小柯机器人

美国普林斯顿大学Timothy J. Buschman等研究人员合作揭示猴子如何学习基于价值决策的注意力模板。这一研究成果于2024年2月23日在线发表在国际学术期刊《细胞》上。

为了了解大脑如何学习和使用模板，研究人员训练猴子执行一项视觉搜索任务，要求它们反复学习新的注意模板。神经记录发现，模板以结构化的方式在前额叶和顶叶皮层中得到表征，因此知觉上相邻的模板具有相似的神经表征。当任务发生变化时，通过逐步将模板转向受奖励的特征，可以学习到新的注意模板。

最后，研究人员发现注意力模板将刺激特征转化为一种共同的价值表征，无论模板的身份如何，都能通过相同的决策机制来部署注意力。总之，这些研究结果让人们深入了解了大脑学习控制注意力的神经机制，以及如何在不同任务中灵活调配注意力。

据了解，注意过滤感觉输入，以增强与任务相关的信息。它由“注意模板”引导，“注意模板”代表了当前相关的刺激特征。

附：英文原文

Title: Learning attentional templates for value-based decision-making

Author: Caroline I. Jahn, Nikola T. Markov, Britney Morea, Nathaniel D. Daw, R. Becket Ebitz, Timothy J. Buschman

Issue&Volume: 2024-02-23

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.

DOI: 10.1016/j.cell.2024.01.041

Source: https://www.cell.com/cell/fulltext/S0092-8674(24)00110-7