Sport Sciences Department, Miguel Hernández University of Elche, 03202 Elche, Spain.
Sensors (Basel). 2021 Sep 27;21(19):6448. doi: 10.3390/s21196448.
Currently, it is not fully understood how motor variability is regulated to ease of motor learning processes during reward-based tasks. This study aimed to assess the potential relationship between different dimensions of motor variability (i.e., the motor variability structure and the motor synergies variability) and the learning rate in a reward-based task developed using a two-axis force sensor in a computer environment. Forty-four participants performed a pretest, a training period, a posttest, and three retests. They had to release a virtual ball to hit a target using a vertical handle attached to a dynamometer in a computer-simulated reward-based task. The participants' throwing performance, learning ratio, force applied, variability structure (detrended fluctuation analysis, DFA), and motor synergy variability (good and bad variability ratio, GV/BV) were calculated. Participants with higher initial GV/BV displayed greater performance improvements than those with lower GV/BV. DFA did not show any relationship with the learning ratio. These results suggest that exploring a broader range of successful motor synergy combinations to achieve the task goal can facilitate further learning during reward-based tasks. The evolution of the motor variability synergies as an index of the individuals' learning stages seems to be supported by our study.
目前，人们对于运动变异性是如何调节以促进基于奖励任务的运动学习过程的还不完全了解。本研究旨在评估在计算机环境中使用二维力传感器开发的基于奖励任务中，不同维度的运动变异性（即运动变异性结构和运动协同变异性）与学习率之间的潜在关系。44 名参与者进行了预测试、训练期、后测试和 3 次再测试。他们必须使用附在测力器上的垂直手柄在计算机模拟的基于奖励的任务中释放虚拟球来击中目标。计算了参与者的投掷表现、学习比率、施加的力、变异性结构（去趋势波动分析，DFA）和运动协同变异性（良好和不良变异性比，GV/BV）。初始 GV/BV 较高的参与者比 GV/BV 较低的参与者表现出更大的性能提升。DFA 与学习率没有关系。这些结果表明，在基于奖励的任务中，探索更广泛的成功运动协同组合以实现任务目标可以促进进一步学习。我们的研究似乎支持运动协同变异性作为个体学习阶段的指标的演变。
