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Record W4225279488 · doi:10.1111/lang.12501

Learning, Inside and Out: Prior Linguistic Knowledge and Learning Environment Impact Word Learning in Bilingual Individuals

2022· article· en· W4225279488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage Learning · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityCentre for Research on Brain Language and Music
Fundersnot available
KeywordsLexicalizationPsychologyMental lexiconLinguisticsSimilarity (geometry)Word learningLexiconArtificial intelligenceVocabularyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.292
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it