The first steps in word learning are easier when the shoes fit: comparing monolingual and bilingual infants
Bibliographic record
Abstract
English, French, and bilingual English-French 17-month-old infants were compared for their performance on a word learning task using the Switch task. Object names presented a /b/ vs. /g/ contrast that is phonemic in both English and French, and auditory strings comprised English and French pronunciations by an adult bilingual. Infants were habituated to two novel objects labeled 'bowce' or 'gowce' and were then presented with a switch trial where a familiar word and familiar object were paired in a novel combination, and a same trial with a familiar word-object pairing. Bilingual infants looked significantly longer to switch vs. same trials, but English and French monolinguals did not, suggesting that bilingual infants can learn word-object associations when the phonetic conditions favor their input. Monolingual infants likely failed because the bilingual mode of presentation increased phonetic variability and did not match their real-world input. Experiment 2 tested this hypothesis by presenting monolingual infants with nonce word tokens restricted to native language pronunciations. Monolinguals succeeded in this case. Experiment 3 revealed that the presence of unfamiliar pronunciations in Experiment 2, rather than a reduction in overall phonetic variability was the key factor to success, as French infants failed when tested with English pronunciations of the nonce words. Thus phonetic variability impacts how infants perform in the switch task in ways that contribute to differences in monolingual and bilingual performance. Moreover, both monolinguals and bilinguals are developing adaptive speech processing skills that are specific to the language(s) they are learning.
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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) | 0.000 | 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 | 0.000 |
| 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".