The role of language control in cross-language phoneme processing: Evidence from Chinese–English bilinguals
Bibliographic record
Abstract
Aims: Cross-language interference studies of language control mainly focus on the lexical level, whereas language control may occur at the smallest unit phonemic level of language. In the present study, we examined the role of language control during cross-language phoneme processing. Design: Participants used one language to name pinyin or alphabet in the single-language blocks, and they used two languages for naming in the mixed-language blocks. Data and analysis: Using a linear mixed-effects model, we built models for mixing costs and switching costs based on reaction times (RTs) and accuracy. Findings: Switching between Chinese (L1) and English (L2) phonetic symbols revealed both mixing and switching costs. Originality: The findings suggest that switching of cross-language phonemes requires not only global control of the non-target phonemes, but also local control of the non-target phonemes. Significance: Just as cross-language interference control occurs at the lexical level, this study demonstrates that control also occurs at the phonemic level.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".