Training-induced adaptive plasticity in human somatosensory reflex pathways
Bibliographic record
Abstract
This paper reviews evidence supporting adaptive plasticity in muscle and cutaneous afferent reflex pathways induced by training and rehabilitative interventions. The perspective is advanced that the behavioral and functional relevance of any intervention and the reflex pathway under study should be considered when evaluating both adaptation and transfer. A cornerstone of this concept can be found in acute task-dependent reflex modulation. Because the nervous system allows the expression of a given reflex according to the motor task, an attempt to evaluate the training adaptation should also be evoked under the same conditions as training bearing in mind the functional role of the pathway under study. Within this framework, considerable evidence supports extensive adaptive plasticity in human muscle afferent pathways in the form of operant conditioning, strength training, skill training, and locomotor training or retraining. Directly comparable evidence for chronic adaptation in cutaneous reflex pathways is lacking. However, activity-dependent plasticity in cutaneous pathways is documented particularly in approaches to neurological rehabilitation. Overall, the adaptive range for human muscle afferent reflexes appears bidirectional (that is, increased or reduced amplitudes) and on the order of 25-50%. The adaptive range for cutaneous pathways is currently uncertain.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".