Simulating face recognition: Implications for modelling cognition

We compare two models that try to simulate neuropsychological findings that prosopagnosic patients, who are unable to recognise faces overtly, nonetheless show evidence of face recognition when indirect tests are used. This "covert recognition" ability has been captured by simulation in an IAC model (Burton, Young, Bruce, Johnston, & Ellis, 1991) and a model we call FOV (Farah, O'Reilly, & Vecera, 1993). The IAC model is localist and has been developed incrementally to account for various effects in normal face recognition. The FOV model is distributed, and was created specifically to demonstrate how covert processing effects emerge as a "natural" consequence of this type of connectionist implementation. We examine the ability of these models to account for data from prosopagnosia, their plausibility as models of normal face recognition, and their general modelling styles. The FOV model is able to simulate only the data for which it was created, whereas the IAC model usually stands up well to these tests, which are beyond its original scope. We conclude that models developed to account for specific data sets can rarely benefit our understanding of underlying cognitive processes, and that connectionist models need to be evaluated against more stringent criteria than are currently used.