Self-positivity bias: A comprehensive examination of the ERP and behavioral substrates of self- and other-referential processing in early adolescence
Bibliographic record
Abstract
Self-referential information, especially when positive in valence, is uniquely salient and preferentially processed even in children. This "self-positivity" bias is critical for adaptive socioemotional development and protects against the emergence of psychopathology. Early adolescence is a critical period of rapid maturation in self-cognition and brain functions; however, it remains unclear how the early adolescence brain mediates the self-positivity bias in information processing. Ninety-two 10- to 14-year-old community-dwelling early adolescents completed an EEG version of the Self-Referent Encoding Task (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 component 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 recognition, memory sensitivity, and the aLPP: youths showed better recognition, higher memory sensitivity, an enhanced aLPP for Self-Positive versus Self-Negative words, whereas no such differences were found between Other-Positive and Other-negative words. We provided novel, robust evidence on a self-positivity bias that uniquely favored Self-Positive words across different behavioral and ERP metrics of the SRET in youths. These findings contribute to our mechanistic knowledge of how early adolescents process self-referential information 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".