Agonist-antagonist muscular co-contraction improves rapid corrective responses
Bibliographic record
Abstract
Muscular co-contraction (simultaneous activation of both agonist and antagonist muscle groups) has been observed to emerge across a range of challenging tasks. But is it beneficial? In this study we address this untested assumption by quantifying the performance benefits of muscular co-contraction when making rapid corrective responses to mechanical perturbations. Even small levels of co-contraction resulted in significant performance improvements compared to the relaxed condition in a target recapture task following a mechanical perturbation. Performance was also better when co-contracting compared to when a single muscle group was active prior to the perturbation. The performance benefits of co-contracting seem partially attributable to neural mechanisms where activation of both agonist and antagonist muscles allowed both groups to contribute to limb control via reflex responses. These findings highlight how co-contraction can greatly impact motor performance. Further, the benefit of co-contraction extends beyond just increased muscle activity providing instantaneous resistance to limb motion.
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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 | 0.001 |
| 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) | 0.002 | 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".