de Lemos Fonseca Marcus, Daneault Jean-François, Vergara-Diaz Gloria, Quixadá Ana Paula, Souza de Oliveira E Torres Ângelo Frederico, Pondé de Sena Eduardo, Bomfim Cruz Vieira João Paulo, Bigogno Reis Cazeta Bianca, Sotero Dos Santos Vitor, da Cruz Figueiredo Thiago, Peña Norberto, Bonato Paolo, Vivas Miranda José Garcia
Faculdade Social da Bahia, Salvador, Brazil.
Department of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ, USA.
Eur J Neurosci. 2020 May;51(10):2082-2094. doi: 10.1111/ejn.14649. Epub 2020 Jan 3.
It has been argued that the central nervous system relies on combining simple movement elements (i.e. motor primitives) to generate complex motor outputs. However, how movement elements are generated and combined during the acquisition of new motor skills is still a source of debate. Herein, we present results providing new insights into the role of movement elements in the acquisition of motor skills that we obtained by analysing kinematic data collected while healthy subjects learned a new motor task. The task consisted of playing an interactive game using a platform with embedded sensors whose aggregate output was used to control a virtual object in the game. Subjects learned the task over multiple blocks. The analysis of the kinematic data was carried out using a recently developed technique referred to as "movement element decomposition." The technique entails the decomposition of complex multi-dimensional movements in one-dimensional elements marked by a bell-shaped velocity profile. We computed the number of movement elements during each block and measured how closely they matched a theoretical velocity profile derived by minimizing a cost function accounting for the smoothness of movement and the cost of time. The results showed that, in the early stage of motor skill acquisition, two mechanisms underlie the improvement in motor performance: 1) a decrease in the number of movement elements composing the motor output and 2) a gradual change in the movement elements that resulted in a shape matching the velocity profile derived by using the above-mentioned theoretical model.
有人认为,中枢神经系统依靠组合简单的运动元素(即运动基元)来产生复杂的运动输出。然而,在获得新运动技能的过程中,运动元素是如何产生和组合的,仍然是一个争论的焦点。在此,我们展示了一些结果,这些结果通过分析健康受试者学习一项新运动任务时收集的运动学数据,为运动元素在运动技能习得中的作用提供了新的见解。该任务包括使用一个带有嵌入式传感器的平台玩一款互动游戏,传感器的总输出用于控制游戏中的虚拟物体。受试者通过多个阶段学习该任务。运动学数据的分析是使用一种最近开发的技术进行的,该技术被称为“运动元素分解”。该技术需要将复杂的多维运动分解为一维元素,这些元素以钟形速度曲线为特征。我们计算了每个阶段的运动元素数量,并测量了它们与通过最小化一个考虑运动平滑度和时间成本的代价函数得出的理论速度曲线的匹配程度。结果表明,在运动技能习得的早期阶段,运动表现的提高有两种机制:1)构成运动输出的运动元素数量减少;2)运动元素逐渐变化,导致其形状与使用上述理论模型得出的速度曲线相匹配。