Department of Psychology, North Dakota State University, Fargo, ND, 58102, USA.
Sci Rep. 2021 Sep 9;11(1):17890. doi: 10.1038/s41598-021-97527-6.
Face recognition is supported by selective neural mechanisms that are sensitive to various aspects of facial appearance. These include event-related potential (ERP) components like the P100 and the N170 which exhibit different patterns of selectivity for various aspects of facial appearance. Examining the boundary between faces and non-faces using these responses is one way to develop a more robust understanding of the representation of faces in extrastriate cortex and determine what critical properties an image must possess to be considered face-like. Robot faces are a particularly interesting stimulus class to examine because they can differ markedly from human faces in terms of shape, surface properties, and the configuration of facial features, but are also interpreted as social agents in a range of settings. In the current study, we thus chose to investigate how ERP responses to robot faces may differ from the response to human faces and non-face objects. In two experiments, we examined how the P100 and N170 responded to human faces, robot faces, and non-face objects (clocks). In Experiment 1, we found that robot faces elicit intermediate responses from face-sensitive components relative to non-face objects (clocks) and both real human faces and artificial human faces (computer-generated faces and dolls). These results suggest that while human-like inanimate faces (CG faces and dolls) are processed much like real faces, robot faces are dissimilar enough to human faces to be processed differently. In Experiment 2 we found that the face inversion effect was only partly evident in robot faces. We conclude that robot faces are an intermediate stimulus class that offers insight into the perceptual and cognitive factors that affect how social agents are identified and categorized.
人脸识别是由选择性神经机制支持的，这些机制对脸部外观的各个方面都很敏感。这些机制包括事件相关电位 (ERP) 成分，如 P100 和 N170，它们对脸部外观的各个方面表现出不同的选择性模式。使用这些反应来检查脸部和非脸部之间的边界是一种发展对大脑中负责处理脸部信息的区域的功能有更深入理解的方法，并确定图像必须具备哪些关键属性才能被视为类似人脸的。机器人脸是一个特别有趣的刺激类别，因为它们在形状、表面属性和面部特征的配置方面与人类面孔有很大的不同，但在各种环境中也被解释为社交代理。在当前的研究中，我们选择研究机器人脸的 ERP 反应可能与人类脸和非人脸物体的反应有何不同。在两个实验中，我们研究了 P100 和 N170 对人类脸、机器人脸和非人脸物体（时钟）的反应。在实验 1 中，我们发现相对于非人脸物体（时钟）以及真实人类面孔和人工人类面孔（计算机生成的面孔和玩偶），机器人脸会引起与面部敏感成分相关的中等反应。这些结果表明，虽然类人无生命的面孔（CG 面孔和玩偶）与真实面孔的处理方式非常相似，但机器人面孔与人类面孔的差异足以使其被不同地处理。在实验 2 中，我们发现面部反转效应在机器人面孔中仅部分明显。我们的结论是，机器人脸是一个中间刺激类别，可以深入了解影响如何识别和分类社交代理的感知和认知因素。
