Trial-level ERPs predicted behavioral responses during self-referential processing in late childhood
Bibliographic record
Abstract
Self-referential information is uniquely salient and preferentially processed even in children. The literature has used the self-referent encoding task (SRET) combined with event-related potentials (ERPs) to study self-referential processing and its associations with youth psychopathology. However, it is unclear how the ERP and behavioral indices of SRET are associated with each other, although this knowledge can promote our mechanistic understanding of this construct and its role in psychopathology. We examined this question in 115 9- to 12-year-old children, a critical period for the development of self-related concepts. By applying a multilevel modeling approach to the trial-level data of SRET, we disaggregated the between- and within-person variability and observed within-person, but not between-person, effects of the P2 and late positive potential (LPP) on behavioral responses: a larger P2 on a given trial predicted a faster response in this trial; a larger LPP on a given trial predicted a higher likelihood of endorsing the word of this trial. We provided novel evidence on how the within-person variability of the ERPs predicted the overt responses of the SRET in children. These findings inform our mechanistic knowledge of self-referential processing and shed light on a better understanding of the role of self-referential processing in the development of psychopathology.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".