Coding spatial variations in faces and simple shapes: a test of two models

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.