An event‐related potential study of self‐positivity bias in native and foreign language contexts
Bibliographic record
Abstract
Self-positivity bias is a common psychological phenomenon in which individuals often associate positive information with themselves. However, little is known about how self-positivity bias is modulated by different language contexts (e.g., a first vs. second language). To this end, we analyzed behavioral and electrophysiological data to examine whether first or second languages play differential roles in the self-positivity bias effect. We used a modified self-positivity bias task which required Chinese-English bilinguals to judge strings of letters or characters as realwords or not and match associations between identity (self, other) and a geometric shape (circle, triangle). The target words in the experiment consisted of positive, negative, and neutral emotional words. The results showed that in the L2 context, the self-positivity condition elicited a smaller N400 effect relative to the self-negativity condition and a larger late positive component effect relative to the self-negativity and self-neutrality conditions. Furthermore, the other-positivity condition elicited a stronger N400 effect than the other-neutrality condition. These patterns did not emerge in the L1 context. We discuss the implications and contributions of these findings to better understand the interaction between emotion and self-concept in different language contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".