Prosody cues word order in 7-month-old bilingual infants
Bibliographic record
Abstract
A central problem in language acquisition is how children effortlessly acquire the grammar of their native language even though speech provides no direct information about underlying structure. This learning problem is even more challenging for dual language learners, yet bilingual infants master their mother tongues as efficiently as monolinguals do. Here we ask how bilingual infants succeed, investigating the particularly challenging task of learning two languages with conflicting word orders (English: eat an apple versus Japanese: ringo-wo taberu ‘apple.acc eat’). We show that 7-month-old bilinguals use the characteristic prosodic cues (pitch and duration) associated with different word orders to solve this problem. Thus, the complexity of bilingual acquisition is countered by bilinguals’ ability to exploit relevant cues. Moreover, the finding that perceptually available cues like prosody can bootstrap grammatical structure adds to our understanding of how and why infants acquire grammar so early and effortlessly. Bilingual infants possess a unique ability to rapidly acquire the grammar of both of their native languages. Gervain and Werker find that bilingual infants achieve this by using characteristic prosodic cues associated with different word orders.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".