Visualizing “Cognitive Fingerprints” from Simple Mobile Game Play
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Serious Games and associated data analytics of-fer the potential of a complementary means of detecting early signs of mild cognitive impairment (MCI), which is often a pre-cursor to more serious forms of dementias. As with all diseases and illnesses, the ability to mitigate the impact of the illness is directly correlated to early detection and intervention. In this work, a representative serious game is used to capture a “cogni-tive fingerprint” of a person’s play, which is then used to ana-lyze and visualize play. The long-term objective of the research is to demonstrate that data collected from serious games may be used to detect cognitive difficulties that may be pre-sympto-matic, and outside the scope of normal age related cognitive de-cline. The present work assesses the viability of the platform for this purpose and opportunities in data visualization, but does not include clinical testing for MCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
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 it