A Machine Learning–Driven Framework for Enhancing Cognitive Function Using tDCS and Brain Gym Interventions
Bibliographic record
Abstract
Structured cognitive training produced the strongest effects within the short-term, doing better than the other two interventions, Brain Gym + tDCS and Brain Gym-only methods. Cognitive training intervention participants exhibited significant within-group improvement between pre and post testing in all of the targeted cognitive domains (global cognition, processing speed, and executive function), with results large enough to be considered practically significant ($p<.05$) and the group differences were large enough to be considered statistically significant ($p$$=0.5542$). These results indicate that the derived features and the composite outcome metric capture a meaningful shift in cognitive performance. These results highlight the potential of cognitive training as a core component of transient boosts of cognitive performance and confirm the value of transparent, machine learning-supported assessment procedures in complex neurocognitive interventions. However, sample size and synthetic augmentation issues point to the necessity of future research conducted with larger randomised groups and in modified approaches in response to the intervention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".