The Flynn effect and cognitive decline among americans aged 65 years and older.
Bibliographic record
Abstract
To contribute to our understanding of cohort differences and the Flynn effect in the cognitive decline among older Americans, this study aims to compare rates of cognitive decline between two birth cohorts within a study of older Americans and to examine the importance of medical and demographic confounders. Analyses used data from the National Health and Aging Trends Study (2011-2019), which recruited older Americans in 2011 and again in 2015 who were then followed for 5 years. We employed mixed-effect models to examine the linear and quadratic main and interaction effects of year of birth while adjusting for covariates such as annual round, sex/gender, education, race/ethnicity, heart disease, hypertension, diabetes, test unfamiliarity, and survey design. We analyzed data from 11,167 participants: 7,325 from 2011 to 2015 and 3,842 from 2015 to 2019. The cohort recruited in 2015 was born, on average, 5.33 years later than that recruited in 2011 and had higher functioning than the one recruited in 2011 across all observed cognitive domains that persisted after adjusting for covariates. In multivariable-adjusted analyses, a 1-year increase in year of birth was associated with increased episodic memory (β = 0.045, SE = 0.001, p < .001), orientation (β = 0.034, SE = 0.001, p < .001), and executive function (β = 0.036, SE = 0.001, p < .001). Participants born 1 year later had slower rates of decline in episodic memory (β = 0.004, SE = 0.000, p < .001), orientation (β = 0.003, SE = 0.000, p < .001), and executive function (β = 0.001, SE = 0.000, p = .002). Additionally, sex/gender modified this relationship for episodic memory (-0.007, SE = 0.002, p < .001), orientation (-0.005, SE = 0.002, p = .008), and executive function (-0.008, SE = 0.002, p < .001). These results demonstrate the persistence of the Flynn effect in old age across cognitive domains and identified a deceleration in the rate of cognitive decline across cognitive domains. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 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".