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Record W4399303345 · doi:10.1162/jocn_a_02190

Event-related Potential Measures of Visual Word Processing in Monolingual and Bilingual Children and Adults: A Focus on Word Frequency Effects

2024· article· en· W4399303345 on OpenAlexafffund
Alexander W. D. McArthur, Veronica Whitford, Marc F. Joanisse

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsWestern UniversityUniversity of New BrunswickUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec
KeywordsN400PsychologyLexical decision taskWord (group theory)Neural correlates of consciousnessWord lists by frequencyNeuroscience of multilingualismEvent-related potentialWord recognitionFocus (optics)AudiologyCognitive psychologyLinguisticsCognitionReading (process)

Abstract

fetched live from OpenAlex

How does language background influence the neural correlates of visual word recognition in children? To address this question, we used an ERP lexical decision task to examine first-language (L1) and second-language (L2) visual word processing in monolingual and bilingual school-aged children and young adults (n = 123). In particular, we focused on the effects of word frequency (an index of lexical accessibility) on RTs and the N400 ERP component. Behaviorally, we found larger L1 versus L2 word frequency effects among bilingual children, driven by faster and more accurate responses to higher-frequency words (no other language or age group differences were observed). Neurophysiologically, we found larger L1 word frequency effects in bilinguals versus monolinguals (across both age groups), reflected in more negative ERP amplitudes to lower-frequency words. However, the bilingual groups processed L1 and L2 words similarly, despite lower levels of subjective and objective L2 proficiency. Taken together, our findings suggest that divided L1 experience (but not L2 experience) influences the neural correlates of visual word recognition across childhood and adulthood.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.316
Teacher spread0.305 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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