Physically active individuals look for more: An eye‐tracking study of attentional bias
Bibliographic record
Abstract
Attentional capture by exercise-related stimuli is important for the regulation of physical activity. Attentional processing underlying this capture has been investigated with indirect behavioral measures based on reaction times. To investigate more direct measures of visual spatial attention toward physical activity (vs. inactivity) stimuli, we used eye-tracking and a visual dot probe task in 77 young adults with various level of physical activity. Reaction times to detect a dot appearing in the area previously occupied by a physical activity (vs. inactivity) stimulus were an indirect measure of attentional bias. The first picture gaze and viewing time were more direct measures of attentional orienting and attentional engagement, respectively. Pupil dilation was an indicator of arousal. Reaction times revealed a two-way interaction between the location of the dot and participants' usual level of physical activity. Only participants with a high level of physical activity more quickly detected a dot when it appeared in the area previously occupied by a physical activity stimulus. Eye-tracking results showed greater odds of first gazing at physical activity stimuli and for a longer time, and a greater decrease in pupil size when viewing physical activity stimuli when usual level of physical activity was moderate or high, but not low. The variance explained in the outcomes ranged from 13.9% (pupil dilation) to 40% (reaction times). Overall, as hypothesized, compared to less physically active participants, participants who were more physically active demonstrated indirect (reaction times) and direct (first gaze, viewing time) evidence of a more pronounced attentional bias toward physical activity. Physical activity stimuli biased attention, with a pronounced effect when the level of physical activity was higher. These findings suggest that physical activity stimuli are relevant to the current concerns of moderately and highly active individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".