人工智能
面子(社会学概念)
计算机视觉
计算机科学
心理学
眼动
感知
人工神经网络
认知心理学
特征(语言学)
面部识别系统
作者
Zenggen Ren,Fu Guo,Mingming Li,Chen Fang
标识
DOI:10.1080/10447318.2026.2633204
摘要
This study examines how two facial attributes of robots—the facial width-to-height ratio (fWHR) and eye shape—influence users’ judgments of trustworthiness, and how these features are processed at the neural level. To achieve this goal, we conducted a within-subject Electroencephalogram (EEG) experiment using a 2 (fWHR: low/high) × 3 (eye shape: round/rectangular/obround) full factorial design. EEG signals were analyzed using event-related potentials (ERPs). Results reveal that eye shape significantly influences trustworthiness, with round and obround eyes rated higher than rectangular ones. High fWHR robots with round or obround eyes elicited more negative N1 and N170 amplitudes, while round eyes reduced P3 amplitudes compared to other shapes. These findings not only clarify the neural mechanisms underlying the evaluation of robotic facial cues but also provide practical insights for designing socially trustworthy robots.
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