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研究揭示解码和干扰决策状态的实时神经机制
2021-01-22 16:27

近日,美国斯坦福大学William T. Newsome等研究人员合作揭示解码和干扰决策状态的实时神经机制。该研究于2021年1月20日在线发表于国际一流学术期刊《自然》。

研究人员表示,在动态环境中,受试者通常会整合一个信号的多个样本,然后将它们合并以做出明确的判断。考虑的过程可以通过随时间变化的决策变量(DV)来描述,该变量是根据神经群体活动进行解码的,可以预测受试者即将做出的决策。然而,在单项试验中,DV中存在较大的瞬时变化,其行为意义尚不清楚。

通过使用刺激持续时间的实时、神经反馈控制,研究人员发现,在试验内DV波动内,运动皮层的解码与猕猴的决策状态紧密相关,并且预测行为选择远优于条件平均DV或仅视觉刺激。此外,DV征兆的强劲变化具有从心理变化行为研究中获得的统计规律。在刺激脉冲较弱的单项试验中探索决策过程时,研究人员发现了时变吸收决策界限的证据,这使得他们能够区分特定的决策模型。

附:英文原文

Title: Decoding and perturbing decision states in real time

Author: Diogo Peixoto, Jessica R. Verhein, Roozbeh Kiani, Jonathan C. Kao, Paul Nuyujukian, Chandramouli Chandrasekaran, Julian Brown, Sania Fong, Stephen I. Ryu, Krishna V. Shenoy, William T. Newsome

Issue&Volume: 2021-01-20

Abstract: In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment1. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject’s upcoming decision2. Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind3. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

DOI: 10.1038/s41586-020-03181-9

Source: https://www.nature.com/articles/s41586-020-03181-9

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

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