Self-reported and physiological reactions to thin and non-thin bodies: Understanding motivational processes underlying disordered eating
Bibliographic record
Abstract
Objective: The present study examined the relative roles of approach and avoidance motivation in eating pathology using a multi-method approach combining self-report and physiological measures. The potential effect of internalized ideals and fears was also investigated. Method: Fifty-nine undergraduate women completed a picture-viewing task in which they viewed images of women’s bodies (thin and non-thin) and affective images. Self-report ratings of valence and arousal were measured along with physiological indicators of approach (postauricular reflex) and avoidance (startle blink reflex) motivation. Results: Greater eating pathology was associated with more negative valence ratings of both thin and non-thin images. There was a significant interaction between valence ratings of non-thin bodies and fear of the unattractive self in relation to eating pathology, such that eating pathology was highest in those who rated non-thin images as more unpleasant and internalized fears of being/becoming unattractive. Thin-ideal internalization did not significantly interact with ratings of thin images to predict eating pathology. There were no significant findings when examining physiological data. Conclusions: Results from self-report measures suggest that eating pathology is associated with avoidant reactions to both thin and non-thin bodies and highlight the importance of internalized appearance-related fears.
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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) | 0.001 | 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".