Monitoring Cognitive Performance with a Serious Game
Bibliographic record
Abstract
This case study involves: the design and evaluation of serious games; the use of longitudinal research and remote testing in an international setting. Current methods for cognitive assessment tend to be inconvenient, costly and infrequently performed. This is unfortunate because cognitive assessment is an important tool. In the young it can detect atypical development, and in older people it can detect cognitive decline. For both young and old, cognitive assessments can identify problems and trigger interventions for reducing harms (e.g., adverse reactions to drugs) or providing treatment. Serious games for cognitive assessment can potentially be self-administered and played on an on-going basis so as to track cognitive status over time, something that is not practical with current methods. Inspired by this opportunity the BrainTagger team has developed a suite of cognitive assessment games. Studies are being carried out to assess the validity of these games for measuring the cognitive functions that they target, but those studies don't address the issue of whether people will be willing to play the game repeatedly, without supervision, over an extended period of time. Thus we carried out a longitudinal study with BrainTagger. We report on the logistical challenges of running this study with an international team located in Canada and Japan during the COVID19 pandemic. We also report on how the perceived “fun” of games changed over time. Our games were all versions of Whack-a-mole games, with each game requiring a different cognitive function to distinguish between targets (moles to hit) and distractors (moles to avoid). While the basic Whack-a-mole game is fun to play, having to play the same games again and again over a larger time period appeared to be more challenging than anticipated and motivation and acceptance seemed to gradually decrease over the course of the study. We conclude that addition of gamification features, such as leaderboards and in-game rewards, are needed to sustain enjoyment of our BrainTagger games and likely other games as well.
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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 | 0.000 |
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".