John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States.
Center for Brain Science, Harvard University, Cambridge, United States.
Elife. 2021 Sep 6;10:e67019. doi: 10.7554/eLife.67019.
Actions often require the selection of a specific goal amongst a range of possibilities, like when a softball player must precisely position her glove to field a fast-approaching ground ball. Previous studies have suggested that during goal uncertainty the brain prepares for all potential goals in parallel and averages the corresponding motor plans to command an intermediate movement that is progressively refined as additional information becomes available. Although intermediate movements are widely observed, they could instead reflect a neural decision about the single best action choice given the uncertainty present. Here we systematically dissociate these possibilities using novel experimental manipulations and find that when confronted with uncertainty, humans generate a motor plan that optimizes task performance rather than averaging potential motor plans. In addition to accurate predictions of population-averaged changes in motor output, a novel computational model based on this performance-optimization theory accounted for a majority of the variance in individual differences between participants. Our findings resolve a long-standing question about how the brain selects an action to execute during goal uncertainty, providing fundamental insight into motor planning in the nervous system.
行动通常需要在一系列可能性中选择一个特定的目标，例如当垒球运动员必须准确地定位她的手套来接球时。以前的研究表明，在目标不确定的情况下，大脑会同时准备所有潜在的目标，并对相应的运动计划进行平均，以发出一个中间运动的指令，随着更多信息的出现，这个中间运动逐渐得到完善。尽管中间运动得到了广泛的观察，但它们也可能反映出在存在不确定性的情况下，大脑对单个最佳动作选择的神经决策。在这里，我们使用新的实验操作系统地分离了这些可能性，并发现当面对不确定性时，人类会生成一个优化任务表现的运动计划，而不是对潜在的运动计划进行平均。除了准确预测运动输出的群体平均变化外，基于这一性能优化理论的新计算模型还解释了参与者之间个体差异的大部分方差。我们的研究结果解决了一个关于大脑在目标不确定时如何选择执行动作的长期问题，为神经系统中的运动规划提供了基本的见解。
