Individual differences in learning and recognizing faces

• Applicants: Dr. Jürgen Michael Kaufmann, Ph.D.; Professor Dr. Stefan R. Schweinberger
• Subject Area: General, Cognitive and Mathematical Psychology
• Term: from 2011 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 197659409

While scientific progress has been made to understand the cognitive and neuronal processes involved in the recognition of familiar faces, a consistent finding has been that mental representations for familiar faces are robust against transformations in the image, whereas the recognition of once-seen unfamiliar faces is massively impaired by image changes. But how do humans achieve robust facial representations during face learning? To date, cognitive and neuronal processes of face learning are surprisingly poorly understood. Moreover, individual differences in face recognition ability have become a focus of recent research, while the mechanisms underlying these differences also remain largely unknown. During the first funding period, we investigated face learning in good and poor performers, and found evidence that poor performers benefit disproportionately from an enhancement of a face´s idiosyncratic shape (by means of selective spatial caricaturing). This could suggest that individual differences in face recognition relate to differences in the relative use of shape and surface reflectance (texture) information. In addition to the N250 ERP component, which is related to the activation of identity-specific representations, we also found that the preceding occipitotemporal P200 responds in a systematic manner to spatial caricaturing. In the second funding period, we will systematically pursue these promising findings, in order to better understand individual differences in face learning and recognition. Specifically, we will investigate differences between good and poor performers in the use of shape and texture information, and further explore our hypothesis that texture information becomes more important as a face becomes familiar (E1 and E2). We will also investigate individual differences in processing the second-order spatial configuration of facial features, by introducing a metric manipulation of feature placement (E3), and study whether the deficit of poor performers in forming robust representations is specifically related to poor formation of within-person prototypes during face learning (E4). Finally, and based on our results, we will assess the efficiency and generalization of a training program in face learning that uses faces with either enhanced shape or enhanced texture information (E5). Overall, by combining methods from experimental psychology, differential psychology, and cognitive neuroscience, we expect substantial progress both in understanding the mechanisms underlying face learning, and in developing interventions for individuals with disorders in face recognition.

• DFG Programme: Research Grants