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An fMRI study with semantic access in low proficiency second language learners

2004· article· en· W2114569540 on OpenAlexaff
Gui Xue, Qi Dong, Zhen Jin, Lei Zhang, Yue Wang

Bibliographic record

VenueNeuroreport · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyNeural correlates of consciousnessSemantic memorySecond languageBrain activity and meditationFunctional magnetic resonance imagingNeuroscience of multilingualismTask (project management)Brain mappingLanguage proficiencyCognitive psychologyCognitionNeuroscienceLinguisticsElectroencephalography

Abstract

fetched live from OpenAlex

Brain activity was measured with fMRI in twelve 10- to 12-year-old Chinese children who began learning English when they were 8 years old in order to find out whether there is a common or a distinct neural semantic system for native language (L1) and second language (L2) in low proficiency bilingual subjects. Although they performed less well in L2 in the semantic decision task administered during fMRI measurement, these subjects showed similar robust activation, for both languages, in brain areas involved in semantic processing (e.g. the left inferior frontal cortex). Within-subject and group analyses revealed no significant difference in the activation patterns for L1 and L2 in these regions. These results suggest that at least at single-word level, there are shared neural substrates for semantic processing of L1 and L2 even when one is at a very low L2 proficiency level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.330
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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

Citations73
Published2004
Admission routes1
Has abstractyes

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