Abstract
Attention filters sensory inputs to enhance task-relevant information. It is guided by an “attentional template” that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the 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 toward rewarded features. Finally, we found that 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 insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.
Caroline I.Jahn, Nikola T.Markov, Britney Morea, Nathaniel D.Daw, R.Becket Ebitz and Timothy J.Buschman. Learning attentional templates for value-based decision-making. Cell, 2024-2. [LINK]
Speaker: Fengjun Ma
Time: 9:00 am, 2024/03/25
Location: CIBR A328
