The Role of Familiarity for Representations in Norm-Based Face SpaceReport as inadecuate




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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.



Author: Stella J. Faerber , Jürgen M. Kaufmann, Helmut Leder, Eva Maria Martin, Stefan R. Schweinberger

Source: http://plos.srce.hr/



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