SENSE OF PURPOSE IN LIFE PREDICTS HIGHER ENGAGEMENT IN COGNITIVELY STIMULATING ACTIVITIES
Bibliographic record
Abstract
Abstract Several studies have noted associations between having a strong sense of purpose or direction in life and better cognitive health outcomes including better memory, executive functioning, and reduced dementia risk. However, limited research has considered why these associations occur. Engaging in cognitively stimulating activities such as reading is believed to promote cognitive reserve—the brain’s ability to compensate for damage and maintain function even in the face of aging or neurological disease. The present study aimed to test this potential mechanism by examining whether a stronger sense of purpose predicts higher engagement in cognitively stimulating activities, and whether these associations differed based on age and retirement status. Data were collected from an online survey of 787 American adults on Prolific, with over-recruitment of older adults (Mage=50.31, SD=16.34, Range=19-85, 49.30% women, 71.08% White). Multiple regression tested whether those with higher in sense of purpose reported more frequent engagement in 15 activities from the Cognitive Leisure Activity Scale (e.g., reading a book/newspaper, doing puzzles, attending a club or group activity). Having a stronger sense of purpose in life was associated with more frequent participation in cognitive leisure activities (β=0.25, 95% CI [0.19, 0.32], p<.001) adjusting for age, gender, race, income, and retirement status. Additional models found no significant interactions with age (β= 0.00, 95% CI [-0.00, 0.01], p=.156) and retirement status (β=0.09, 95% CI [-0.09, 0.24], p=.236). These findings suggest that a strong sense of purpose may encourage cognitive activity, regardless of age or retirement, potentially supporting cognitive health.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".