Proceedings of the Annual Meeting of the Cognitive Science Society

We examine the ability of an autoassociative memory trained with faces to classify faces by race and by sex. The model learns a low-level visual coding of Japanese and Caucasian male and female faces. Since recall of a face from the autoassociative memory is equivalent to computing a weighted sum of the eigenvectors of the memory matrix, faces can be represented by these weights and the set of corresponding eigenvectors. W e show that reasonably accurate classification of the faces by race and sex can be achieved using only these weights. Hence, race and sex information can be extracted in the model without explicitly learning the classification itself.