Large-Scale Neural Dynamics as a Predictor of Stimulus Effects Variability
Bibliographic record
Abstract
was applied for the initial two rounds of four (tRNS-ON; tRNS-OFF).EEG data were collected before the first, between the second and third, and after the fourth round.The ratio of overwhelmed enemies (O) to the player's defeats (D) was calculated (O/D).The participants' shooting skills and cognitive abilities were evaluated before, after, and one week after the training (T0, T1, T2).Results.The Active-tRNS group showed significantly higher O/D performance compared to the Sham-tRNS group, particularly during tRNS-OFF rounds.Furthermore, at T2, the Active-tRNS group demonstrated superior performance in a long-range shooting task relative to the Sham-tRNS group.Both groups showed improved cognitive abilities at T1 and T2.In the stimulation site, a desynchronization of beta and gamma waves was observed after the second round, and a global enhancement of the delta band was noted after the fourth round in the Active-tRNS group.Conclusions.tRNS of the visuomotor network enhances the learning curve of VR-FPS training, with long lasting after-effects.Furthermore, tRNS induces changes in EEG patterns associated with motor learning and the reward system.These findings have potential applications for both training and treatment purposes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".