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Record W4393276447 · doi:10.1111/mbe.12410

What's in the Sound? Common and Language‐Specific Patterns in Brain Activation and Functional Connectivity for Phonological Awareness in <scp>Spanish–English</scp> Bilinguals

2024· article· en· W4393276447 on OpenAlexaff
Nia Nickerson, Xin Sun, Valeria C. Caruso, Kehui Zhang, Chi‐Lin Yu, Rachel L. Eggleston, Natasha Chaku, Xiaosu Hu, Teresa Satterfield, Ioulia Kovelman

Bibliographic record

VenueMind Brain and Education · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthUniversity of Michigan
KeywordsPsychologyNeuroscience of multilingualismPhonologyLinguisticsContext (archaeology)Heritage languagePhonological awarenessLiteracyReading (process)MultilingualismCognitive psychologyFirst language

Abstract

fetched live from OpenAlex

Abstract Phonological awareness is the stepping‐stone to learning to read as it helps children map language sounds onto letters. Theories of bilingualism posit that phonological awareness is a language‐common literacy skill. However, bilingual learners are also thought to build language‐specific representations. To illuminate common and specific dual‐language processes, we asked bilingual Spanish–English heritage language speakers ( N = 60, M age = 8.2) to complete a phonological sound‐matching task in Spanish and English during functional Near Infrared Neuroimaging (fNIRS). The left perisylvian activation was common across bilinguals' two languages, including similar active regions and functional connections. The findings further revealed language‐specific modulation of the system with more robust engagement of the temporal networks for Spanish and frontal networks for English. We interpret the results in the context of analytically demanding reading experiences in English and more informal home‐based Spanish language experiences typical of heritage language speakers.

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)0.000
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 score0.559
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
GPT teacher head0.343
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 routes1
Has abstractyes

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