Kiani Roozbeh, Cueva Christopher J, Reppas John B, Newsome William T
Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003, USA; Department of Neurobiology, Stanford University School of Medicine, Fairchild Building D209, Stanford, CA 94305, USA.
Department of Neurobiology, Stanford University School of Medicine, Fairchild Building D209, Stanford, CA 94305, USA.
Curr Biol. 2014 Jul 7;24(13):1542-7. doi: 10.1016/j.cub.2014.05.049. Epub 2014 Jun 19.
Decision making is a complex process in which different sources of information are combined into a decision variable (DV) that guides action [1, 2]. Neurophysiological studies have typically sought insight into the dynamics of the decision-making process and its neural mechanisms through statistical analysis of large numbers of trials from sequentially recorded single neurons or small groups of neurons [3-6]. However, detecting and analyzing the DV on individual trials has been challenging [7]. Here we show that by recording simultaneously from hundreds of units in prearcuate gyrus of macaque monkeys performing a direction discrimination task, we can predict the monkey's choices with high accuracy and decode DV dynamically as the decision unfolds on individual trials. This advance enabled us to study changes of mind (CoMs) that occasionally happen before the final commitment to a decision [8-10]. On individual trials, the decoded DV varied significantly over time and occasionally changed its sign, identifying a potential CoM. Interrogating the system by random stopping of the decision-making process during the delay period after stimulus presentation confirmed the validity of identified CoMs. Importantly, the properties of the candidate CoMs also conformed to expectations based on prior theoretical and behavioral studies [8]: they were more likely to go from an incorrect to a correct choice, they were more likely for weak and intermediate stimuli than for strong stimuli, and they were more likely earlier in the trial. We suggest that simultaneous recording of large neural populations provides a good estimate of DV and explains idiosyncratic aspects of the decision-making process that were inaccessible before.
决策是一个复杂的过程，在这个过程中，不同的信息源被整合到一个指导行动的决策变量（DV）中[1,2]。神经生理学研究通常通过对顺序记录的单个神经元或小群神经元的大量试验进行统计分析，来深入了解决策过程的动态及其神经机制[3-6]。然而，在单个试验中检测和分析DV一直具有挑战性[7]。在这里，我们表明，通过同时记录猕猴前弓状回中数百个神经元的活动，这些猕猴正在执行方向辨别任务，我们可以高精度地预测猴子的选择，并在单个试验中随着决策的展开动态解码DV。这一进展使我们能够研究在最终做出决策之前偶尔发生的思维变化（CoM）[8-10]。在单个试验中，解码后的DV随时间显著变化，偶尔会改变其符号，这表明存在潜在的思维变化。在刺激呈现后的延迟期内通过随机停止决策过程来询问该系统，证实了所识别的思维变化的有效性。重要的是，候选思维变化的特性也符合基于先前理论和行为研究的预期[8]：它们更有可能从错误选择转变为正确选择，对于弱刺激和中等强度刺激比对于强刺激更有可能发生，并且在试验早期更有可能发生。我们认为，同时记录大量神经群体能够很好地估计DV，并解释了以前无法获得信息的决策过程中的特殊方面。
