Evaluating Users’ Auditory Affective Preference for Humanoid Robot Voices through Neural Dynamics

Abstract

Users’ affective preference for voices has become a topic of great interest with the prevalence of humanoid robots. Nevertheless, the affective preference formation for humanoid voices remains unknown, and its evaluation lacks objective methods. Consequently, we conducted an EEG experiment to unravel the underlying neural dynamics and evaluate users’ affective preference for humanoid robot voices. Significantly larger P2, P3, and LPP amplitudes, enhanced theta, and decreased alpha oscillations were observed when users affectively preferred humanoid robot voices. The results suggest that the neural dynamics underlying users’ affective preference for humanoid robot voices might primarily consist of early detection of affective information in voices, further processing of affective information, and later evaluative categorization of affective preference. Moreover, the neural indicators could distinguish users’ affective preferences for humanoid robot voices. The study contributes to understanding the auditory affective preference formation for humanoid robot voices and providing a neurological evaluation method.