Late Positive Potential Elicited by Monetary Reward Feedback Predicts Changes of Disordered Eating From Age 11 to Age 12 in Community‐Dwelling Girls
Bibliographic record
Abstract
OBJECTIVE: Early adolescence is marked by elevated psychopathology, including disrupted eating attitudes and behaviors. Reward processing is an identified mechanism in portending eating pathology, that is, aberrant reward responsivity may contribute to disrupted reward-seeking behaviors (e.g., food consuming). This literature has focused on adults or mid-to-late adolescents, with little work done on early adolescence. We examined the linkages between reward feedback processing, indexed by event-related potentials (ERPs), and changes of emerging disordered eating in community-dwelling early adolescents. METHOD: At T1, 115 youths (66 girls, mean/SD age = 11.00/1.16 years) completed an EEG monetary reward Doors task. Youths completed the Eating Disorder Examination-Questionnaire Short at T1 and ~6 months (T2) and ~12 months (T3) after T1. In the ERP data, we isolated a reward positivity (RewP) and a late positive potential (LPP) via principal component analysis. We applied multilevel modeling to examine whether baseline ERPs interacted with Time in predicting disordered eating and whether these interactions varied by sex. RESULTS: We found a significant Time × LPP interaction in girls but not boys. Among girls, only those with a smaller LPP toward the losses (versus wins), which might reflect suboptimal evaluation and regulatory processes in undesired situations, showed increases in disordered eating from T1 to T3. DISCUSSION: We provided preliminary yet novel evidence concerning the prospective associations between reward processing and changes of disordered eating in early adolescents. Future studies along this line will be critical for understanding the early mechanisms of eating pathology, identifying youths at risk, and developing prevention strategies.
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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 | 0.001 |
| 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) | 0.003 | 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".