Task-relevant selective modulation of somatosensory afferent paths from the lower limb
Bibliographic record
Abstract
Leg movement attenuates initial somatosensory evoked potentials (SEPS) from both cutaneous and muscle afferent origin. To date, as different sensory inputs become relevant for task performance, selective facilitation from such movement-related gating influences has not been shown. We hypothesized that initial SEP amplitudes from cutaneous (sural nerve, SN) and muscle afferent (tibial nerve, TN) sources are dependent on the relevance of the specific afferent information to task performance. SEPs were obtained at rest and during three movement conditions. In each movement condition, the left foot was passively moved episodically and additional cutaneous 'codes' of sensory information were applied to the dorsum of the left foot. Subjects were instructed to: simply relax (passive), or to make a response following the cessation of movement, dependent either on the cutaneous code (cutaneous task), or the passive movement trajectory of the left foot (position task). Passive movement, with no required subsequent response, attenuated initial TN and SN SEPs to approximately 40% of that at rest (p < 0.05). Versus passive movement, when cutaneous inputs provided the relevant cue for the task, mean SN SEPs significantly increased (p < 0.05), and when the proprioceptive inputs provided the relevant cue for the task, mean TN SEPs significantly increased (p < 0.05). We conclude that specific relevancy of sensory information selectively facilitates somatosensory paths from movement-related attenuation.
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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 | 0.000 |
| 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) | 0.001 | 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".