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Assessment of human response to robot facial expressions through visual evoked potentials

机译:通过视觉诱发电位评估人类对机器人面部表情的反应

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The focus of this work is to investigate and quantify the ability of a humanoid ‘hybrid face’ robot to effectively convey emotion to a human observer by mapping their physiological (EEG) response to perceived emotional information. Specifically, we examine the event related response during two implicit emotion recognition experiments to determine the modulation of the face-specific N170 brain response component to robot facial expressions. EEG recordings were taken from a range of test subjects observing the BERT2 robot cycle through a range of facial emotions in each emotion recognition experiment. Results from both experiments demonstrate that the stimuli evoke the N170 component and that digital facial expressions with high correlations can be discriminated. Emotional expressions evoke a larger response relative to neutral stimuli, with negative evoking an increased amplitude and latency to positive emotions, and demonstrate that the response to robot facial expressions evoke similar brain activity to that of a human emotions. This study is the first of its nature to investigate and quantify the human physiological response to digital facial expressions as conveyed in real-time by a humanoid robot.
机译:这项工作的重点是调查和量化类人“混合脸”机器人通过将其生理(EEG)响应映射到感知到的情绪信息来将情绪有效传达给人类观察者的能力。具体来说,我们在两个隐式情感识别实验中检查了与事件相关的响应,以确定特定于脸的N170脑响应组件对机器人面部表情的调制方式。在每个情绪识别实验中,通过观察一系列BERT机器人的一系列面部表情,从观察BERT2机器人周期的一系列测试对象中获取EEG记录。来自两个实验的结果表明,该刺激唤起了N170成分,并且可以区分具有高度相关性的数字面部表情。情绪表达引起相对于中性刺激更大的响应,而负面表达则引起对积极情绪的幅度和潜伏期增加,并表明对机器人面部表情的响应引起了与人类情绪相似的大脑活动。这项研究是调查和量化人形机器人实时传达的对数字面部表情的人类生理反应的性质的第一项研究。

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