Abstract

Models of face recognition and classification often adopt a framework in which faces are represented as points in a multidimensional space. This psychological face space organizes the faces according to similarity and makes predictions for representational theories of faces. A variety of image-processing techniques have been used to create novel stimuli in this space that represent the average of a population or make a face appear more distinctive. The current research examined the relation between the stimuli created by these image-processing techniques and the underlying psychological representation as measured by multidimensional scaling (MDS) procedures. Morphing procedures were used to create 16 faces that were embedded in a set of 84 other faces. Similarity ratings between all possible pairs of faces were collected, and the data were analyzed using MDS procedures. Dimensions that emerged from the MDS solution included age, race, adiposity, and facial hair. In the MDS space, the morphs appeared more typical than the parents, as predicted by the geometric model. A number of biases were examined, including the tendency of the morphs to be less typical than predicted, which may be attributed to the effects of density near the center of face space. In addition, age and facial-adiposity biases were found. The results support the use of the face-space framework for models of face recognition, although image-processing techniques that are designed to create novel stimuli in this space may introduce systematic biases.

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