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.
Wu, Howard GMiyamoto, Yohsuke RGonzalez Castro, Luis NicolasOlveczky, Bence PSmith, Maurice AengR01 AG041878/AG/NIA NIH HHS/R01 NS066408/NS/NINDS NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov't2014/01/15 06:00Nat Neurosci. 2014 Feb;17(2):312-21. doi: 10.1038/nn.3616. Epub 2014 Jan 12.