COHORT SHIFTS IN POPULATION COGNITIVE AGING
Bibliographic record
Abstract
Abstract Declines in cognitive functioning with increased age, on average and individually, is well documented and demonstrated to be related to genetics and a variety of life course risk factors, many of which are modifiable. Related to population cognitive aging is the phenomenon of the Flynn effect, the finding of increasing cognitive test scores across successive cohorts of young adults (e.g., Flynn 1987). This cohort effect has been repeatedly observed, in a wide variety of contexts, for over 30 years, with evidence that it has been occurring for at least a century. Our research looks at the interaction of the population cognitive aging and the Flynn effect. Using data from the English Longitudinal Survey of Ageing (ELSA) we show that, indeed, later born cohorts show significant (and meaningful) differences from earlier born cohorts. Using nonlinear Bayesian modeling we find that, on certain measures, later born cohorts have higher initial ability. This higher ability leads to a persistent advantage for later born cohorts, even as they experience (or will experience) cognitive decline. Additionally, we find that the advantage for later born cohorts is not present for every measure. Later born cohorts show an advantage for verbal fluency and episodic memory, however there is no cohort advantage on orientation scores. The lack of measurable differences on orientation is likely due to ceiling effects on orientation, which suggests that any substantial decline on that measure is indicative of pathology. We discuss the potential factors underlying both population cognitive aging and recent birth cohort trends.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".