Bilingual toddlers’ comprehension of mixed sentences is asymmetrical across their two languages
Bibliographic record
Abstract
In bilingual language environments, infants and toddlers listen to two separate languages during the same key years that monolingual children listen to just one and bilinguals rarely learn each of their two languages at the same rate. Learning to understand language requires them to cope with challenges not found in monolingual input, notably the use of two languages within the same utterance (e.g., Do you like the perro? or ¿Te gusta el doggy?). For bilinguals of all ages, switching between two languages can reduce the efficiency in real-time language processing. But language switching is a dynamic phenomenon in bilingual environments, presenting the young learner with many junctures where comprehension can be derailed or even supported. In this study, we tested 20 Spanish-English bilingual toddlers (18- to 30-months) who varied substantially in language dominance. Toddlers' eye movements were monitored as they looked at familiar objects and listened to single-language and mixed-language sentences in both of their languages. We found asymmetrical switch costs when toddlers were tested in their dominant versus non-dominant language, and critically, they benefited from hearing nouns produced in their dominant language, independent of switching. While bilingualism does present unique challenges, our results suggest a united picture of early monolingual and bilingual learning. Just like monolinguals, experience shapes bilingual toddlers' word knowledge, and with more robust representations, toddlers are better able to recognize words in diverse sentences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".