Bibliographic record
Abstract
Abstract Postural complexity may shape how the nervous system processes plantar cutaneous input. We tested whether somatosensory evoked potentials (SEPs) elicited by foot-sole stimulation scale with balance demands, hypothesizing larger responses for more complex tasks. Thirty-one healthy adults performed standing, straight-step, and diagonal-step conditions while receiving brief electrical stimulation to the stance foot sole; SEPs (P50, N90, peak-to-peak) were analyzed at Cz using pooled and order-specific approaches. In the pooled analysis, peak-to-peak SEP amplitude was greater for both stepping conditions than standing (Standing vs. Straight, P = 0.041; Standing vs. Diagonal, P = 0.026). Order-specific analysis showed an early amplification: the first SEP (0.5 s after the warning cue) was larger for diagonal than straight stepping (peak-to-peak, P = 0.009; N90, P = 0.027). Source localization at N90 revealed greater activation during stepping than standing in paracentral gyrus & sulcus, inferior parietal angular gyrus, and superior parietal gyrus, consistent with enhanced sensorimotor processing under higher postural demands. Moreover, right paracentral gyrus & sulcus activity was higher for diagonal vs. straight stepping for the first and fourth SEPs. Together, these findings indicate that increasing balance demands up-weight plantar afferent processing and recruit contralateral sensorimotor/parietal regions, particularly early in preparation, supporting the view that cortical sensory gain is tuned to postural complexity.
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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".