Co-contraction uses dual control of agonist-antagonist muscles to improve motor performance
Bibliographic record
Abstract
Abstract Co-contraction of agonist-antagonist muscles is commonly observed when performing difficult motor tasks. The benefit of co-contraction is thought to be zero-delay corrections to unexpected disturbances from increased intrinsic muscle impedance. We used upper-limb postural and tracking tasks to characterize the effects of co-contraction on motor corrections to loads applied to the limb. We systematically controlled pre-perturbation muscle activity and showed that co-contraction improves subsequent corrective responses in both tasks. However, substantial improvements in the corrective response are only observed at the time when neural feedback pathways can also contribute. We demonstrate that muscle impedance appears to play a minor role in improving performance. Instead, co-contraction engages a dual agonist-antagonist control strategy to counter disturbances, that is distinct from the control strategy used when not co-contracting or selectively pre-activating a single muscle group. Critically, we showed that this dual agonist-antagonist control strategy improved performance even at low levels of co-contraction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".