Bibliographic record
Abstract
The current study aimed to understand how aging influences cognitive and mentalizing processes, and to explore whether physical activity protects against cognitive and social function decline in older adults. A total of 104 participants (44 Older adults, 60 younger Control) were recruited to complete the Go/No-go task and the Picture sequencing task. Their general cognitive function was measured using the Montreal Cognitive Assessment (MoCA), and the frequency of physical activity was assessed through a self-report question. The current study demonstrated that, compared to Control, Older participants performed relatively well in maintaining attention and inhibiting irrelevant responses, as evidenced by comparable response times on Go trials and accuracy on both Go and No-go trials (all ps > 0.1). However, Older participants showed significant impairments in reconstructing sequences of both mechanical and social events, particularly those requiring inferring others’ false belief, even after controlling MoCA scores (both accuracy and response times, all ps < 0.05). Importantly, the frequency of physical activity was found to buffer declines in processing speed in sequencing mechanical and social stories in Older, especially stories involved others’ false belief (ps < 0.01). Overall, the current findings suggest that mentalizing abilities decrease independently of general cognitive decline. Furthermore, physical activity may serve as a potential protective factor in mitigating age-related cognitive impairments.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.445 | 0.284 |
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".