title: Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.
abstract: 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.
authors.affiliation: 1] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA. [2].
authors.forename: Howard G
authors.initials: HG
authors.lastname: Wu
authors.affiliation: 1] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA. [2].
authors.forename: Yohsuke R
authors.initials: YR
authors.lastname: Miyamoto
authors.affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Luis Nicolas
authors.initials: LN
authors.lastname: Gonzalez Castro
authors.affiliation: 1] Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA. [2] Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Bence P
authors.initials: BP
authors.lastname: Ölveczky
authors.affiliation: 1] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA. [2] Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Maurice A
authors.initials: MA
authors.lastname: Smith
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D006801:Humans; D007206:Individuality; D007858:Learning / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D011597:Psychomotor Performance / Q000502:physiology*; D012201:Reward; D013997:Time Factors; D055815:Young Adult