metabolic cost. Therefore, strategic allocation of attention is crucial in performing the task efficiently. This work aims to understand this strategy. Recently, de Gee et al. conducted experiments involving mice performing an auditory sustained attention-value task. This task required the mice to exert attention to identify whether a high-order acoustic feature was present amid the noise. By varying the trial duration and reward magnitude, the task allows us to investigate how an agent should strategically deploy their attention to maximize their benefits and minimize their costs. In our work, we develop a reinforcement learning-based normative model of the mice to understand how it balances attention cost against its benefits. The model is such that at each moment the mice can choose between two levels of attention and decide when to take costly actions that could obtain rewards. Our model suggests that efficient use of attentional resources involves alternating blocks of high attention with blocks of low attention. In the extreme case where the agent disregards sensory input during low attention states, we see that high attention is used rhythmically. Our model provides evidence about how one should deploy attention as a function of task utility, signal statistics, and how attention affects sensory evidence.

Problem

• Attention strategies
• Optimal balance
• Auditory tasks

Innovation

• Attention Modulation
• Auditory Processing
• Efficient Brain Strategy