Differential Changes in Early Somatosensory Evoked Potentials between the Dominant and Non-Dominant Hand, Following a Novel Motor Tracing Task
Bibliographic record
Abstract
During training in a novel dynamic environment, the non-dominant upper limb favors feedback control, whereas the dominant limb favors feedforward mechanisms. Early somatosensory evoked potentials (SEPs) offer a means to explore differences in cortical regions involved in sensorimotor integration (SMI). This study sought to compare differences in SMI between the right (Dom) and left (Non-Dom) hand in healthy right-handed participants. SEPs were recorded in response to median nerve stimulation, at baseline and post, a motor skill acquisition-tracing task. One group (n = 12) trained with their Dom hand and the other group (n = 12), with their Non-Dom hand. The Non-Dom hand was significantly more accurate at baseline (p < 0.0001) and both groups improved with time (p < 0.0001), for task accuracy, with no significant interaction effect between groups for both post-acquisition and retention. There were significant group interactions for the N24 (p < 0.001) and the N30 (p < 0.0001) SEP peaks. Post motor acquisition, the Dom hand had a 28.9% decrease in the N24 and a 23.8% increase in the N30, with opposite directional changes for the Non-Dom hand; 22.04% increase in N24 and 24% decrease in the N30. These SEP changes reveal differences in early SMI between Dom and Non-Dom hands in response to motor acquisition, providing objective, temporally sensitive measures of differences in neural mechanisms between the limbs.
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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) | 0.002 | 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".