Context-dependent inhibition of unloaded muscles during the long-latency epoch
Bibliographic record
Abstract
A number of studies have highlighted the sophistication of corrective responses in lengthened muscles during the long-latency epoch. However, in various contexts, unloading can occur, which requires corrective actions from a shortened muscle. Here, we investigate the sophistication of inhibitory responses in shortened muscles due to unloading. Our first experiment quantified the inhibitory responses following an unloading torque that displaced the hand either into or away from a peripheral target. We observed larger long-latency inhibitory responses when perturbed into the peripheral target compared with away from the target. In our second experiment, we characterized the degree of inhibition following unloading with respect to different levels of preperturbation muscle activity. We initially observed that the inhibitory activity during the short-latency epoch scaled with increased levels of preperturbation muscle activity. However, this scaling peaked early in the R2 epoch (∼ 50 ms) but then quickly diminished through the rest of the long-latency epoch. Finally, in experiment 3, we investigated whether inhibitory perturbation responses consider intersegmental dynamics of the limb. We quantified unloading responses for either pure shoulder or pure elbow torques that evoked similar motion at the shoulder but different elbow motion. The long-latency inhibitory response in the shoulder, unlike the short-latency, was greater for the shoulder torque compared with the response following an elbow torque, as previously observed for a loading response. Taken together, these results illustrate that the long-latency unloading response is capable of a similar level of complexity as observed when loads are applied to the limb.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".