Attention to fat- and thin-related words in body-satisfied and body-dissatisfied women before and after thin model priming
Bibliographic record
Abstract
Understanding the cognitive processes underlying body dissatisfaction provides important information on the development and perpetuation of eating pathology. Previous research suggests that body-dissatisfied women process weight-related information differently than body-satisfied women, but the precise nature of these processing differences is not yet understood. In this study, eye-gaze tracking was used to measure attention to weight-related words in body-dissatisfied (n = 40) and body-satisfied (n = 38) women, before and after exposure to images of thin fashion models. Participants viewed 8-second displays containing fat-related, thin-related, and neutral words while their eye fixations were tracked and recorded. Based on previous research and theory, we predicted that body-dissatisfied women would attend to fat-related words more than body-satisfied women and would attend to thin-related words less. It was also predicted that exposure to thin model images would increase self-rated body dissatisfaction and heighten group differences in attention. The results indicated that body-dissatisfied women attended to both fat- and thin-related words more than body-satisfied women and that exposure to thin models did not increase this effect. Implications for cognitive models of eating disorders are discussed.
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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 | 0.000 | 0.002 |
| 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) | 0.006 | 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".