Bilingualism affects 9‐month‐old infants’ expectations about how words refer to kinds
Bibliographic record
Abstract
Infants are precocious word learners, and seem to possess systematic expectations about how words refer to object kinds. For example, while monolingual infants show a one-to-one mapping bias (e.g. mutual exclusivity), expecting each object to have only one basic level label, previous research has shown that this is less robust in bi- and multilinguals aged 1.5 years and older. This study examined the early origins of such one-to-one mapping biases by comparing monolingual and bilingual 9-10-month-olds' expectations about the relationship between labels and object kinds. In a violation of expectation paradigm, infants heard a speaker name hidden objects with either one label ('I see a mouba! I see a mouba!') or two labels ('I see a camo! I see a tenda!'). An occluder moved to reveal two objects that were either identical or of different kinds. Monolingual infants looked longest when two labels were associated with identical objects, and when one label was associated with objects of different kinds, showing that they found these outcomes unexpected. This replicated previous findings showing that monolinguals expect that distinct words label distinct object kinds (Dewar & Xu, ). Bilinguals looked equally to the outcomes regardless of the number of labels, showing no such expectations. This finding indicates that bilingualism influences young infants' expectations about how words refer to kinds, and more broadly supports the position that language experience contributes to the development of word learning heuristics.
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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) | 0.000 | 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 | 0.001 |
| 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".