Sato Wataru, Namba Shushi, Yang Dongsheng, Nishida Shin'ya, Ishi Carlos, Minato Takashi
Psychological Process Research Team, Guardian Robot Project, RIKEN, Kyoto, Japan.
Field Science Education and Research Center, Kyoto University, Kyoto, Japan.
Front Psychol. 2022 Feb 4;12:800657. doi: 10.3389/fpsyg.2021.800657. eCollection 2021.
Android robots capable of emotional interactions with humans have considerable potential for application to research. While several studies developed androids that can exhibit human-like emotional facial expressions, few have empirically validated androids' facial expressions. To investigate this issue, we developed an android head called Nikola based on human psychology and conducted three studies to test the validity of its facial expressions. In Study 1, Nikola produced single facial actions, which were evaluated in accordance with the Facial Action Coding System. The results showed that 17 action units were appropriately produced. In Study 2, Nikola produced the prototypical facial expressions for six basic emotions (anger, disgust, fear, happiness, sadness, and surprise), and naïve participants labeled photographs of the expressions. The recognition accuracy of all emotions was higher than chance level. In Study 3, Nikola produced dynamic facial expressions for six basic emotions at four different speeds, and naïve participants evaluated the naturalness of the speed of each expression. The effect of speed differed across emotions, as in previous studies of human expressions. These data validate the spatial and temporal patterns of Nikola's emotional facial expressions, and suggest that it may be useful for future psychological studies and real-life applications.
能够与人类进行情感互动的安卓机器人在研究应用方面具有相当大的潜力。虽然有几项研究开发出了能够展现类人情感面部表情的安卓机器人，但很少有研究对安卓机器人的面部表情进行实证验证。为了研究这个问题，我们基于人类心理学开发了一个名为尼古拉的安卓机器人头部，并进行了三项研究来测试其面部表情的有效性。在研究1中，尼古拉做出单个面部动作，并根据面部动作编码系统进行评估。结果表明，它能恰当地做出17个动作单元。在研究2中，尼古拉做出六种基本情绪（愤怒、厌恶、恐惧、快乐、悲伤和惊讶）的典型面部表情，然后让没有经验的参与者对这些表情的照片进行标注。所有情绪的识别准确率都高于随机水平。在研究3中，尼古拉以四种不同速度做出六种基本情绪的动态面部表情，然后让没有经验的参与者评估每种表情速度的自然程度。与之前关于人类表情的研究一样，速度的影响因情绪而异。这些数据验证了尼古拉情感面部表情的时空模式，并表明它可能对未来的心理学研究和实际应用有用。
