Robustness of muscle synergies during visuomotor adaptation
Bibliographic record
Abstract
During visuomotor adaptation a novel mapping between visual targets and motor commands is gradually acquired. How muscle activation patterns are affected by this process is an open question. We tested whether the structure of muscle synergies is preserved during adaptation to a visuomotor rotation. Eight subjects applied targeted isometric forces on a handle instrumented with a force transducer while electromyographic (EMG) activity was recorded from 13 shoulder and elbow muscles. The recorded forces were mapped into horizontal displacements of a virtual sphere with simulated mass, elasticity, and damping. The task consisted of moving the sphere to a target at one of eight equally spaced directions. Subjects performed three baseline blocks of 32 trials, followed by six blocks with a 45° CW rotation applied to the planar force, and finally three wash-out blocks without the perturbation. The sphere position at 100 ms after movement onset revealed significant directional error at the beginning of the rotation, a gradual learning in subsequent blocks, and aftereffects at the beginning of the wash-out. The change in initial force direction was closely related to the change in directional tuning of the initial EMG activity of most muscles. Throughout the experiment muscle synergies extracted using a non-negative matrix factorization algorithm from the muscle patterns recorded during the baseline blocks could reconstruct the muscle patterns of all other blocks with an accuracy significantly higher than chance indicating structural robustness. In addition, the synergies extracted from individual blocks remained similar to the baseline synergies throughout the experiment. Thus synergy structure is robust during visuomotor adaptation suggesting that changes in muscle patterns are obtained by rotating the directional tuning of the synergy recruitment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".