The activation of phonological representations by bilinguals while reading silently: Evidence from interlingual homophones.
Bibliographic record
Abstract
These experiments investigated whether bilinguals activate phonological representations from both of their languages when reading silently in one. The critical stimuli were interlingual homophones (e.g., sank in English and cinq in French). French-English and English-French bilinguals completed an English lexical decision task. Decisions made by French-English bilinguals were significantly faster and more accurate for interlingual homophones than for matched English control words. In subsequent experiments, the homophone facilitation effect in the latency data disappeared when distractors were changed to pseudohomophones, when cognates and interlingual homographs were added to the experiment, and when the proportion of critical stimuli was decreased. However, the homophone effect in the error data remained. In contrast, English-French bilinguals revealed little evidence of an interlingual homophone effect. Several attempts were made to increase the saliency of the nontarget language, however these manipulations produced only a small effect in the error data. These results indicate that the activation of phonological representations can appear to be both language-specific and nonspecific depending on the proficiency of the bilinguals and whether they are reading in their weaker or stronger 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 | 0.000 | 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) | 0.002 | 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".