Decisions on a platter: Food biases and arousal alter reward learning
Bibliographic record
Abstract
Food seeking and avoidance are powerful drivers of decision-making in healthy and clinical populations. The urge to eat engages primary reward systems in the brain, thought to bias preference for energy-dense or high-calorie food more likely to sate hunger. This process is however interrupted in eating disorders where high-calorie food is avoided. It is nevertheless unclear how innate or learned food biases may interact with general reward processing to predict learning and decisions. We developed a novel paradigm to investigate whether and how biases for high- and low-calorie food alter decision-making in a large sample of participants with typical eating (‘TE’) and disordered eating (‘DE’) behavior. Importantly, food characteristics (high- vs. low-calorie) were completely incidental to the task goal of maximizing monetary reward. An arousal manipulation involving a large monetary win or loss, examined whether heightened arousal, thought to influence goal-directed behavior, enhances the impact of food biases on reward learning. Consistent with prior notions of food-relevant biases, the TE group learned better (i.e., made more correct choices) when high-calorie foods were rewarding while the DE group learned better when low-calorie foods were rewarding. The arousal manipulation boosted this group-dependent bias. Fitting behavior to reinforcement learning models enabled us to identify distinct cognitive components underlying the effect. Specifically, the impact of food-related biases on learning was best explained when accounting for group differences in the initial values and learning rates (for positive prediction errors) for high- and low-calorie foods. In other words, typical and disordered eaters showed differences in their pre-experimental preference for a food type (high- or low-calorie food, respectively), as well as the extent to which they learned from positive outcomes associated with that preference. These findings provide a mechanistic account of how food biases alter reward-based choice, especially under heightened arousal. Our results suggest that interventions altering innate or learned food preference should target habit-directed mechanisms to help mitigate maladaptive eating behavior.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".