Self-positivity bias and/or other-negativity bias? A comprehensive examination of the ERP correlates of self- and other-referential processing in early adolescence
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 ERPs to study the neural substrates of self-referential processing and its role in development. However, no work has implemented a data-driven, comprehensive examination of the ERP correlates of SRET in youths by comparing a self-referential condition with an other-referential condition. Ninety-two 10-to-14-year-old typically developing youths completed an ERP version of the SRET consisting of a self-referential and an other-referential condition, following which they were unexpectedly asked to complete a recognition task of the presented words. A data-driven Principal Components Analysis isolated five SRET-elicited ERPs: P1, P2, N400, and anterior and posterior late positive potential (aLPP, pLPP). Two-way ANOVAs (Referent × Valence) demonstrated a “self-positivity” bias in aLPP, recognition, and memory sensitivity: youths showed an enhanced aLPP, better recognition, and higher memory sensitivity for Self-Positive versus Self-Negative words, whereas no such differences were found between Other-Positive and Other-negative words. Further, a (marginal) “other-negativity” bias was found in pLPP, P2, and recognition: youths displayed an enhanced pLPP and P2 and higher memory sensitivity in the Other-Negative versus Other-Positive condition, whereas no such pattern was observed in the Self conditions. We provided novel evidence on a self-positivity bias that uniquely favored positive self-referential words as well as an other-negativity bias that uniquely favored negative other-referential words. These findings contribute to our mechanistic knowledge of self-referential processing in youths and inform future studies on the role of self-referential processing in socioemotional development.
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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) | 0.001 | 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".