Examining the roles of reward sensitivity and difficulties in emotion regulation in relation to low-restraint binge eating
Bibliographic record
Abstract
Leading treatments for binge eating target dietary restraint, but up to 35% of the people with binge eating report low restraint. This study examined the roles of reward sensitivity and emotion dysregulation in relation to low-restraint binge eating. Women with binge eating (low-restraint: n = 22; high-restraint: n = 69) and controls (n = 49) completed self-report measures of generalized reward sensitivity and emotion dysregulation and a picture-viewing task assessing craving and pleasure for high-calorie food. As expected, food-related craving and emotion dysregulation were greater in the clinical than in the control group, but no differences emerged between high- and low-restraint binge eating groups. However, correlational analyses found that, within the clinical group, the number of restraint days related to greater anticipatory sensitivity for generalized rewards and lower pleasure ratings of food. Results suggest that emotion dysregulation characterizes both high- and low-restraint binge eating. As self-reported food liking was linked with lower restraint, greater enjoyment of palatable foods may uniquely contribute to low-restraint binge eating. Increasing emphasis on emotion regulation and food-related reward sensitivity may enhance treatment outcomes for individuals with low-restraint binge 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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".