Lexical recruitment across contrasts in Japanese and Mandarin L2 English speakers
Bibliographic record
Abstract
Lexical recruitment in speech perception can vary across individuals and tasks (Giovannone & Theodore, 2023). This study examines second-language (L2) English speakers to explore how lexical bias can be shaped by language background and L2 proficiency. Mandarin and Japanese speakers (ages 18–35) participated in a Ganong task for phoneme categorization using stimuli from four phonemic contrasts: /ɛ/–/æ/, /r/–/l/, /s/–/ʃ/, and /d/–/t/. Using L2 populations allows us to assess how language experience influences lexical bias without confounding age-related cognitive factors. The selected contrasts span acoustic dimensions—including formant, fricative spectra, and voice onset time. They also vary in expected difficulty across groups: the liquid contrast is likely more challenging for Japanese speakers, while the vowel contrast may be difficult for both groups but slightly less so for Japanese speakers due to exposure through English loanwords. Data collection is ongoing. Preliminary results show contrast-dependent differences in lexical bias between language groups, but no consistent correlation between English proficiency (measured through LexTALE; Lemhöfer & Broersma, 2012) and lexical bias. This suggests that language ability in L2 speakers may not directly modulate lexical recruitment. These findings suggest a potential role for native language background in integrating lexical and acoustic information in perception.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".