Late positive potentials elicited by negative self-referential processing predict increases in social anxiety, but not depressive, symptoms from age 11 to age 12
Bibliographic record
Abstract
Social anxiety and depression exacerbate in early adolescence. Maladaptive self-referential processing confers risk for both conditions and can be assessed by the Self-Referent Encoding Task (SRET). Our cross-sectional findings indicated that the SRET-elicited anterior late positive potential (LPP) was uniquely associated with social anxiety symptoms, whereas behavioral SRET scores were uniquely associated with depressive symptoms. Expanding this work, this study investigated whether the SRET-generated behavioral and LPP indices differentially predicted changes of social anxiety or depressive symptoms over time. At baseline, 115 community-dwelling youths (66 girls; Mean age/SD = 11.00/1.16 years) completed an SRET with EEG. Youths reported social anxiety and depressive symptoms at baseline and ∼six and ∼ 12 months later, based on which the intercept and slope of symptoms were estimated as a function of time. A larger anterior LPP in the negative SRET condition uniquely predicted a larger slope (faster increase) of social anxiety (but not depressive) symptoms. Greater positive behavioral SRET scores marginally predicted a smaller slope (slower increase) of depressive (but not social anxiety) symptoms. We provided novel evidence concerning the differential, prospective associations between self-referential processing and changes of social anxiety and depressive symptoms in early adolescence.
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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) | 0.001 | 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".