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Record W4213440335 · doi:10.1017/s1366728922000049

The dynamics of spoken word recognition in bilinguals

2022· article· en· W4213440335 on OpenAlexafffund
Amy S. Desroches, Deanna C. Friesen, Matthew Teles, Chloe A. Korade, Evan W. Forest

Bibliographic record

VenueBilingualism Language and Cognition · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of ManitobaWestern UniversityUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationUniversity of Winnipeg
KeywordsTask (project management)LinguisticsN400Word (group theory)PsychologyNeuroscience of multilingualismSecond languageComputer scienceEvent-related potentialCognition

Abstract

fetched live from OpenAlex

Abstract This study examined whether bilinguals automatically activate lexical options from both of their languages when performing a picture matching task in their dominant language (L1) by using event related potentials. English–French bilinguals and English monolinguals performed a picture-spoken word matching task with three conditions: match (BEACH-“beach”), unrelated mismatch (BEACH-“tack”), and L2 onset competitor mismatch (BEACH-“plaid”; plaid sounds like plage , the French word for beach ). Critically, bilinguals, but not monolinguals, showed reduced N400s for L2-cohort vs. unrelated mismatches. The results provide clear evidence that when bilinguals identify pictures, they automatically activate lexical options from both languages, even when expecting oral input from only their dominant language. N400 attenuation suggests bilinguals activate but do not expect L2 lexical options.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.401
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2022
Admission routes2
Has abstractyes

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