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Record W2809556473 · doi:10.1017/s1366728918000512

Bilingual exposure enhances left IFG specialization for language in children

2018· article· en· W2809556473 on OpenAlexaff
Maria M. Arredondo, Xiaosu Hu, ERICA SEIFERT, Teresa Satterfield, Ioulia Kovelman

Bibliographic record

VenueBilingualism Language and Cognition · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsLateralization of brain functionPsychologySyntaxLinguisticsNeuroscience of multilingualismGrammaticalityNeuroimagingVerbAgreementCognitive psychologyGrammarNeuroscience

Abstract

fetched live from OpenAlex

Language acquisition is characterized by progressive use of inflectional morphology marking verb tense and agreement. Linguistic milestones are also linked to left-brain lateralization for language specialization. We used neuroimaging (fNIRS) to investigate how bilingual exposure influences children's cortical organization for processing morpho-syntax. In Study 1, monolinguals and bilinguals (n = 39) completed a grammaticality judgment task that included English sentences with violations in earlier-acquired (verb agreement) and later-acquired (verb tense/agreement) structures. Groups showed similar performance and greater activation in left inferior frontal region (IFG) for later- than earlier-acquired conditions. Bilinguals showed stronger and more restricted left IFG activation. In Study 2, bilinguals completed a comparable Spanish task revealing patterns of left IFG activation similar to English. Taken together, the findings suggest that bilinguals with linguistic competence at parity with monolingual counterparts have a higher degree of cortical specialization for language, likely a result of enriched linguistic experiences.

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 score0.039
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 studies0.0000.000
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 head0.017
GPT teacher head0.294
Teacher spread0.278 · 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

Citations41
Published2018
Admission routes1
Has abstractyes

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