Implicit learning and generalization of stretch response modulation in humans
Bibliographic record
Abstract
Adaptation of neural responses to repeated muscle stretching likely represents implicit learning to minimize muscle resistance to perturbations. To test this hypothesis, the forearm was placed on a horizontal manipulandum. Elbow flexors or extensors compensated an external load and were stretched by 20° or 70° rotations. Participants were instructed not to intervene by intentionally modifying the muscle resistance elicited by stretching. In addition to phasic stretch reflexes (SRs), muscle stretching was associated with inhibitory periods (IPs) in the ongoing muscle activity starting at minimal latencies of ∼35 ms. The SR amplitude decreased dramatically across 5-12 trials and was not restored after a resting period of 3-5 min, despite the increase in stretch amplitude from 20° to 70°, but IPs remained present. When SRs were suppressed, stretching of originally nonstretched, antagonist muscles initiated after the rest period showed immediate SR suppression while IPs remained present in the first and subsequent trials. Adaptation to muscle stretching thus includes features characteristic of implicit learning such as memory consolidation and generalization. Adaptation may be achieved by central shifts in the threshold positions at which muscles begin to be activated. Shifts are thought to be prepared in advance and triggered with stretch onset. Threshold position resetting provides a comprehensive explanation of the results in the broader context of the control of posture, movement, and motor learning in the healthy and damaged nervous system.
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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 | 0.004 |
| 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 | 0.001 |
| 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".