Machine learning and serious games: opportunities and requirements for detection of mild cognitive impairment
Bibliographic record
Abstract
This perspective paper presents a simple serious game on a mobile platform (Smartphone game). The game has the integrated capability to track a person’s play by storing player metadata on start time, end time, and moves within the game. These data can be analyzed to infer cognitive processes of strategy learning, retention, and recall over a brief period of time for potential future applications in pre-symptomatic assessment of mild cognitive impairment (MCI). Through machine learning (ML), the data are demonstrated to be of utility in providing a “cognitive fingerprint” of play. The ML methods used to classify play use synthetic data generated by robots (bots), ranging from bots playing perfectly to bots playing with various degrees of impairment. The findings include guidance on the volume of data required, as well as the features deemed effective for ML classification of various degrees of bot impairment. The work illustrates several significant considerations when applying ML to simple serious games and the data they can generate.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 | 0.000 |
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 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".