The effect of local vs remote experimental pain on motor learning and sensorimotor integration using a complex typing task
Bibliographic record
Abstract
Recent work demonstrated that capsaicin-induced acute pain improved motor learning performance; however, baseline accuracy was very high, making it impossible to discern the impact of acute pain on motor learning and retention. In addition, the effects of the spatial location of capsaicin application were not explored. Two experiments were conducted to determine the interactive effects of acute pain vs control (experiment 1) and local vs remote acute pain (experiment 2) on motor learning and sensorimotor processing. For both experiments, somatosensory evoked potential (SEP) amplitudes and motor learning acquisition and retention (accuracy and response time) data were collected at baseline, after application, and after motor learning. Experiment 1: N11 (P < 0.05), N13 (P < 0.05), and N30 (P < 0.05) SEP peak amplitudes increased after motor learning in both groups, whereas the N20 SEP peak increased in the control group (P < 0.05). At baseline, the intervention group outperformed the control group in accuracy (P < 0.001). Response time improved after motor learning (P < 0.001) and at retention (P < 0.001). Experiment 2: The P25 SEP peak decreased in the local group after application of capsaicin cream (P < 0.01), whereas the N30 SEP peaks increased after motor learning in both groups (P < 0.05). Accuracy improved in the local group at retention (P < 0.005), and response time improved after motor learning (P < 0.005) and at retention (P < 0.001). This study suggests that acute pain may increase focal attention to the body part used in motor learning, contributing to our understanding of how the location of pain impacts somatosensory processing and the associated motor learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".