Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

• Published in: Nature neuroscience Vol. 17; no. 2; pp. 312 - 321
• Main Authors: Wu, Howard G, Miyamoto, Yohsuke R, Castro, Luis Nicolas Gonzalez, Ölveczky, Bence P, Smith, Maurice A
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-02-2014; Nature Publishing Group
• Subjects: 631/378/1595/1637; 631/378/1595/2167; 631/378/2632; Adolescent; Adult; Animal Genetics and Genomics; Behavioral Sciences; Biological Techniques; Biomedicine; Brain mapping; Brain research; Female; Humans; Individuality; Learning; Learning - physiology; Male; Middle Aged; Motor learning; Movement - physiology; Nervous system; Neurobiology; Neurosciences; Nonlinear Dynamics; Predictive Value of Tests; Psychological aspects; Psychomotor Performance - physiology; Reward; Time Factors; Young Adult; United States > US; Massachusetts
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/nn.3616

Here the authors report that higher levels of task-relevant motor variability predict faster learning both across individuals and across tasks in two different paradigms and that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results support the importance of action exploration, a key idea from reinforcement learning theory.
Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.