Emotional Responses to Food Pictures according to Caloric Value in Women with an Eating Disorder
Bibliographic record
Abstract
Objective: The aim of the current study was to improve the understanding of emotions evoked by food pictures in women with an eating disorder (ED), by distinguishing anorexia nervosa (AN) and bulimia nervosa (BN) diagnoses, while taking into account the caloric content of food and the influence of participants’ nutritional knowledge. Methods: Thirteen AN, 9 BN and 22 healthy controls (HC) women participated in the study. In a laboratory setting, participants first completed self-report questionnaires regarding their affective state. Then, an emotional rating task of food and non-food pictures was performed in order to examine participants’ emotional reactions to these pictures, depending on the caloric value of the food depicted and controlling for internal state. Finally, an energy density ranking task of food pictures was completed to investigate participants’ nutritional knowledge and its influence on their reactions to food. Results: Compared to HC, ED participants experienced more fear towards food, which was neither due to their internal state nor to their nutritional knowledge. In AN, fear occurred towards all food, whereas in BN, fear was observed for high-calorie products only. Conclusion: The key role of food-induced fear in ED was highlighted, particularly in AN.
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 | 0.000 | 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) | 0.002 | 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".