Possible contributions of CPG activity to the control of rhythmic human arm movement
Bibliographic record
Abstract
There is extensive modulation of cutaneous and H-reflexes during rhythmic leg movement in humans. Mechanisms controlling reflex modulation (e.g., phase- and task-dependent modulation, and reflex reversal) during leg movements have been ascribed to the activity of spinal central pattern generating (CPG) networks and peripheral feedback. Our working hypothesis has been that neural mechanisms (i.e., CPGs) controlling rhythmic movement are conserved between the human lumbar and cervical spinal cord. Thus reflex modulation during rhythmic arm movement should be similar to that for rhythmic leg movement. This hypothesis has been tested by studying the regulation of reflexes in arm muscles during rhythmic arm cycling and treadmill walking. This paper reviews recent studies that have revealed that reflexes in arm muscles show modulation within the movement cycle (e.g., phase-dependency and reflex reversal) and between static and rhythmic motor tasks (e.g., task-dependency). It is concluded that reflexes are modulated similarly during rhythmic movement of the upper and lower limbs, suggesting similar motor control mechanisms. One notable exception to this pattern is a failure of contralateral arm movement to modulate reflex amplitude, which contrasts directly with observations from the leg. Overall, the data support the hypothesis that CPG activity contributes to the neural control of rhythmic arm movement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".