When I’m 64: Age-Related Variability in Over 40,000 Online Cognitive Test Takers
Bibliographic record
Abstract
OBJECTIVES: Age-related differences in cognition are typically assessed by comparing groups of older to younger participants, but little is known about the continuous trajectory of cognitive changes across age, or when a shift to older adulthood occurs. We examined the pattern of mean age differences and variability on episodic memory and executive function measures over the adult life span, in a more fine-grained way than past group or life-span comparisons. METHOD: We used a sample of over 40,000 people aged 18-90 who completed psychometrically validated online tests measuring episodic memory and executive functions (the Cogniciti Brain Health Assessment). RESULTS: Cognitive performance declined gradually over adulthood, and rapidly later in life on spatial working memory, processing speed, facilitation (but not interference), associative recognition, and set shifting. Both polynomial and segmented regression fit the data well, indicating a nonlinear pattern. Segmented regression revealed a shift from gradual to rapid decline that occurred in the early 60s. Variability between people (interindividual variability or diversity) and variability within a person across tasks (intraindividual variability or dispersion) also increased gradually until the 60s, and rapidly after. Confirmatory factor analysis revealed a single general factor (of variance shared between tasks) offered a good fit for performance across tasks. DISCUSSION: Life-span cognitive performance shows a nonlinear pattern, with gradual decline over early and mid-adulthood, followed by a transition in the 60s to notably accelerated, but more variable, decline. Some people show less decline than others, and some cognitive abilities show less within-person decline than others.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".