Do Cognitive Speed and Executive Functions Follow the Expected Age Trajectory for Fluid Intelligence?
Bibliographic record
Abstract
After reviewing cognitive safety in healthcare, effectiveness of BrainTagger cognitive assessment games in tracking age-related cognitive changes is evaluated. One hundred eighty-three participants, aged 4 to 92, each played up to four games designed to assess cognitive speed, response inhibition, cognitive control, and working memory. Participants were recruited during two public events in Toronto: AGE-TECH Innovation Week (October 24–27, 2023) and the National Home Show (March 8–17, 2024). Piecewise linear regression analyzed game performance across three life stages: development (0–25 years); peak performance (26–50 years); age-related decline (51+ years). All four games demonstrated expected trends, with performance increasing in early years, stabilizing during middle age, and declining after age 50. BrainTagger are shown to track age-related cognitive decline. Future applications in clinical settings may enhance early identification of cognitive harm caused by medical interventions or neglect, contributing to improved cognitive safety in healthcare environments.
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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 | 0.001 | 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) | 0.000 | 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".