Cohort differences in cognitive aging and terminal decline in the Seattle Longitudinal Study.
Bibliographic record
Abstract
Life span researchers have long been interested in how and why fundamental aspects of human ontogeny differ between cohorts of people who have lived through different historical epochs. When examined at the same age, later born cohorts are often cognitively and physically fitter than earlier born cohorts. Less is known, however, about cohort differences in the rate of cognitive aging and if, at the very end of life, pervasive mortality-related processes overshadow and minimize cohort differences. We used data on 5 primary mental abilities from the Seattle Longitudinal Study (Schaie, 2005) to compare both age-related and mortality-related changes between earlier born cohorts (1886-1913) and later born cohorts (1914-1948). Our models covary for several individual and cohort differences in central indicators of life expectancy, education, health, and gender. Age-related growth models corroborate and extend earlier findings by documenting level differences at age 70 of up to 0.50 SD and less steep rates of cognitive aging on all abilities between 50 and 80 years of age favoring the later born cohort. In contrast, mortality-related models provide limited support for positive cohort differences. The later born cohort showed steeper mortality-related declines. We discuss possible reasons why often reported positive secular trends in age-related processes may not generalize to the vulnerable segment of the population that is close to death and suggest routes for further inquiry.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".