Abstract
Motor variability is inevitable in our body movements and is discussed from several various perspectives in motor neuroscience and biomechanics; it can originate from the variability of neural activities, it can reflect a large degree of freedom inherent in our body movements, it can decrease muscle fatigue, or it can facilitate motor learning. How to evaluate motor variability is thus a fundamental question in motor neuroscience and biomechanics. Previous methods have quantified (at least) two striking features of motor variability; the smaller variability in the task-relevant dimension than in the task-irrelevant dimension and the low-dimensional structure that is often referred to as synergy or principal component. However, those previous methods were not only unsuitable for quantifying those features simultaneously but also applicable in some limited conditions (e.g., a method cannot consider motion sequence, and another method cannot consider how each motion is relevant to performance). Here, we propose a flexible and straightforward machine learning technique that can quantify task-relevant variability, task-irrelevant variability, and the relevance of each principal component to task performance while considering the motion sequence and the relevance of each motion sequence to task performance in a data-driven manner. We validate our method by constructing a novel experimental setting to investigate goal-directed and whole-body movements. Furthermore, our setting enables the induction of motor adaptation by using perturbation and evaluating the modulation of task-relevant and task-irrelevant variabilities through motor adaptation. Our method enables the identification of a novel property of motor variability; the modulation of those variabilities differs depending on the perturbation schedule. Although a gradually imposed perturbation does not increase both task-relevant and task-irrelevant variabilities, a constant perturbation increases task-relevant variability.
