How malleable are attentional biases in women with body dissatisfaction? Priming effects and their impact on attention to images of women’s bodies
Bibliographic record
Abstract
Understanding attentional biases associated with body dissatisfaction can aid in devising and refining programs to reduce body dissatisfaction. This study compared attention to images of women’s bodies before and after a body satisfaction or body dissatisfaction priming task. Attention was assessed using eye-gaze tracking, by measuring participants’ fixations to images of “thin” models, “fat” models, and images of average women over an 8-s presentation. Women with high ( n = 65) and low ( n = 43) levels of trait body dissatisfaction, as measured by the Body Shape Questionnaire, were randomly assigned to a body satisfaction or body dissatisfaction priming task. Results indicated that both priming tasks were effective at modifying participants’ state body satisfaction. Women with high body dissatisfaction exhibited an attentional bias to thin and fat model images prior to the priming procedure, replicating previous findings. Contrary to predictions, body dissatisfaction priming increased attention to body images for women with both high and low body dissatisfaction, whereas body satisfaction priming had no effect on attention for either group. These findings show that women with high and low body dissatisfaction are vulnerable to the effects of body dissatisfaction priming.
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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 | 0.004 |
| 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) | 0.004 | 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".