Fast corrective responses are evoked by perturbations approaching the natural variability of posture and movement tasks
Bibliographic record
Abstract
A wealth of studies highlight the importance of rapid corrective responses during voluntary motor tasks. These studies used relatively large perturbations to evoke robust muscle activity. Thus it remains unknown whether these corrective responses (latency 20-100 ms) are evoked at perturbation levels approaching the inherent variability of voluntary control. To fill this gap, we examined responses for large to small perturbations applied while participants either performed postural or reaching tasks. To address multijoint corrective responses, we induced various amounts of single-joint elbow motion with scaled amounts of combined elbow and shoulder torques. Indeed, such perturbations are known to elicit a response at the unstretched shoulder muscle, which reflects an internal model of arm intersegmental dynamics. Significant muscle responses were observed during both postural control and reaching, even when perturbation-related joint angle, velocity, and acceleration overlapped in distribution with deviations encountered in unperturbed trials. The response onsets were consistent across the explored range of perturbation loads, with short-latency onset for the muscles spanning the elbow joints (20-40 ms), and long-latency for shoulder muscles (onset > 45 ms). In addition, the evoked activity was strongly modulated by perturbation magnitude. These results suggest that multijoint responses are not specifically engaged to counter motor errors that exceed a certain threshold. Instead, we suggest that these corrective processes operate continuously during voluntary motor control.
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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) | 0.000 | 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".