The effects of referential continuity on novel word learning in bilingual and monolingual preschoolers
Bibliographic record
Abstract
• We tested monolingual and bilingual three- and four-year-olds in a word learning task. • Words were taught in either a referentially continuous or discontinuous context. • Contrary to previous work, children showed robust word learning in both contexts. • Children were more accurate at test for words they learned in discontinuous contexts. Previous research suggests that monolingual children learn words more readily in contexts with referential continuity (i.e., repeated labeling of the same referent) than in contexts with referential discontinuity (i.e., referent switches). Here, we extended this work by testing monolingual and bilingual 3- and 4-year-olds’ ( N = 64) novel word learning in an interactive tablet-based task. We predicted that bilinguals’ experience with language switches would buffer them against the attested challenges of referent switches on word learning. Unexpectedly, we found that monolinguals and bilinguals readily learned words in contexts of both referential continuity and referential discontinuity, and if anything performance was better in the referential discontinuity context. Overall, these results indicate that, at least for some learners under some conditions, referential discontinuity does not disrupt word learning. Our findings invite future research into understanding how and when referential continuity affects language acquisition.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".