Investigating Selective Attentional Biases in Nutritional Food Labels Through Eye-Tracking in the Disordered-Eating Population
Bibliographic record
Abstract
BACKGROUND: Recent research has implicated the role of selective attentional biases in a variety of anxiety disorders. Specifically, in individuals engaging in patterns of disordered eating, such biases are believed to be a moderating factor in food choice and/or avoidance. The current study used eye-tracking methodology to examine how selective attentional biases towards specific stimuli on nutritional food labels were moderated by gender, BMI, and presence of specifc patterns of disordered eating. METHOD: A total of 60 participants were asked to complete a triad of clinical eating questionnaires (EDI-3, EDQ, and SCOFF), view a series of nutritional food labels, and decide whether such labels were indicative of healthy or unhealthy foods all while eye-movements were recorded to quantify overall viewing times, number of fixations, and total number of saccades. RESULTS: Overall, participants as a whole spent most time viewing and fixating on calorie values, rating lower calorie values as healthy and those high as unhealthy. In terms of gender, male participants spent more time fixating on protein values, rating higher values as healthy, while females spent more time fixating on carbohydrate values, rating lower values as healthy. Participants at a high-risk for anorexia spent significantly more time viewing and fixating on fat values, rating lower values as healthy and higher values as unhealthy, while participants with a high drive for thinness spent most time viewing sugar and calorie values. CONCLUSION: Results from this study suggest that selective attentional biases affect perception to nutritional food labels and that those biases are influenced by both gender and presence of specific patterns of disordered-eating.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".