Bridging the Gap Between Theory and Practice in Neurofeedback Training for Attention
Bibliographic record
Abstract
ABSTRACT Modernizing classroom pedagogical practice requires openness to revisiting previously held assumptions and theories about what constitutes authentic teaching/learning cycles. The ever‐growing gap between the number of stimuli that students are exposed to and their available attentional resources indicates that sustained attention may have increasing value transitioning into 21st‐century learning environments requiring self‐reflection, collaborative learning, and self‐directed decision‐making. Neurofeedback has shown promise in laboratory and clinical settings as a tool for building sustained attention, but little in situ research has been completed in bringing the technology into the school for empirical testing. Furthermore, attentional research lacks connections between neural network modeling and observable neuromarkers for attention. This article aims to bridge these distinct concepts to support an understanding of the potential impacts of neurofeedback training (NT) and to provide a framework for other Mind, Brain, and Education researchers planning in situ NT studies.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".