Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1371/journal.pcbi.1005044 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Noise reduction,Psychological intervention,Biology,Simulation,Motor skill,Throwing,Error detection and correction,Learning models,Audiology,Bioinformatics,Motor function | Journal | 12 |
Issue | ISSN | Citations |
8 | 1553-7358 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher J Hasson | 1 | 3 | 1.10 |
| Zhaoran Zhang | 2 | 1 | 0.70 |
| Masaki O Abe | 3 | 7 | 1.36 |
| Dagmar Sternad | 4 | 91 | 21.36 |