Is There a Bilingual Advantage in Implicit and Explicit Phonetic Imitation?
Bibliographic record
Abstract
PURPOSE: There are a number of reasons to predict that early bilinguals might have better imitation abilities than monolinguals; however, evidence for a bilingual advantage in phonetic imitation is mixed. In the current study, we attempt to reconcile these disparate findings by testing Spanish-English bilinguals' and English monolinguals' imitation of word-initial voice onset time (VOT) across two types of imitation tasks (implicit: word repetition and explicit: word imitation). METHOD: In both tasks, participants heard English /p/-initial words manipulated to have canonical, shortened, or lengthened VOT. They were asked to repeat each word they heard, either with or without explicit instructions to imitate. RESULTS: Overall, the explicit task elicited more imitation than the implicit task. In the explicit task, both groups converged to both lengthened and shortened VOTs, whereas in the implicit task, both groups converged to lengthened VOTs but not to shortened VOTs. Importantly, we did not observe differences in degree of imitation between monolinguals and bilinguals. CONCLUSIONS: This study found no evidence of a bilingual advantage in either implicit or explicit imitation. However, the two tasks elicited different patterns of results, with more imitation in the explicit task than in the implicit task, in terms of the degree of imitation (for lengthened VOT) and the presence/absence of imitation (for shortened VOT). In summary, the implicit versus explicit nature of the task cannot account for the mixed evidence for a bilingual advantage in imitation found in previous studies; more work is necessary to uncover which factors might underlie these discrepancies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".