Evolutionary, as well as cultural, pressures may contribute to our perceptions of facial attractiveness.Biologists predict that facial symmetry should be attractive, because it may signal mate quality. We tested the prediction that facial symmetry is attractive by manipulating the symmetry of individual faces and observing the effect on attractiveness, and by examining whether natural variations in symmetry (between faces) correlated with perceived attractiveness. Attractiveness increased when we increased symmetry, and decreased when we reduced symmetry, in individual faces (Experiment 1), and natural variations in symmetry correlated significantly with attractiveness (Experiments 1 and lA). Perfectly symmetric versions, made by blending the normal and mirror images of each face, were preferred to less symmetric versions of the same faces (even when those versions were also blends) (Experiments 1 and 2). Similar results were found when subjects judged the faces on appeal as a potential life partner, suggesting that facial symmetry may affect human mate choice. We conclude that facial symmetry is attractive and discuss the possibility that this preference for symmetry may be biologically based.The question of what makes a face attractive, and whether our preferences come from culture or biology, has fascinated scholars for centuries. Variation in the ideals of beauty across societies and historical periods suggests that standards of beauty are set by cultural convention. Recent evidence challenges this view, however, with infants as young as 2 months of age preferring to look at faces that adults find attractive (Langlois et aI., 1987), and people from different cultures showing considerable agreement about which faces are attractive

Faces all have the same basic elements in the same overall arrangement, and must be discriminated using variations in this shared configuration. An efficient way to represent these variations would be to code how each configuration differs from an average face (norm-based coding model). Alternatively, configurations could be represented simply by coding their absolute values in some coordinate system (absolute coding model). The two models differ in the variables they predict will influence an image's recognizability. Absolute coding predicts that recognizability will depend on an image's distinctiveness and degree of distortion from its veridical target. Norm-based coding predicts that recognizability will also depend on the way the image differs from a norm or average face, namely its distance from the norm and/or its degree of displacement from the norm-deviation vector for the target. We determined the effects of these four critical variables on recognition of undistorted (veridical) images, caricatures, anticaricatures and 'lateral' distortions of famous faces (Experiment 1), newly learned faces (Experiment 2), and simple shapes that also share a configuration (Experiment 2). The results favored absolute coding of both faces and shapes, and indicate that caricatures derive their power from their distinctiveness.