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Crossmodal Interactions in Human-Robot Communication: Exploring the Influences of Scent and Voice Congruence on User Perceptions of Social Robots

Fangyuan Chang, Bing Chen, Lin Sheng, Dian Zhu, Jianan Zhao, Zhenyu Gu

Year
2025
Citations
1

Abstract

Olfactory stimuli have demonstrated the potential to evoke emotional depth and enhance user experiences in HCI. Yet, their role in shaping perceptions of social robots remains largely untapped. This study investigates how olfactory (scent) and auditory (voice) stimuli influence user perceptions of social robots. Using a 2x2 between-subjects design, participants interacted with a social robot under conditions with pleasant/unpleasant scents and friendly/unfriendly voices. The study measured perceived trust, friendliness, competence, and engagement. Our findings show that pleasant scents can enhance the perceptions of friendliness and engagement, while friendly voices can improve trust, friendliness, and engagement. The congruent combination of scents and voices affects friendliness and engagement but does not influence trust and competence. This study contributes to the growing work on multi-sensory Human-Robot Interaction (HRI) design, offering implications for creating more socially interactive robots.

Keywords

CrossmodalCongruence (geometry)RobotPerceptionHuman–robot interactionHuman–computer interactionComputer scienceSocial robotPsychologyCognitive psychology

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