Dual agonist and antagonist muscle vibration produces a bias in end point with no change in variability
Bibliographic record
Abstract
Abstract Muscle spindles provide critical proprioceptive feedback about muscle length to the central nervous system (CNS). Single muscle tendon vibration can stimulate muscle spindles, causing participants to misjudge limb position, while dual muscle tendon vibration is thought to produce a noisy proprioceptive system. It is currently unclear exactly how the CNS uses kinesthetic feedback from the agonist and antagonist muscles during target-directed reaches. The purpose of the current project was to investigate the effects of agonist, antagonist, and dual agonist/antagonist during target-directed reaching. Using an elbow extension task, we found that antagonist muscle vibration produced an undershooting effect relative to the no-vibration control, while agonist muscle vibration produced an overshooting effect relative to the no-vibration control. Neither of the single muscle vibrations produced any change in the variable error of the movements. While it was originally hypothesized that dual agonist/antagonist vibration would increase participants variable error with no change in bias, the opposite was found. Participants undershot relative to the no-vibration control with no change in variable error. Overall, the results from this study suggest that dual vibration does not necessarily create a noisy proprioceptive system but can produce a bias in end point.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".