Word learning in 14-month-old monolinguals and bilinguals: Challenges and methodological opportunities
Bibliographic record
Abstract
Infants can learn words in their daily interactions early in life, and many studies have demonstrated that they can also learn words from brief in-lab exposures. While most studies have included monolingual infants, less is known about bilingual infants’ word learning and the role that language familiarity plays in this ability. In this study we examined word learning in a large sample (up to N = 155) of bilingual and monolingual 14-month-olds using a preferential looking paradigm. To support word learning, novel words were presented within sentence frames in one language (single-language condition) or two languages (dual-language condition). We predicted that infants would exhibit greater word–object learning when they were more familiar with the language of the sentence frame. Using both traditional (t-tests) and updated (linear mixed-effects models) analyses, we found no evidence for successful word learning, nor an effect of familiarity. Our results suggest that word learning in experimental settings can be challenging for 14-month-olds, even when sentence frames are provided. We discuss these results in relation to prior work and suggest how open science practices can contribute to more reliable findings about early word 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".