Games as neurofeedback training for children with FASD
Bibliographic record
Abstract
Biofeedback games help people maintain specific mental or physical states and are useful to help children with cognitive impairments learn to self-regulate their brain function. However, biofeedback games are expensive and difficult to create and are not sufficiently appealing to hold a child's interest over the long term needed for effective biofeedback training. We present a system that turns off-the-shelf computer games into biofeedback games. Our approach uses texture-based graphical overlays that vary in their obfuscation of underlying screen elements based on the sensed physiological state of the child. The textures can be visually customized so that they appear to be integrated with the underlying game. Through a 12-week deployment, with 16 children with Fetal Alcohol Spectrum Disorder, we show that our solution can hold a child's interest over a long term, and balances the competing needs of maintaining the fun of playing, while providing effective biofeedback training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".