How Robots Influence Human Perception: Investigating the Role of Body Language and Music in Emotion Perception for Social HRI
Bibliographic record
Abstract
Emotional dance is an engaging and stimulating multimodal social activity involving the display of both body language and music. An interesting area of research is in the investigation of how people perceive the emotions of robots. In particular, how the interaction between several modalities influences emotion perception in human-robot interactions (HRI). In this paper, we present the first study that investigates how robot body movements and music influence human emotion perception with respect to robot emotional dance. Through an online survey, 115 participants rated the emotion expressed by the dancing robot with varying body movements and music in two conditions: 1) robot dancing with music (visual + auditory) and 2) robot dancing without music (only visual). Our results showed that perceived valence is primarily influenced by robot body language and movements, especially with respect to positive valence, regardless of the presence of the music mode. Women also had higher perceived negative valence when observing the negative valence body movements displayed by the robot than men did. Furthermore, music had limited influence on the perception of: 1) valence when negative valence body language is displayed while the music had positive valence, and 2) arousal when the music had negative arousal. Our study provides insights on how to effectively design social HRI when considering human emotional perception of robots.
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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 | 0.000 | 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 | 0.000 |
| 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".