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Abstract
According to the norm-based version of the multidimensional face space model (nMDFS, Valentine, 1991), any given face and its corresponding anti-face (which deviates from the norm in exactly opposite direction as the original face) should be equidistant to a hypothetical prototype face (norm), such that by definition face and anti-face should bear the same level of perceived typicality. However, it has been argued that familiarity affects perceived typicality and that representations of familiar faces are qualitatively different (e.g., more robust and image-independent) from those for unfamiliar faces. Here we investigated the role of face familiarity for rated typicality, using two frequently used operationalisations of typicality (deviation-based: DEV), and distinctiveness (face in the crowd: FITC) for faces of celebrities and their corresponding anti-faces. We further assessed attractiveness, likeability and trustworthiness ratings of the stimuli, which are potentially related to typicality. For unfamiliar faces and their corresponding anti-faces, in line with the predictions of the nMDFS, our results demonstrate comparable levels of perceived typicality (DEV). In contrast, familiar faces were perceived much less typical than their anti-faces. Furthermore, familiar faces were rated higher than their anti-faces in distinctiveness, attractiveness, likability and trustworthiness. These findings suggest that familiarity strongly affects the distribution of facial representations in norm-based face space. Overall, our study suggests (1) that familiarity needs to be considered in studies of mental representations of faces, and (2) that familiarity, general distance-to-norm and more specific vector directions in face space make different and interactive contributions to different types of facial evaluations.
Highlights
Models of face representation aim at elucidating face recognition and face classification processes and often use a framework that is based on the similarity between faces [1]
According to the influential norm-based version of the multidimensional face space model, a face is encoded as a point in an ndimensional space and its location is defined by a vector from the norm—i.e. the centre—to that point
This is in strong contrast to the idea that the distance to the norm is the only factor that influences perceived typicality (DEV) and distinctiveness (FITC), as predicted by the norm-based MDFS model
Summary
Models of face representation aim at elucidating face recognition and face classification processes and often use a framework that is based on the similarity between faces [1]. The norm is the central tendency of all dimensions, which serve to discriminate faces Those dimensions are explicitly not further specified, they are assumed to correspond to physiognomic features such as hair colour and length, face shape or age ([2], p.166). This specific relation to the norm is critical for the nMDFS in that it assumes a gradual and concentric increase in perceived typicality (or decrease in perceived distinctiveness) the closer a face is located to the norm. The typicality or distinctiveness within the nMDFS is defined by the distance of an individual face to that average
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