Eating disorder symptoms and emotional arousal modulate food biases during reward learning in females
Bibliographic record
Abstract
Food seeking and avoidance engage primary reward systems to drive behavior. It is nevertheless unclear whether innate or learned food biases interact with general reward processing to interfere with goal-directed choice. To this end, we recruited a large non-clinical sample of females with high eating-disorder symptoms (‘HED’) and a matched sample of females with low eating-disorder symptoms (‘LED’) to complete a reward-learning task where the calorie content of food stimuli was incidental to the goal of maximizing monetary reward. We find and replicate a low-calorie food bias in HED and a high-calorie food bias in LED, reflecting the strength of pre-experimental food-reward associations. An emotional arousal manipulation shifts this group-dependent bias across individual differences, with interoceptive awareness predicting this change. Reinforcement-learning models further identify distinct cognitive components supporting these group-specific food biases. Our results highlight the influence of reinforcement-based mechanisms and emotional arousal in eliciting potentially maladaptive food-reward associations. Disordered eating can disrupt the rewarding value of food. Here, the authors show in a female sample that eating disorder symptoms, emotional arousal, and interoceptive awareness modulate goal-irrelevant food biases during reinforcement learning.
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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 | 0.000 |
| 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".