Learning new faces in an interactive activation and competition model

Abstract

Certain contemporary accounts of object and face recognition use connectionist networks with local representations. This paper describes and extends one such account: an interactive activation and competition (IAC) model of face recognition. In contrast to many networks with distributed representations, IAC models do not incorporate a learning mechanism. This limits their use in psychological modelling. This paper describes how a learning mechanism can be built into an IAC model. The mechanism automatically learns new representations and appears to have many of the desirable properties traditionally associated with distributed networks. Some simulations that produce results consistent with our knowledge of human face learning are reported. Finally, the relation between this work and current theories of visual object recognition is discussed.