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Record W4391361578 · doi:10.1080/23273798.2024.2307630

Effects of social interactions on the neural representation of emotional words in late bilinguals

2024· article· en· W4391361578 on OpenAlexaff
Chunlin Liu, Hyeonjeong Jeong, Haining Cui, Jean‐Marc Dewaele, Kiyo Okamoto, Yuichi Suzuki, Motoaki Sugiura

Bibliographic record

VenueLanguage Cognition and Neuroscience · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
FundersJapan Society for the Promotion of Science
KeywordsPsychologyEmotionalityCognitive psychologyNeural correlates of consciousnessDevelopmental psychologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

This fMRI study explored the relationship between social interactions and neural representations of emotionality in a foreign language (LX). Forty-five late learners of Japanese performed an auditory Japanese lexical decision task involving positive and negative words. The intensity of their social interactions with native Japanese speakers was measured using the Study Abroad Social Interaction Questionnaire. Activity in the left ventral striatum significantly correlated with social interaction intensity for positive words, while the right amygdala showed a significant correlation for negative words. These results indicate neural representations of LX emotional words link with the intensity of social interactions. Furthermore, LX negative words activated the left inferior frontal gyrus more than positive and neutral words, suggesting greater cognitive effort for processing negative words, aligning with a bias in adult social interactions towards more positively-valenced language. Overall, our findings underscore the importance of social interaction experiences in the processing of LX emotional words.

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 score0.002
Threshold uncertainty score0.005

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.348
Teacher spread0.309 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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