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Record W4416426744 · doi:10.1016/j.dcn.2025.101645

Language exposure predicts infants’ neural processing of others’ actions based on language group

2025· article· en· W4416426744 on OpenAlexaff
Yiyi Wang, Marc Colomer, Hyesung G. Hwang, Enda Tan, Nathan A. Fox, Amanda L. Woodward

Bibliographic record

VenueDevelopmental Cognitive Neuroscience · 2025
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsNeural correlates of consciousnessAction (physics)ElectroencephalographyNeural activityFirst languageForeign language

Abstract

fetched live from OpenAlex

What language a person speaks has been shown to divide even infants' worlds. However, open questions remain about what neural processes are involved in the differentiation of native and foreign speakers in the infant's brain. This study used electroencephalography (EEG) to examine the neural responses related to top-down attention (frontal theta synchronization), action processing (mu desynchronization), and approach-avoidance (frontal alpha asymmetry) of 8- to 12-month-old infants as they observed a native (English) speaker and a foreign (French) speaker perform a goal-directed action (i.e., grasping objects). We further examined whether infants' language exposure modulated these neural responses. We found that monolingual infants exhibited stronger mu desynchronization when observing a native (versus foreign) speaker perform goal-directed actions. In contrast, non-monolingual (i.e., hearing more than one language) infants did not show a difference in mu desynchronization between native and foreign speakers. No language group and exposure effects were found for frontal theta and frontal alpha symmetry. These results suggest that infants' emerging differentiation of native and foreign speakers is also manifested in their neural processing of goal-directed actions and that this neural action processing is shaped by early exposure to different languages.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.335
Teacher spread0.297 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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