Putting Mutual Exclusivity in Context: Speaker Race Influences Monolingual and Bilingual Infants’ Word-Learning Assumptions
Bibliographic record
Abstract
Previous work indicates mutual exclusivity in word learning in monolingual, but not bilingual toddlers. We asked whether this difference indicates distinct conceptual biases, or instead reflects best-guess heuristic use in the absence of context. We altered word-learning contexts by manipulating whether a familiar- or unfamiliar-race speaker introduced a novel word for an object with a known category label painted in a new color. Both monolingual and bilingual infants showed mutual exclusivity for a familiar-race speaker, and relaxed mutual exclusivity and treated the novel word as a category label for an unfamiliar-race speaker. Thus, monolingual and bilingual infants have access to similar word-learning heuristics, and both use nonlinguistic social context to guide their use of the most appropriate heuristic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".