Second label learning in bilingual and monolingual infants
Bibliographic record
Abstract
Mutual exclusivity is the assumption that each object has only one category label. Prior research suggests that bilingual infants, unlike monolingual infants, fail to adhere to this assumption to guide word learning. Yet previous work has not addressed whether bilingual infants systematically interpret a novel word for a familiar object (i.e. an object with a known category label) as a second category label. We addressed this question by exploring bilingual and monolingual infants' use of mutual exclusivity in a task in which they heard a novel label for a familiar object with a salient color (e.g. an aqua-colored dog). They were subsequently tested with two trials that probed whether they interpreted the word as a second category label for the object (e.g. another word meaning dog) or as a label for one of the object's salient properties, namely its color (e.g. a word meaning aqua). Bilingual infants failed to adhere to mutual exclusivity and interpreted the novel word systematically as a second object category label for the familiar object. In contrast, consistent with their use of mutual exclusivity, monolingual infants rejected the novel word as a second category label, and instead showed some evidence of interpreting it as a property (color) term for the familiar object. The findings suggest that both bilingual and monolingual infants are systematic in their interpretation of a novel label for a familiar object, but that they show different interpretations of that label. We thus argue that theoretical accounts of early word learning must consider the crucial role of linguistic experience.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".