Serious Games and ML for Detecting MCI
Bibliographic record
Abstract
Our work has focused on detecting Mild Cognitive Impairment (MCI) by developing Serious Games (SG) on mobile devices, distinct from games marketed as `brain training' which claim to maintain mental acuity. One game, WarCAT, captures players' moves during the game to infer processes of strategy recognition, learning, and memory. The purpose of our game is to use the generated game-play data combined with machine learning (ML) to help detect MCI. MCI is difficult to detect for several reasons. Firstly, it is a mild impairment and as such difficult to detect in its early stages, Secondly, it is a subtle impairment for which the brain attempts compensation; as a consequence, it is considered rare in light of normal cognitive decline and the brain's ability to mask its manifestation. The problem of early MCI detection is further compounded as people have various cognitive acumen which again can lead to false positives which would exacerbate the rare diagnosis still further. To evaluate the conjecture, ML methods are used to generate synthetic data to plausibly emulate a large population of players. Reinforcement Learning (RL) is used to train bots as RL most closely emulates the way humans learn. Considerable trial and error (training) is required, therefore RL bots were developed that process millions of gameplay training patterns and achieve results comparable to the best human performance. This baseline allows us to create bots to emulate individuals at various stages of learning, or conversely, various levels of cognitive decline. The paper demonstrates the ML work to both generate data and subsequently classify different levels of play. This development stage is necessary as part of the larger objective to create SGs that detect MCI.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".