Robin Kramer

We used attractiveness judgements as a proxy to visualize the ideal female and male body for male and female participants and investigated how individual differences in the internalization of cultural ideals influence these representations. In the first of two studies, male and female participants judged the attractiveness of 242 male and female computer-generated bodies which varied independently in muscle and adipose. This allowed us to map changes in attractiveness across the complete body composition space, revealing single peaks for the attractiveness of both men and women. In the second study, we asked our participants to choose the most attractive male and female bodies in a method of adjustment task in which they could independently vary muscle and adipose to create the most attractive body. We asked whether individual differences in internalization of cultural ideals, drive for muscularity, eating disorder symptomatology and depressive symptoms could systematically shift the location of peak attractiveness in body composition space. We found a clear preference by both genders for a male body with high muscle and low adipose, and a toned, low adipose female body. The degree of internalization of cultural ideals predicted large individual differences in the composition of the most attractive bodies.

Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework.