Face recognition is a challenging categorization task, as in many cases the variability between different images of the same identity may be larger than the variability between images of different identities. Nevertheless, humans excel in this task, in particular for faces they are familiar with. What type of learning and what is the nature of the representation of the learned identity that support such remarkable categorization ability? Here we propose that conceptual learning and the generation of a conceptual representation of the learned identity in memory enables this classification performance. First, we show that humans learn to link perceptually different faces to the same identity, if faces are learned with the same conceptual information. Next, we show that this conceptual learning does not generate a single perceptual representation of the different appearances of each identity. Instead, perceptually dissimilar images of the same identity remain separated in the perceptual space and are linked conceptually rather than perceptually. This conceptual representation of face identity is advantageous, as it enables generalization across perceptually dissimilar images of the same identity/category, without increasing false recognition of perceptually similar images of different identities. A similar conceptual mechanism may also apply to other familiar categories such as familiar voices or objects of expertise that involve fine discrimination of a homogenous sets of stimuli that are linked to unique conceptual information. Overall these findings highlight the importance of studying the contribution of both cognition and perception to face recognition.
Conceptual rather than perceptual similarity enables generalization across perceptually different appearances of familiarized faces
