People who overvalue appearance selectively attend to descriptors of the attractiveness ideal: Findings from an emotional Stroop task
Bibliographic record
Abstract
OBJECTIVE: Attentional biases to stimuli related to stigmatized appearance are theorized to stem from appearance overvaluation, but little research has examined this link. As well, little research has examined whether appearance overvaluation is associated with biases toward stimuli that describe the attractiveness ideal. We addressed these gaps in the literature using the emotional Stroop task and tested whether appearance overvaluation is associated with an attentional bias for words that describe stigmatized appearance (e.g., fat, ugly, shabby), the attractiveness ideal (e.g., thin, beautiful, fashionable), or both. METHOD AND RESULTS: In Study 1, a community sample of people (N = 86) completed measures of appearance overvaluation, body dissatisfaction, and body mass index (BMI) followed by an emotional Stroop task. Appearance overvaluation was associated with an attentional bias for attractiveness ideal words-not stigmatized appearance words. Results remained significant when controlling for participants' body dissatisfaction, BMI, and gender. Study 2 (N = 316) replicated these findings. Finally, an integrative data analysis that pooled the data of Studies 1 and 2 (N = 402) provided additional support for our general hypothesis that people who overvalue appearance display an attentional bias to stimuli related to the attractiveness ideal. DISCUSSION: The results show a robust association between appearance overvaluation and selective attention for attractiveness ideal stimuli. Results are discussed in reference to theory and research on information processing in eating disorders. We also call for research to examine information processing of stimuli related to the attractiveness ideal among people with eating disorders and who place high importance on appearance for self-definition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".