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Record W2996589984 · doi:10.1017/s1366728919000816

Monolinguals and bilinguals respond differently to a delayed matching-to-sample task: An ERP study

2019· article· en· W2996589984 on OpenAlexaff
Cassandra Morrison, Farooq Kamal, Kim Le, Vanessa Taler

Bibliographic record

VenueBilingualism Language and Cognition · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBruyèreUniversity of Ottawa
Fundersnot available
KeywordsP3bPsychologyNeuroscience of multilingualismTask (project management)Event-related potentialNeuropsychologyCognitionCognitive psychologyWorking memoryMatching (statistics)Brain activity and meditationAudiologyElectroencephalographyNeuroscience

Abstract

fetched live from OpenAlex

Abstract Previous research examining whether bilinguals exhibit enhanced working memory (WM) compared to monolinguals has yielded mixed results. This inconsistency may be due to lack of sensitivity in behavioral and neuropsychological measures. The current study aimed to investigate the effects of bilingualism on WM by focusing on brain activity patterns (event-related potentials) in monolinguals and bilinguals during a WM task. We recorded brain activity while participants (26 monolingual English speakers and 28 English–French bilinguals) performed a delayed matching-to-sample task. Although performance measures were similar, electrophysiological differences were present across groups. Bilinguals exhibited larger P3b amplitudes than monolinguals, and smaller negative slow wave and N2b amplitudes during retrieval. These results suggest that bilinguals may have more cognitive resources available in WM to allocate to task completion, and that task completion may be less effortful for bilinguals than for monolinguals.

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)0.000
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 score0.222
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.068
GPT teacher head0.386
Teacher spread0.317 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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