Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking
研究了惯用手对人在回路控制系统中学习轨迹跟踪任务的影响，发现无论用哪只手，性能提升都源于反馈控制的适应，且双手间无显著差异。
In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This article studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a nonparametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e., applied by the experimenter) and endogenously (i.e., generated by sensorimotor noise) explains observed performance improvements.