“Just another pretty face”: A multidimensional scaling approach to face attractiveness and variability

Potter, Timothy; Corneille, Olivier; Ruys, Kirsten I.; Rhodes, Gillian

doi:10.3758/bf03194079

Cited by 38 publications

(34 citation statements)

References 15 publications

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“…In addition, and in line with the recent findings of Potter et al (2007), attractive faces of both groups in Study 1 were more clustered in the psychological space than were unattractive faces [African t(18) …”

Section: Complementary Analysessupporting

confidence: 88%

Locating attractiveness in the face space: Faces are more attractive when closer to their group prototype

Potter

Corneille

2008

Psychonomic Bulletin & Review

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Previous research on faces has shown a relation between facial attractiveness and the mathematical averageness of a face (e.g., Langlois & Roggman, 1990). Rhodes, Harwood, Yoshikawa, Nishitani, and McLean (2002) also showed that people from different social groups (e.g., Caucasian and Asian) show high intergroup correlations in attractiveness ratings of the same mathematically average faces, regardless of the group memberships of the raters or of the faces being rated. Yet, relatively little is known about how people's mental representations of faces relate to the faces' attractiveness. In particular, no research to date has examined how faces that vary in attractiveness are represented relative to mathematical averages (i.e., to prototypes) for various ethnic groups, a gap that the present research was designed to address.One popular framework used to understand how faces are perceived, encoded, and retrieved is the face space (Lewis, 2004;Valentine, 1991). This space is akin to a mental "map" in which each dimension is normally distributed and holds information necessary to remembering and recognizing a face. Busey (1998) showed that the face space can be modeled adequately using multidimensional scaling (MDS) analysis. He used faces of bald men and their morphs in a similarity judgment task involving all possible combinations of faces. The morphs appeared more typical than their parent faces and were found closer to the center, in line with predictions made by the face space model. The morphs were also found to be less typical than predicted, consistent with recent research on attractor field models that has shown

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Section: Complementary Analysessupporting

confidence: 88%

Locating attractiveness in the face space: Faces are more attractive when closer to their group prototype

Potter

Corneille

2008

Psychonomic Bulletin & Review

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“…As of now, we are confident that the density asymmetry in this stimulus sample and the following processing differences are general phenomena observable across many different domains; the density asymmetry has been found for nouns, trait words, self-generated words, IAPS pictures, and mental representations of other people (Alves et al, 2015;Bruckmüller & Abele, 2013;Koch et al, 2015;Leising et al, 2012;Potter et al, 2007;Unkelbach et al, 2010;Unkelbach et al, 2008a, b). However, given the limitation of the stimulus sample used in the present work it remains unclear to what extent the present findings can be generalized to different stimulus sets and procedures other than the old-new recognition task.…”

Section: Limitations and Open Questionsmentioning

confidence: 99%

“…This was shown for English verbs (Semin & Fiedler, 1992), German personality traits (Leising, Ostrovski, & Borkenau, 2012), as well as for English and Spanish emotion words (Schrauf & Sanchez, 2004). Another example is facial attractiveness; attractive faces are alike, while there are many different ways to be unattractive (Potter, Corneille, Ruys, & Rhodes, 2007). The same principle extents to person perception in general as likable, or Bpositive^persons are perceived as more similar to one another compared to disliked persons (Leising, Ostrovski, & Zimmermann, 2013;Alves, Koch, & Unkelbach, under review).…”

Section: The Density Hypothesismentioning

confidence: 99%

“…The original measure of the density construct consisted of pairwise similarity ratings that were analyzed using a multidimensional scaling procedure (MDS; Krumhansl, 1978). Stimulus density was defined as the average Euclidean distance of a stimulus to all other stimuli of the same valence in a given stimulus set (Potter et al, 2007;Unkelbach et al, 2008b). As of now, we and others have found this density asymmetry for a variety of different stimulus classes including nouns, trait words, self-generated words, as well as IAPS pictures using different measures like multidimensional scaling the spatial arrangement method (SPAM; Goldstone, 1994) and Google co-frequency analysis (Bruckmüller & Abele, 2013;Koch, Alves, Krüger, & Unkelbach, 2015;Unkelbach, Guastella, & Forgas, 2008).…”

Section: The Density Hypothesismentioning

confidence: 99%

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A density explanation of valence asymmetries in recognition memory

et al. 2015

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The density hypothesis states that positive information is more similar than negative information, resulting in higher density of positive information in mental representations. The present research applies the density hypothesis to recognition memory to explain apparent valence asymmetries in recognition memory, namely, a recognition advantage for negative information. Previous research explained this negativity advantage on the basis of valence-induced affect. We predicted that positive information's higher density impairs recognition performance. Two old-new word recognition experiments tested whether differential density between positive and negative stimuli creates a negativity advantage in recognition memory, over and above valence-induced affect. In Experiment 1, participants better discriminated negative word stimuli (i.e., less false alarms) and showed a response bias towards positive words. Regression analyses showed the asymmetry to be function of density and not of valence. Experiment 2 varied stimulus density orthogonal to valence. Again, discriminability and response bias were a function of density and not of valence. We conclude that the higher density of positive information causes an apparent valence asymmetry in recognition memory.

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“…A concept similar to the "face-similarity space" used for illustrative purposes later has been described in the context of analyzing facial attractiveness (Potter et al 2007). Such mappings have a long tradition in the visualization of human preference data (Young 1987).…”

Section: Introductionmentioning

confidence: 99%

Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

et al. 2010

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While the retrieval of datasets from human subjects based on demographic characteristics such as gender or race is an ability with wide-ranging application, it remains poorly-studied. In contrast, a large body of work exists in the field of biometrics which has a different goal: the recognition of human subjects. Due to this disparity of interest, existing methods for retrieval based on demographic attributes tend to lag behind the more well-studied algorithms designed purely for face matching. The question this raises is whether a face recognition system could be leveraged to solve these other problems and, if so, how effective it could be. In the current work, we explore the limits of such a system for gender and ethnicity identification given (1) a ground truth of demographically-labeled, textureless 3-D models of human faces and (2) a state-of-the-art facerecognition algorithm. Once trained, our system is capable

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“Just another pretty face”: A multidimensional scaling approach to face attractiveness and variability

Cited by 38 publications

References 15 publications

Locating attractiveness in the face space: Faces are more attractive when closer to their group prototype

Locating attractiveness in the face space: Faces are more attractive when closer to their group prototype

A density explanation of valence asymmetries in recognition memory

Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

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