The role of prior language context on bilingual spoken word processing: Evidence from the visual world task
Bibliographic record
Abstract
We investigated whether speaking in one language affects cross- and within-language activation when subsequently switching to a task performed in the same or different language. English–French bilinguals (L1 English, n = 29; L1 French, n = 28) were randomly assigned to a prior language context condition consisting of a spontaneous production task in English (the no-switch group) or in French (the switch group). Participants then performed an English spoken language comprehension task using the visual world method. The key result was that the switch group showed less evidence of cross-language competition than the no-switch group, consistent with the notion of an active inhibition of a prior language in the switch group. These data suggest that proficient bilinguals can globally suppress a non-target language, whether it is L1 or L2, though doing so requires cognitive resources that may be diverted from other demands, such as controlling within-language competition.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".