Attention and spatial navigation in everyday life: Physical activity is associated with subjective aspects of cognitive function
Bibliographic record
Abstract
Efforts to understand the effects of physical activity on cognitive health have long relied on employing objective measures that assess the efficacy of the mechanics of cognition. However, this perspective overlooks complementary dimensions of cognitive functioning, namely one's subjective appraisal of the efficacy of their cognitive mechanics. In a set of four investigations (N = 2965), we sought to discern whether physical activity (PA), and other health and demographic factors, contribute to subjective experiences of cognitive mechanics (SCF) and to map for future investigations domains of function that are sensitive to health factors. We employed linear multiple regression analyses to examine survey data collected online from four large samples of young adults who responded to measures of health behaviours and SCF. PA contributed to subjective experiences of attentional control and spatial navigation but not memory, executive function, or general cognitive functioning. Further, sleep, diet, and stress were each consistently associated with selective measures of subjective experiences of cognition. Taken together, these studies indicate the importance of PA, as well as additional health behaviours, as significant contributors to SCF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".