Abstract
Versions of the “face space” are considered and built upon to develop an explicitly defined model of face recognition based on stimulus generalization that is similar to models of animal learning. This face‐space‐R model is implemented using realistic numbers of known faces. The model is able to account for distinctiveness, caricature, and race effects. It also predicts which faces will be falsely recognized and accounts for mirror effects. The application of the model to face learning and development is considered, as well as the effects of brief presentation. By varying parameters of the model, it is possible to match its performance to that of humans, leading to an estimate of the dimensionality of face space (of between 15 and 22 dimensions for same‐race faces).
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