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Record W2804676276 · doi:10.1017/s1366728918000111

Bilingual lexical access: A dynamic operation modulated by word-status and individual differences in inhibitory control

2018· article· en· W2804676276 on OpenAlexafffund
Aruna Sudarshan, Shari R. Baum

Bibliographic record

VenueBilingualism Language and Cognition · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and Music
FundersCentre for Research on Brain, Language and MusicMcGill University
KeywordsCognateLexical decision taskPsychologyLinguisticsLexical accessInhibitory controlTask (project management)Selection (genetic algorithm)Neuroscience of multilingualismControl (management)Word (group theory)Cognitive psychologyComputer scienceArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

A question central to bilingualism research is whether representations from the contextually inappropriate language compete for lexical selection during language production. It has been argued recently that the extent of interference from the non-target language may be contingent on a host of factors. In two studies, we investigated whether factors such as word-type and individual differences in inhibitory control capacities influence lexical selection via a cross-modal picture-word interference task and a non-linguistic Simon task. Highly proficient French–English bilinguals named non-cognate and cognate target pictures in L2 (English) while ignoring auditory distractors in L1 (French) and L2. Taken together, our results demonstrated that lexical representations from L1 are active and compete for selection when naming in L2, even in highly proficient bilinguals. However, the extent of cross-language activation was modulated by both word-type and individual differences in inhibitory control capacities.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.299
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2018
Admission routes2
Has abstractyes

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