More than accuracy: Nonverbal dialects modulate the time course of vocal emotion recognition across cultures.
Bibliographic record
Abstract
Using a gating paradigm, this study investigated the nature of the in-group advantage in vocal emotion recognition by comparing 2 distinct cultures. Pseudoutterances conveying 4 basic emotions, expressed in English and Hindi, were presented to English and Hindi listeners. In addition to hearing full utterances, each stimulus was gated from its onset to construct 5 processing intervals to pinpoint when the in-group advantage emerges, and whether this differs when listening to a foreign language (English participants judging Hindi) or a second language (Hindi participants judging English). An index of the mean emotion identification point for each group and unbiased measures of accuracy at each time point was calculated. Results showed that in each language condition, native listeners were faster and more accurate than non-native listeners to recognize emotions. The in-group advantage emerged in both conditions after processing 400 ms to 500 ms of acoustic information. In the bilingual Hindi group, greater oral proficiency in English predicted faster and more accurate recognition of English emotional expressions. Consistent with dialect theory, our findings provide new evidence that nonverbal dialects impede both the accuracy and the efficiency of vocal emotion processing in cross-cultural settings, even when individuals are highly proficient in the out-group target language.
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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 | 0.002 |
| 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 | 0.001 | 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".