Age differences in the association of physical activity, sociocognitive engagement, and TV viewing on face memory.
Bibliographic record
Abstract
OBJECTIVE: Physical and sociocognitive lifestyle activities promote aspects of cognitive function in older adults. Very little is known about the relation between these lifestyle activities and cognitive function in young adults. One aspect of cognitive function that is critical for everyday function is episodic memory. The present study examined the relationship between lifestyle activities and episodic memory in younger and older adults. METHOD: Participants were 62 younger (mean age = 24 years) and older adults (mean age = 74 years). The augmented Victoria Longitudinal Study Activities Questionnaire was used to quantify level of engagement in physical activity, sociocognitive activity, and TV viewing. Episodic memory was assessed using the old-new face recognition paradigm in which memory for younger and older faces was tested. RESULTS: Compared to younger adults, older adults reported being less physically and sociocognitively active while engaging in more passive behaviors such as TV viewing. A positive association was observed between physical activity and episodic memory for young adults but not for older adults. Interestingly, TV viewing was negatively associated with episodic memory in older adults but not younger adults. No relationship was found between sociocognitive activity and episodic memory for either younger or older adults. Although the own-age effect was observed for older adults, face age did not interact with lifestyle activities. CONCLUSION: The positive cognitive benefits of physical activity extend to younger adults; however, the interplay between physical activity and cognition may differ across the life span. Furthermore, TV viewing may be particularly detrimental to cognitive performance later in life.
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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".