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Pupillary Responses to Robotic and Human Emotions: The Uncanny Valley and Media Equation Confirmed

机译:小学生对机器人和人类情绪的反应:奇谷和媒体方程式得到确认

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摘要

Physiological responses during human–robots interaction are useful alternatives to subjective measures of uncanny feelings for nearly humanlike robots (uncanny valley) and comparable emotional responses between humans and robots (media equation). However, no studies have employed the easily accessible measure of pupillometry to confirm the uncanny valley and media equation hypotheses, evidence in favor of the existence of these hypotheses in interaction with emotional robots is scarce, and previous studies have not controlled for low level image statistics across robot appearances. We therefore recorded pupil size of 40 participants that viewed and rated pictures of robotic and human faces that expressed a variety of basic emotions. The robotic faces varied along the dimension of human likeness from cartoonish to humanlike. We strictly controlled for confounding factors by removing backgrounds, hair, and color, and by equalizing low level image statistics. After the presentation phase, participants indicated to what extent the robots appeared uncanny and humanlike, and whether they could imagine social interaction with the robots in real life situations. The results show that robots rated as nearly humanlike scored higher on uncanniness, scored lower on imagined social interaction, evoked weaker pupil dilations, and their emotional expressions were more difficult to recognize. Pupils dilated most strongly to negative expressions and the pattern of pupil responses across emotions was highly similar between robot and human stimuli. These results highlight the usefulness of pupillometry in emotion studies and robot design by confirming the uncanny valley and media equation hypotheses.
机译:人机交互过程中的生理反应是主观测量类似人的机器人(令人难以置信的山谷)的怪异感觉以及人与机器人之间可比的情感反应(媒体方程式)的有用替代方法。然而,尚无研究采用可轻易获得的瞳孔测量方法来确认异常谷和媒体方程假说,缺乏支持这些假说与情感机器人互动的证据,并且以前的研究并未控制低水平图像统计跨机器人外观。因此,我们记录了40名参与者的学生人数,这些参与者查看并评价了表示各种基本情绪的机器人和人脸的照片。机器人的面孔沿人类相似的维度变化,从卡通化到人类化。我们通过消除背景,头发和颜色,以及均衡低级图像统计数据,严格控制混杂因素。在演示阶段之后,与会人员指出了机器人在多大程度上显得异常和人性化,以及他们是否可以想象在现实生活中与机器人的社交互动。结果表明,被评定为接近类人的机器人在烦躁感上得分更高,在想象中的社交互动方面得分更低,诱发了较弱的瞳孔扩张,其情感表达更难以识别。学生对负表达的影响最大,而机器人和人类的刺激在情感上的瞳孔反应模式非常相似。这些结果通过证实了异常谷和媒体方程假设,凸显了瞳孔测量法在情感研究和机器人设计中的有用性。

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