Modelling the Effects of Demographics and Lifestyle on Cognitive Performance
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
Human cognitive performance is ultimately the result of many factors. Previous inquiries note the contributions of demographic and lifestyle cognitive performance. I used a series of structural equation models and dimensionality reduction methods to identify how demographic and lifestyle measures simultaneously contribute to cognitive performance: a theory-driven model using combined measures of cognitive performance and latent variable structure; and a data- driven model using principal components analysis. Participants (N = 1141, Mage = 23.13 years) completed a battery of tasks and questionnaires measuring cognitive performance and collecting demographic and lifestyle measures. Overall, both models provided evidence that the inclusion of lifestyle measures over and above demographic measures accounted for and predicted cognitive performance. Further, the two models give rise to complementary but distinct insights into the basic components of cognitive performance. This work provides a methodology and evidence for accounting for difference in cognitive performance with demographic and lifestyle measures.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it