Reflective and Non-conscious Responses to Exercise Images
Bibliographic record
Abstract
Images portraying exercise are commonly used to promote exercise behavior and to measure automatic associations of exercise (e.g., via implicit association tests). The effectiveness of these promotion efforts and the validity of measurement techniques partially rely on the untested assumption that the images being used are perceived by the general public as portrayals of exercise that is pleasant and motivating. The aim of this study was to investigate how content of images impacted people’s automatic and reflective evaluations of exercise images. Participants (N = 90) completed a response time categorization task (similar to the implicit association test) to capture how automatically people perceived each image as relevant to Exercise or Not exercise. Participants also self-reported their evaluations of the images using visual analogue scales with the anchors: Exercise/Not exercise, Does not motivate me to exercise/Motivates me to exercise, Pleasant/Unpleasant, and Energizing/Deactivating. People tended to more strongly automatically associate images with exercise if the images were of an outdoor setting, presented sport (as opposed to active labor or gym-based) activities, and included young (as opposed to middle-aged) adults. People tended to reflectively find images of young adults more motivating and relevant to exercise than images of older adults. The content of exercise images is an often overlooked source of systematic variability that may impact measurement validity and intervention effectiveness.
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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 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".