Head movements encode emotions during speech and song.
Bibliographic record
Abstract
When speaking or singing, vocalists often move their heads in an expressive fashion, yet the influence of emotion on vocalists' head motion is unknown. Using a comparative speech/song task, we examined whether vocalists' intended emotions influence head movements and whether those movements influence the perceived emotion. In Experiment 1, vocalists were recorded with motion capture while speaking and singing each statement with different emotional intentions (very happy, happy, neutral, sad, very sad). Functional data analyses showed that head movements differed in translational and rotational displacement across emotional intentions, yet were similar across speech and song, transcending differences in F0 (varied freely in speech, fixed in song) and lexical variability. Head motion specific to emotional state occurred before and after vocalizations, as well as during sound production, confirming that some aspects of movement were not simply a by-product of sound production. In Experiment 2, observers accurately identified vocalists' intended emotion on the basis of silent, face-occluded videos of head movements during speech and song. These results provide the first evidence that head movements encode a vocalist's emotional intent and that observers decode emotional information from these movements. We discuss implications for models of head motion during vocalizations and applied outcomes in social robotics and automated emotion recognition.
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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".