Effects of English Proficiency on Caucasian Face Gender Perception by Chinese-English Bilinguals: Evidence from ERP
Bibliographic record
Abstract
Chinese and English differ in the encoding of biological gender in the spoken forms of 3rd person singular pronouns. Linguistic relativity theories predict that structural differences across languages are accompanied with differences in non-linguistic cognition. However, the pronoun difference between the two languages seems so trivial that its influence on gender perception is unbelievable except with empirical support. The present study conducted an ERP experiment with native speakers of Chinese learning English as a foreign language and differing in English proficiency. The odd-ball paradigm was used to examine whether L2 proficiency would influence how these Chinese-English bilinguals perform on Caucasian face gender perception. The experiment yielded null effect of L2 proficiency on the vMMN that was elicited for the gender category, as well as the control age category. The results suggest that the difference in the pronoun encoding of biological gender between Chinese and English may not influence gender perception in the nonlinguistic context, although it is not surprising considering the triviality of such cross-linguistic difference and the widespread gender binary opposition in daily life.
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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) | 0.003 | 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".