THE FLYNN EFFECT IN SHORT-TERM COGNITIVE DECLINES OF AMERICANS AGED 65 YEARS AND OLDER: SMARTER AND MAYBE SLOWER
Bibliographic record
Abstract
Abstract To contribute to our understanding of cohort differences and the Flynn Effect in cognitive declines, this study aims to: 1) describe and compare cognitive decline trends of two nationally representative American older cohorts; 2) investigate significant determinants of cognitive declines and the cohort differences. The analysis used data from the National Health and Aging Trends Study (NHATS, 2011-2019), including one nationally representative cohort of older Americans in 2011 and another from 2015. We used mixed-effect models adjusted for cohort, wave, baseline age, sex, education, race, familiarity, and follow-up years, as well as survey designs, to describe and compare the intercepts and slopes in cognitive functions of the two NHATS cohorts. We included Cohort 1 (N=7,325) respondents (2011-2015), and Cohort 2 (N=7,330) respondents (2015-2019). Compared to Cohort 1, Cohort 2 has a significantly higher intercept and a slower decline for episodic memory, and a significantly higher intercept but a significantly faster decline for global cognition, orientation function, and executive function. Consistently, older age, poorer educational attainment, and minority races/ethnicity are associated with worse cognitive performances. Our results provide a comprehensive image of cohort declines for Americans aged 65 years and older. Our findings are consistent with the Flynn Effect in that the general levels of cognition of later cohorts improved. Furthermore, we found support for the Flynn Effect in a short term. We also found significant effects of older age, poorer educational attainment, and minority race/ethnicity on cognitive function.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".