The effects of training time, sensory loss and pain on human motor learning
Bibliographic record
Abstract
This study determined, in humans, the effects of (i) the number of within-session task repetitions (72 or 144 over a period of 15 or 30 min, respectively) on the time course of motor learning in a long-term (seven consecutive daily motor-training sessions and a 1-week post-follow-up) novel tongue-task training regime and (ii) somatosensory manipulations (capsaicin-induced intra-oral pain or lidocaine-induced sensory loss of the tongue tip) on motor learning in a short-term (single motor-training session consisting of 72 within-session task repetitions over a period of 15 min) novel tongue-task training regime. Novel tongue-task training consisted of tracking a moving target box by generating a pre-set amount of tongue-protrusion force onto a force lever. Analysis of motor behaviour revealed (i) a higher within-session gain for the 30-min tongue-task training regime, but this difference did not differentially affect the time course of the overall motor performance or additional motor performance variables between the 15- and 30-min tongue-task training regimes in subsequent training sessions. (ii) somatosensory manipulations of the tongue tip reduced the gains in overall motor performance, and this reduced motor performance was mainly characterized by exaggerated undershoot errors and delayed reaction times for the lidocaine tongue-task training regime and exaggerated overshoot and undershoot errors as well as delayed reaction times for the capsaicin tongue-task training regime. It is concluded that extended within-session task repetitions do not facilitate additional long-term gains in overall motor performance and intra-oral sensory loss or pain hinders motor learning.
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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) | 0.000 | 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".