Learning attentional templates for value-based decision-making

Abstract

AbstractAttention filters sensory inputs to enhance task-relevant information. It is guided by an ‘attentional template’ that represents the stimulus features that are relevant for the current task. To understand how the brain learns and uses new templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found templates were represented across prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template towards rewarded features. Finally, we found attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide new insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.