Depressive symptoms modulated the trial-level changes in behavioral and ERP measures of self-referential processing in early adolescents
Bibliographic record
Abstract
Self-referential processing has been linked to internalizing psychopathology in children and adolescents, including depression. Although numerous studies have examined this association, most have focused on the between-person differences in the behavioral (e.g., reaction time [RT]) and neurophysiological (e.g., event-related potentials [ERPs]) indices of self-referential processing. It remains unclear how self-referential processing unfolds dynamically from moment to moment within individuals, and to what extent these within-person fluctuations may be modulated by individual differences in depressive symptoms. Using a multilevel modelling (MLM) approach, the present study examined the trial-level changes in RT and ERP indices (anterior late positive potential [LPP]) of self-referential processing and their associations with depressive symptoms in 115 community-dwelling youths aged 9–12. We found curvilinear patterns in both the anterior LPP and RT, with youths showing an initial decrease followed by a subsequent increase in these measures over the course of the task. Moreover, compared to their lower-symptom peers, youths with higher depressive symptoms showed distinct patterns of changes in the anterior LPP and RT towards endorsed positive words. These findings extend existing evidence on the between-person associations between self-referential processing and depressive symptoms, providing novel insight into how depressive symptoms shape the dynamic, moment-to-moment processing of self-relevant information in youths. Further research is needed to elucidate the neurocognitive mechanisms underlying these trial-level dynamics and their associations with between-person differences in internalizing symptoms.
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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".