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Record W4417187740 · doi:10.1017/s1366728925100886

The paradoxical associations between language and executive control in monolinguals and bilinguals

2025· article· en· W4417187740 on OpenAlexafffund
Victor A. Sanchez-Azanza, Daniel Adrover‐Roig, Tanya Dash, Ana Inés Ansaldo

Bibliographic record

VenueBilingualism Language and Cognition · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalUniversity of Alberta
FundersFonds de Recherche du Québec - SantéRéseau en Bio-Imagerie du Quebec
KeywordsNeuroscience of multilingualismStroop effectCognitionContext (archaeology)Age of AcquisitionControl (management)Task (project management)Second language

Abstract

fetched live from OpenAlex

Abstract This study investigated whether differences in executive control exist between bilinguals and monolinguals who share a dual-language context. We compared functional monolingual and bilingual groups’ cognitive performance and the correlation between self-reported and objective linguistic variables and cognitive outcomes. Group comparisons revealed no significant differences between functional monolinguals and bilinguals on inhibition, task switching and updating of information. However, distinct correlational patterns were observed within groups. In functional monolinguals, participants with lower bilingualism scores showed better task-specific inhibition (Color–Word part of the Stroop task) and a better ability to monitor for conflicts (Digits Forward task). In contrast, bilinguals with higher degrees of bilingualism showed better overall inhibition outcomes (Stroop effect). Findings are discussed in terms of the importance of adopting more comprehensive methodological approaches to study bilingualism as a heterogeneous phenomenon, considering the diversity within each group and the cultural and linguistic context in which the bilingual experience takes place.

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)0.003
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 score0.190
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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