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The proponents of exemplar models of categorization and memory have claimed that recognition judgements are based on familiarity computed by summing the similarity between a probe and all exemplars in memory. A probe which is highly similar to many previously seen exemplars should be recognized more accurately or faster than a more dissimilar probe. The ‘summed-similarity rule’ has been supported in a number of experiments on recognition of relatively unfamiliar and artificial stimuli. However, evidence from face recognition clearly contradicts the rule. Distinctive or unusual faces are recognized more accurately than typical faces. It is proposed that this contradiction can be resolved if tasks using photographs of faces as stimuli which have been termed ‘recognition’ tasks are interpreted as ‘identification’ tasks. However, if this interpretation is made, an exemplar model is not the only class of models which can account for the effects of distinctiveness in face ‘classification’ and ‘identification’ tasks. Three simulations are reported which show that parallel distributed processing models can also account for the data from face-processing tasks. Two simulations are based on a single-layer auto-associative network. The final simulation is based on a multi-layer network using backward error propagation.