<scp>ERP</scp> correlates of self‐referential processing moderate the association between pubertal status and disordered eating in preadolescence
Bibliographic record
Abstract
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. To address these gaps, 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. 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. 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 | 0.001 |
| 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 | 0.000 | 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".