Exploring and Evaluating Sound for Helping Children Self-Regulate with a Brain-Computer Application
Bibliographic record
Abstract
Children in North America are more likely to suffer from attentional challenges than any other mental health issue. Studies suggest that neurofeedback treatments may be useful for helping these children learn to self-regulate. Applying neurofeedback treatments in real-world, school settings poses a challenge, however, as these environments are often noisy and filled with distractions. The addition of ambient audio to neurofeedback systems may help reduce these disruptions. Further, research suggests that certain auditory treatments, such as binaural beats and white noise, may improve children's focus and aid memory recall. In the following paper we present the theories supporting this idea as well as a mixed methods framework for evaluating whether sound can help children focus while learning to self-regulate using a neurofeedback system. Specifically, we wish to investigate whether these treatments may help children (1) achieve an attentive state sooner and (2) maintain an attentive state for longer, when compared to the same system without sound.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".