Cultural differences in on-line sensitivity to emotional voices: comparing East and West
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".