Attention when you need

TL;DR

This paper models how mice strategically allocate attention during a task, balancing metabolic costs and benefits, using a reinforcement learning approach that predicts rhythmic attention deployment based on task demands.

Contribution

It introduces a normative reinforcement learning model that explains strategic, rhythmic attention allocation in mice during a complex auditory task.

Findings

Efficient attention involves alternating high and low attention blocks.

Rhythmic high attention emerges when sensory input is disregarded during low attention.

Model aligns with observed attention strategies in mice.

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.