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Record W1519761837 · doi:10.3389/fnhum.2015.00311

Cultural differences in on-line sensitivity to emotional voices: comparing East and West

2015· article· en· W1519761837 on OpenAlexafffund
Pan Liu, Simon Rigoulot, Marc D. Pell

Bibliographic record

VenueFrontiers in Human Neuroscience · 2015
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsInternational Laboratory for Brain, Music and Sound ResearchMcGill UniversityCentre for Research on Brain Language and Music
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyEmotional expressionPerceptionNeurocognitiveCognitive psychologyTask (project management)Facial expressionFace (sociological concept)Mismatch negativityElectroencephalographyCommunicationCognitionLinguisticsNeuroscience

Abstract

fetched live from OpenAlex

Evidence that culture modulates on-line neural responses to the emotional meanings encoded by vocal and facial expressions was demonstrated recently in a study comparing English North Americans and Chinese (Liu et al., 2015). Here, we compared how individuals from these two cultures passively respond to emotional cues from faces and voices using an Oddball task. Participants viewed in-group emotional faces, with or without simultaneous vocal expressions, while performing a face-irrelevant visual task as the EEG was recorded. A significantly larger visual Mismatch Negativity (vMMN) was observed for Chinese vs. English participants when faces were accompanied by voices, suggesting that Chinese were influenced to a larger extent by task-irrelevant vocal cues. These data highlight further differences in how adults from East Asian vs. Western cultures process socio-emotional cues, arguing that distinct cultural practices in communication (e.g., display rules) shape neurocognitive activity associated with the early perception and integration of multi-sensory emotional cues.

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 score0.007
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
Bibliometrics0.0000.000
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 head0.266
GPT teacher head0.379
Teacher spread0.113 · 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

Citations32
Published2015
Admission routes2
Has abstractyes

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