Temporal Evolution of “Automatic Gain-Scaling”
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it