Phonological activation in bilinguals: Evidence from interlingual homograph naming
Bibliographic record
Abstract
This study investigated whether bilinguals simultaneously activate phonological representations from both of their languages when reading words in just one. The critical stimuli were interlingual homographs (e.g., PAIN) that were low in frequency in the target language of the study (English) and high in frequency in the nontarget language (French). Both English-French and French-English bilinguals were tested. In each experiment, participants named a block of English experimental words, a block of French filler words, and then a second block of English experimental words. In the first block of English trials, the English-French bilinguals had similar naming latencies for homographs and English-only control words, although they made more errors on homographs. In contrast, the French-English bilinguals showed a homograph disadvantage in both the latency and error data. In the second block of English trials, both the English-French bilinguals and the French-English bilinguals showed homograph interference on latency and error measures. We interpret these results as indicating that the activation of phonological representations can appear to be both language-specific and nonspecific, depending on the characteristics of the bilingual and whether they have recently named words in the nontarget language.
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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 | 0.004 |
| 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) | 0.004 | 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".