Singing emotionally: a study of pre-production, production, and post-production facial expressions
Bibliographic record
Abstract
Singing involves vocal production accompanied by a dynamic and meaningful use of facial expressions, which may serve as ancillary gestures that complement, disambiguate, or reinforce the acoustic signal. In this investigation, we examined the use of facial movements to communicate emotion, focusing on movements arising in three epochs: before vocalization (pre-production), during vocalization (production), and immediately after vocalization (post-production). The stimuli were recordings of seven vocalists' facial movements as they sang short (14 syllable) melodic phrases with the intention of communicating happiness, sadness, irritation, or no emotion. Facial movements were presented as point-light displays to 16 observers who judged the emotion conveyed. Experiment 1 revealed that the accuracy of emotional judgment varied with singer, emotion, and epoch. Accuracy was highest in the production epoch, however, happiness was well communicated in the pre-production epoch. In Experiment 2, observers judged point-light displays of exaggerated movements. The ratings suggested that the extent of facial and head movements was largely perceived as a gauge of emotional arousal. In Experiment 3, observers rated point-light displays of scrambled movements. Configural information was removed in these stimuli but velocity and acceleration were retained. Exaggerated scrambled movements were likely to be associated with happiness or irritation whereas unexaggerated scrambled movements were more likely to be identified as "neutral." An analysis of singers' facial movements revealed systematic changes as a function of the emotional intentions of singers. The findings confirm the central role of facial expressions in vocal emotional communication, and highlight individual differences between singers in the amount and intelligibility of facial movements made before, during, and after vocalization.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".