Temporal Evolution of “Automatic Gain-Scaling”
Bibliographic record
Abstract
The earliest neural response to a mechanical perturbation, the short-latency stretch response (R1: 20-45 ms), is known to exhibit "automatic gain-scaling" whereby its magnitude is proportional to preperturbation muscle activity. Because gain-scaling likely reflects an intrinsic property of the motoneuron pool (via the size-recruitment principle), counteracting this property poses a fundamental challenge for the nervous system, which must ultimately counter the absolute change in load regardless of the initial muscle activity (i.e., show no gain-scaling). Here we explore the temporal evolution of gain-scaling in a simple behavioral task where subjects stabilize their arm against different background loads and randomly occurring torque perturbations. We quantified gain-scaling in four elbow muscles (brachioradialis, biceps long, triceps lateral, triceps long) over the entire sequence of muscle activity following perturbation onset-the short-latency response, long-latency response (R2: 50-75 ms; R3: 75-105 ms), early voluntary corrections (120-180 ms), and steady-state activity (750-1250 ms). In agreement with previous observations, we found that the short-latency response demonstrated substantial gain-scaling with a threefold increase in background load resulting in an approximately twofold increase in muscle activity for the same perturbation. Following the short-latency response, we found a rapid decrease in gain-scaling starting in the long-latency epoch ( approximately 75-ms postperturbation) such that no significant gain-scaling was observed for the early voluntary corrections or steady-state activity. The rapid decrease in gain-scaling supports our recent suggestion that long-latency responses and voluntary control are inherently linked as part of an evolving sensorimotor control process through similar neural circuitry.
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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".