Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, 184-8588, Japan.
Sci Rep. 2020 Feb 12;10(1):2422. doi: 10.1038/s41598-020-59257-z.
Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. Several previous methods have succeeded in this type of decomposition while focusing on the clear relation of motion to both a specific goal and a continuous outcome, such as a 10 mm deviation from a target or 1 m/s hand velocity. In daily life, it is vital to quantify not only continuous but also categorical outcomes. For example, in baseball, batters must judge whether the opposing pitcher will throw a fastball or a breaking ball; tennis players must decide whether an opposing player will serve out wide or down the middle. However, few methods have focused on quantifying categorical outcome; thus, how to decompose motion data into task-relevant and task-irrelevant components when the outcome is categorical rather than continuous remains unclear. Here, we propose a data-driven method to decompose motion data into task-relevant and task-irrelevant components when the outcome takes categorical values. We applied our method to experimental data where subjects were required to throw fastballs or breaking balls with a similar form. Our data-driven approach can be applied to the unclear relation between motion and outcome, and the relation can be estimated in a data-driven manner. Furthermore, our method can successfully evaluate how the task-relevant components are modulated depending on the task requirements.
将运动数据分解为与任务相关和与任务无关的成分是阐明运动控制和学习中涉及的多种特征的有效方法。以前有几种方法成功地实现了这种分解，同时关注运动与特定目标和连续结果之间的明确关系，例如目标偏差 10 毫米或手速 1 米/秒。在日常生活中，不仅要量化连续的结果，还要量化分类的结果。例如，在棒球中，击球手必须判断投手是否会投出快球还是曲线球；网球运动员必须决定对手球员是否会发球到外侧还是中路。然而，很少有方法专注于量化分类结果；因此，当结果是分类而不是连续时，如何将运动数据分解为与任务相关和与任务无关的成分仍然不清楚。在这里，我们提出了一种数据驱动的方法，可以将运动数据分解为与任务相关和与任务无关的成分，当结果取分类值时。我们将我们的方法应用于实验数据，其中要求受试者以相似的形式投出快球或曲线球。我们的基于数据的方法可以应用于运动与结果之间不明确的关系，并且可以以数据驱动的方式估计这种关系。此外，我们的方法可以成功地评估任务相关成分如何根据任务要求进行调制。
