A mechanistic staging model of reward processing alterations in individuals with binge‐type eating disorders
Bibliographic record
Abstract
Altered reward processing is thought to characterize binge-type eating disorders, but the exact nature of these alterations is unclear. A more fine-grained understanding of whether specific aspects of reward processing contribute to the development or maintenance of binge eating may point to new therapeutic targets and personalized treatments. The incentive sensitization theory of addiction proposes that repeated use of a substance increases the desire to approach a reward ('wanting') but not pleasure when consuming the reward ('liking'), suggesting that reward processes driving addiction change over time. We hypothesize that the same may be true for binge eating. Further, consistent with the maladaptive scaling hypothesis, reward processing may be heightened for multiple reinforcers in at-risk individuals but become tuned toward food once binge eating is initiated. In this article, we propose a mechanistic staging model of reward processing in binge-type eating disorders that synthesizes existing data and posits that alterations of reward processing depend on illness stage and reward type. We outline translational methods for testing key hypotheses and discuss clinical implications. Considering reward processing alterations in relation to illness stage has the potential to improve treatment outcomes by ensuring that the mechanisms targeted are personalized to the individual patient. PUBLIC SIGNIFICANCE: Individuals with binge-type eating disorders experience alterations in their desire for, and pleasure from, food. We believe that the exact nature of these alterations in reward processing change over the course of illness-from the at-risk state to an established illness. If true, treatments for binge-type eating disorders that target reward processing should be personalized to the illness stage of the patient.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".