Proceedings of the Annual Meeting of the Cognitive Science Society

A considerable body of evidence from prosopagnosia, a deficit in face recognition dissociable from nonface object recognition, indicates that the visual system devotes a specialized functional area to mechanisms appropriate for face processing. We present a modular neural network composed of two "expert" networks and one mediating "gate" network with the task of learning to recognize the faces of 12 individuals and classifying 36 nonface objects as members of one of three classes. While learning the task, the network tends to divide labor between the two expert modules, with one expert specializing in face processing and the other specializing in nonface object processing. After training, we observe the network's performance on a test set as one of the experts is progressively damaged. The results roughly agree with data reported for prosopagnosic patients: as damage to the "face" expert increases, the network's face recognition performance decreases dramatically while its object classification performance drops slowly. We conclude that data-driven competitive learning between two unbiased functional units can give rise to localized face processing, and that selective damage in such a system could underlie prosopagnosia.