Budding bilinguals: Investigating bilingual infants’ language acquisition
Bibliographic record
Abstract
Almost one in six children in Canada grow up hearing two languages. Bilingual children frequently encounter switches between their languages, and must learn words in both. My thesis tackles three central issues of early bilingual language development. \nManuscript 1 investigated whether infants can detect language switches at the level of individual words. This ability could help infants cope with rapid language switching in their language input, and prepare them, once they start speaking, to have control over which language they use. I tested bilingual and monolingual 8- to 12-month-olds’ interest in single-language trials (“milk…dog”) and switched-language trials (“milk…chien”). Neither group showed evidence of differentiating between contexts, suggesting that detection of single-word language switching is more difficult than previously assumed based on prior research investigating multi-sentence language switching. \nManuscript 2 examined whether infants can associate a person with the language that person is speaking. Some theories of early bilingualism propose that person–language associations help infants navigate their bilingual language input. I tested 5- to 18-month-olds’ surprisal when a speaker switches to a different language. Results showed no evidence that infants spontaneously associate a person with a language. This contrasts with common but outdated advice to caregivers to choose a single language when speaking to their child, and is consistent with research showing that bilingual infants learn languages from a variety of family language strategies. \nManuscript 3 examined how bilingual infants mentally represent the sounds in familiar words. Bilingual and monolingual infants were tested to examine whether bilingual infants are more or less sensitive to mispronunciations than monolinguals. I tested 24- to 26-month-olds in a looking-while-listening task. I found a robust mispronunciation effect in bilingual and monolingual toddlers, indicating that bilinguals’ encoding of sounds in familiar words is phonetically detailed in a similar way as monolinguals’, despite bilinguals having to navigate a more complex phonetic environment. \nIn these three manuscripts, I found that bilingual language acquisition is similar to monolingual development in many aspects, and rigorous testing of assumptions about bilingual language acquisition is needed to learn about the mechanisms bilingual infants use to acquire language.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".