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

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:
$0.0万
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
项目状态:
已结题
起止时间:
2015-12-31 至 2019-12-31

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中文摘要
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英文摘要
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.
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