More than accuracy: Nonverbal dialects modulate the time course of vocal emotion recognition across cultures.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it