ERP correlates of self-referential processing moderate association between pubertal status and disordered eating in preadolescence
Bibliographic record
Abstract
Objective: Preadolescence is a critical period for the onset of puberty and eating-related psychopathology. More advanced pubertal status is associated with elevated eating pathology. However, it was unclear whether this association was moderated by self-referential processing, an important, modifiable cognitive risk for various forms of psychopathology, including eating problems. Further, no study has examined the neural correlates of self-referential processing in relation to eating pathology. Method: We examined how the association between pubertal status and disordered eating was moderated by self-referential processing in a community sample of 115 nine-to-12-year-old preadolescents (66 girls; Mean age/SD =10.98/1.18 years; 87.5% White). Youths reported their pubertal status and disordered eating behaviors and completed an ERP version of the Self-Referent Encoding Task (SRET) to assess self-referential processing. A Principal Component Analysis of the ERP data identified an anterior late positive potential (LPP) in both the positive and negative SRET conditions. Results: The LPP in the positive condition moderated the positive association between pubertal status and disordered eating behaviors, such that this association was significant for youths with a smaller LPP toward positive self-referential cues, but non-significant for those showing a larger LPP toward positive self-referential cues. Conclusions: These results suggest that a deeper processing of positive self-referential information, indicated by a potentiated LPP, may weaken the negative impact of pubertal status on disordered eating. Our findings also suggest that enhancing positive self-referential processing may be a useful tool in preventing the development of eating pathology in preadolescents, especially for those with more advanced pubertal status.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".