Immediate cross-language transfer of novel articulatory plans in bilingual speech.
Bibliographic record
Abstract
Current models of second language (L2) acquisition focus on interactions with a first language (L1) at the level of speech sound targets. In multilinguals, the degree of interaction between the articulatory plans that guide speech in each language remains unclear. Here, we directly address this question in bilingual speakers. We use a sensorimotor adaptation paradigm to drive the acquisition of novel articulatory plans for speech in one language and then measure the extent to which these new motor plans influence articulatory plans in the speaker's other language. Twenty L1-French, L2-English bilinguals adapted their speech production to a real-time alteration of vowel sounds. In one session, the adaptation was acquired during French sentence production; in a second session, the adaptation was acquired during English sentence production. In each session, cross-language transfer of these novel articulatory plans for speech was assessed using a transfer task that involved the production of French and English words with heavily noise-masked auditory feedback. Sensorimotor adaptation that countered the vowel sound alteration was observed in both French and English. Regardless of the linguistic context in which the adaptation was acquired, the adaptation transferred to the production of words in both languages. The amount of transfer did not depend on whether the adaptation was acquired in the participant's L1 or L2. In a second experiment, the result was replicated with 20 L1-English, L2-French speakers. The experiments support the idea that, in bilinguals, the interaction between L1 and L2 articulatory motor plans is rapid and bidirectional. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 |
| 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 | 0.001 |
| 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".