Spinal stretch reflexes exploit musculoskeletal redundancy to support postural hand control
Bibliographic record
Abstract
Abstract Motor behaviour is most efficiently controlled by only correcting disturbances or deviations that influence task success. It is currently thought that such sophisticated control is computed within a transcortical feedback pathway. Here we show that even the fastest spinal feedback pathway can produce corrective responses that adhere to this control scheme. We first applied small mechanical perturbations that flexed the elbow joint – stretching the triceps muscle – and simultaneously flexed or extended the wrist joint, displacing the hand various distances away from a central target. We then changed the arm’s orientation and applied the same joint perturbations, which reversed the mapping between joint motion and hand displacement. In all cases, we found that the triceps’ spinal stretch reflex was tuned to the hand’s displacement relative to the target, and not how the triceps muscle was stretched. Our findings reveal that the fastest spinal feedback pathway is capable of integrating and modulating feedback from multiple muscles to produce efficient corrective responses, forcing a re-evaluation of the how the nervous system derives the sophisticated control laws that support natural motor behaviour.
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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 | 0.001 |
| 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".