title: Neuromotor Noise Is Malleable by Amplifying Perceived Errors.
abstract: Variability in motor performance results from the interplay of error correction and neuromotor noise. This study examined whether visual amplification of error, previously shown to improve performance, affects not only error correction, but also neuromotor noise, typically regarded as inaccessible to intervention. Seven groups of healthy individuals, with six participants in each group, practiced a virtual throwing task for three days until reaching a performance plateau. Over three more days of practice, six of the groups received different magnitudes of visual error amplification; three of these groups also had noise added. An additional control group was not subjected to any manipulations for all six practice days. The results showed that the control group did not improve further after the first three practice days, but the error amplification groups continued to decrease their error under the manipulations. Analysis of the temporal structure of participants' corrective actions based on stochastic learning models revealed that these performance gains were attained by reducing neuromotor noise and, to a considerably lesser degree, by increasing the size of corrective actions. Based on these results, error amplification presents a promising intervention to improve motor function by decreasing neuromotor noise after performance has reached an asymptote. These results are relevant for patients with neurological disorders and the elderly. More fundamentally, these results suggest that neuromotor noise may be accessible to practice interventions.
authors.affiliation: Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, Massachusetts, United States of America.
authors.forename: Christopher J
authors.identifier: 0000-0002-9851-0919
authors.initials: CJ
authors.lastname: Hasson
authors.affiliation: Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.
authors.forename: Zhaoran
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Graduate School of Education, Hokkaido University, Sapporo, Hokkaido, Japan.
authors.forename: Masaki O
authors.initials: MO
authors.lastname: Abe
authors.affiliation: Departments of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, Boston, Massachusetts, United States of America.|Center for the Interdisciplinary Research on Complex Systems, Northeastern University, Boston, Massachusetts, United States of America.
authors.forename: Dagmar
authors.initials: D
authors.lastname: Sternad
mesh_terms: D000328:Adult; D000465:Algorithms; D019295:Computational Biology; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009048:Motor Skills / Q000502:physiology*; D013647:Task Performance and Analysis; D055815:Young Adult