Fine-tuning language discrimination: Bilingual and Monolingual infants’ detection of language switches
Bibliographic record
Abstract
The ability to differentiate between two languages sets the stage for bilingual learning. Infants can discriminate languages when hearing long passages, but language switches often occur on short time scales with few cues to language identity. As bilingual infants begin learning sequences of sounds and words, how do they detect the dynamics of two languages? In two studies using the head-turn preference procedure, we investigated whether infants (n = 44) can discriminate languages at the level of individual words. In Study 1, monolingual and bilingual 8- to 12-month-olds were tested on their detection of single-word language switching in lists of words (e.g., “dog… lait [fr. milk]”). In Study 2, they were tested on language switching within sentences (e.g., “Do you like the lait?”). Infants detected language switching within sentences, but not in lists of words. Moreover, there was no difference between bilingual and monolingual infants’ performance. Based on these contrasting effects for natural sentences versus lists of words, we conclude that infants may detect language switches more successfully if preceded by sequences of sounds and words in a single language. The ability to detect disruptions in such sequences is likely important in supporting the beginnings of bilingual proficiency.
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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 | 0.003 |
| 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".