Learning, Inside and Out: Prior Linguistic Knowledge and Learning Environment Impact Word Learning in Bilingual Individuals
Bibliographic record
Abstract
Abstract Although several studies have focused on novel word learning and lexicalization in (presumably) monolingual speakers, less is known about how bilinguals add novel words to their mental lexicon. In this study we trained 33 English–French bilinguals on novel word‐forms that were neighbors to English words with no existing neighbors. The number of novel neighbors to each English word varied, as did the cross‐linguistic orthographic overlap between the English word and its French translation. We assessed episodic memory and lexicalization of the novel words before and after a consolidation period. Cross‐linguistic similarity enhanced episodic memory of novel neighbors only when neighborhood density among the novel neighbors was low. We also found evidence that novel neighbors of English words with high cross‐linguistic similarity became lexicalized after a consolidation period. Overall, the results suggest that similarity to preexisting lexical representations crucially impacted lexicalization of novel words by bilingual individuals.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".