The effect of age on executive functions in adults is not sex specific
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have shown a decrease in executive functions (EF) associated with aging. However, few investigations examined whether this decrease is similar between sexes throughout adulthood. The present study investigated if age-related decline in EF differs between men and women from early to late adulthood. METHODS: A total of 302 participants (181 women) aged between 18 and 78 years old completed four computer-based cognitive tasks at home: an arrow-based Flanker task, a letter-based Visual search task, the Trail Making Test, and the Corsi task. These tasks measured inhibition, attention, cognitive flexibility, and working memory, respectively. To investigate the potential effects of age, sex, and their interaction on specific EF and a global EF score, we divided the sample population into five age groups (i.e., 18-30, 31-44, 45-54, 55-64, 65-78) and conducted analyses of covariance (MANCOVA and ANCOVA) with education and pointing device as control variables. RESULTS: Sex did not significantly affect EF performance across age groups. However, in every task, participants from the three youngest groups (< 55 y/o) outperformed the ones from the two oldest. Results from the global score also suggest that an EF decrease is distinctly noticeable from 55 years old onward. CONCLUSION: Our results suggest that age-related decline in EF, including inhibition, attention, cognitive flexibility, and working memory, becomes apparent around the age of 55 and does not differ between sexes at any age. This study provides additional data regarding the effects of age and sex on EF across adulthood, filling a significant gap in the existing literature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".