Norm-based face space model revisited: The role of familiarity, distance-to-norm and vector direction

重新审视基于范数的人脸空间模型：熟悉度、范数距离和矢量方向的作用

• 批准号: 317318972
• 负责人: Dr. Stella J. Faerber
• 金额: --
• 依托单位: Abteilung für Allgemeine Psychologie I
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/317318972?language=en
• 关键词: Norm; based; face; space; model

The evaluation of a face regarding its age, likability, expressed emotion or attractiveness occurs in relation to other faces, which have been encountered previously. Thus, such judgments heavily depend on the relation between mental representations of faces. According to the influential norm-based multidimensional face space model (nMDFS, Valentine, 1991) these representations are located around a central norm or prototype, with the distance to the norm defining face typicality. A face's location is uniquely defined by a vector from the norm to its point in space, and thus by a combination of vector length (distance-to-norm, DTN) and vector direction (VD). A vast part of the face perception literature is based on assumptions of this model (for instance, adaptation studies and identity aftereffects in particular are typically interpreted in the context of nMDFS).Interestingly, the role of familiarity for mental face representations has been neglected in the context of nMDFS. Because recognition studies suggest that representations for familiar faces are qualitatively different (more robust and image-independent) than those for unfamiliar ones (Burton, Jenkins, & Schweinberger, 2011), we predicted that familiar faces would be perceived as less typical than unfamiliar ones, and confirmed this in a pilot-study. In Study 1, we will investigate the combined roles of familiarity and DTN on face representations in nMDFS, and assess face-sensitive EEG components which reflect familiarity (N250) and DTN (P200). In Study 2, we will assess the role of DTN of familiar faces compared to unfamiliar faces via similarity ratings in nMDFS.While DTN may predict the typicality of unfamiliar faces, other attributes such as the attractiveness of a face may be defined by both DTN and VD: Evidence suggests that typicality can predict attractiveness to some degree, but at the same time attractiveness can be increased by VD, for example, enhanced sexual dimorphism (Little, Jones, & DeBruine, 2011). Aiming at a further development and specification of the nMDFS, we will investigate neuro-physiological correlates of DTN and VD on the example of attractiveness (Study 3), which has the potential of clarifying controversial results of previous research on facial attractiveness.Using both advanced methods of image processing and electrophysiological recordings of brain activity, the present project aims at a systematic investigation of the behavioural and neurocognitive mechanisms underlying face perception in the context of nMDFS. We expect that this project will (1) improve our understanding of how faces are mentally represented and (2) further uncover the neurophysiological basis of familiar and unfamiliar face representations.

对一张脸的年龄、可爱性、表达的情感或吸引力的评估是与之前遇到的其他脸相关的。因此，这种判断在很大程度上取决于面部心理表征之间的关系。根据有影响力的规范为基础的多维人脸空间模型（nMDFS，情人节，1991年），这些表示位于一个中心规范或原型，与规范的距离定义的脸典型性。人脸的位置由从范数到其空间点的向量唯一定义，因此由向量长度（距离范数，DTN）和向量方向（VD）的组合定义。 很大一部分的面孔知觉文献是基于这个模型的假设（例如，适应性研究和身份后效，特别是典型的nMDFS的上下文中解释）。有趣的是，熟悉的心理面孔表征的作用被忽视的nMDFS的上下文中。由于识别研究表明，熟悉面孔的表征与不熟悉面孔的表征有质的不同（更强大和独立于图像）（Burton，Jenkins，& Schweinberger，2011），我们预测熟悉的面孔将被视为不像不熟悉的面孔那样典型，并在试点研究中证实了这一点。在研究1中，我们将研究熟悉度和DTN对nMDFS中面孔表征的联合作用，并评估反映熟悉度（N250）和DTN（P200）的面孔敏感EEG成分。在研究2中，我们将通过nMDFS中的相似性评级来评估熟悉面孔与不熟悉面孔相比的DTN的作用。虽然DTN可以预测不熟悉面孔的典型性，但其他属性（如面孔的吸引力）可以由DTN和VD定义：有证据表明，典型性在某种程度上可以预测吸引力，但同时吸引力也可以通过VD来增加，例如，增强性二态性（Little，Jones，& DeBruine，2011）。为了进一步发展和规范nMDFS，我们将以吸引力为例研究DTN和VD的神经生理学相关性（研究3），这有可能澄清先前关于面部吸引力的研究中有争议的结果。使用先进的图像处理方法和大脑活动的电生理记录，本研究旨在系统研究nMDFS中面孔知觉的行为和神经认知机制。我们期望这个项目将（1）提高我们对面孔是如何被心理表征的理解，（2）进一步揭示熟悉和不熟悉面孔表征的神经生理学基础。