Face Learning / Expertise

Abstract

Familiar faces can be easily recognized even from poor quality images and across a large range of viewing conditions. By contrast, it is surprisingly difficult to recognize or even to match unfamiliar faces across different images. While this suggests qualitative differences between the processing of familiar and unfamiliar faces, little is known about how new representations of faces are formed during learning. However, recent research suggests that mental representations of faces essentially may code an average across the perceptual instances perceived during familiarization. At the same time, natural variability in visual encounters of familiar faces also appears to play an important role for the acquisition of robust representations of well-known faces, in a way that remains to be precisely understood. Building on previous work from our group that identified event-related brain potential (ERP) correlates of face recognition, we use behavioural and ERP experiments to improve our understanding of face learning. The project aims at understanding how non-visual cues such as voices and semantic information contribute to face recognition and investigates how these links develop during face learning. Importantly, the project also utilises sophisticated methods of image manipulation (such as selective photorealistic caricaturing in either shape or reflectance information) to determine the relative role of different kinds of visual information for face learning and recognition. We also currently use these techniques in order to devise and explore training programmes for individuals with poor face recognition skills.

Selected Relevant Publications

Zhou, X., Itz, M.L., Kaufmann, J.M., Schweinberger, S.R., & Mondloch, C.J. (2021). The other-race effect is not modulated by differential use of shape and texture cues during face learning and recognition. Vision Research. (Link to PDF)

Andrews, S., Burton, A.M., Schweinberger, S.R., and Wiese, H. (2017). Event-related potentials reveal the development of stable face representations from natural variability. The Quarterly Journal of Experimental Psychology. (Link to PDF)

Blickhan, M., Kaufmann, J.M., Denzler, J., Schweinberger, S.R., & Redies, C. (2011). 1/f p Characteristics of the Fourier Power Spectrum Affects ERP Correlates of Face Learning and Recognition. Biological Psychology, 88(2-3), 204-214.

Dobel, C., & Schweinberger, S.R. (2006). Plasticity of face perception: Psychophysiological correlates of familiarity and expertise. Symposium at the Annual meeting of the Society for Psychophysiological Research, Vancouver, October 26-29, 2006. Psychophysiology, 43, S13.

Faerber, S.J., Kaufmann, J.M., Leder, H., Martin, E.-M., & Schweinberger, S.R. (2016). The role of familiarity for representations in norm-based face space. PloS One 11(5): e0155380. doi:10.1371/ journal.pone.0155380. (Link to PDF)

Itz, M.L., Schweinberger, S.R., Schulz, C., & Kaufmann, J.M. (2014) Neural correlates of facilitations in face learning by selective caricaturing of facial shape or reflectance. NeuroImage, 102, 736-747. (Link to PDF)

Itz, M.L., Schweinberger, S.R., & Kaufmann, J.M. (2017). Caricature generalization benefits for faces learned with enhanced idiosyncratic shape or texture. Cognitive, Affective, and Behavioral Neuroscience, 17, 185-197. doi:10.3758/s13415-016-0471-y. (Link to PDF)

Kaufmann, J. M., Burton, A. M., & Schweinberger, S. R. (2004). Neural correlates of face learning and long-term repetition priming. Perception, 33, 107.

Kaufmann, J.M., Schulz, C., & Schweinberger, S.R. (2013). High and low performers differ  in the use of shape information for face recognition. Neuropsychologia, 51(7),1310-1319. (Link to PDF)

Kaufmann, J.M., & Schweinberger, S.R. (2012). The faces you remember: Caricaturing shape facilitates brain processes reflecting the acquisition of new face representations.Biological Psychology, 89(1), 21-33.

Kaufmann, J.M., Schweinberger, S.R., & Burton, A.M. (2009). N250 ERP correlates of the acquisition of face representations across different images. Journal of Cognitive Neuroscience, 21, 625-641.

Kaufmann, J.M., & Schweinberger, S.R. (2008). Distortions in the brain? ERP effects of caricaturing familiar and unfamiliar faces. Brain Research, 1228, 177-188.

Kaufmann, J.M., Schweinberger, S.R. & Burton A.M. (2005) Neural Correlates of Learning Faces versus Learning People. 35th annual meeting of the International Neuropsychological Society (INS) and the British Neuropsychological Society (BNS). July 6-9, 2005, Dublin, Ireland.

Kaufmann, J.M., & Schweinberger, S.R. (2004). Expression influences the recognition of familiar faces. Perception, 33, 399-408.

Limbach, K., Kaufmann, J.M., Wiese, H., Witte, O.W., & Schweinberger, S.R. (2018). Enhancement of face-sensitive ERPs in older adults induced by face recognition training. Neuropsychologia, 119, 197-213. (Link to PDF)

Schulz, C., Kaufmann, J.M., Walther, L., & Schweinberger, S.R. (2012). Effects of anticaricaturing vs. caricaturing elucidate a role of shape for face learning.Neuropsychologia, 50, 2426-2434.

Schulz, C., Kaufmann, J.M., Kurt, A., & Schweinberger, S.R. (2012). Faces forming traces: Neurophysiological correlates of learning naturally distinctive and caricatured faces.NeuroImage, 63, 491-500.

Wiese, H., & Schweinberger, S.R. (2018). Inequalities between biases in face memory: Event-related potentials reveal dissociable neural correlates of own-race and own-gender biases. Cortex, 101, 119-135. (Link to PDF)

Wiese, H., Wolff, N., Steffens, M.C., & Schweinberger, S.R. (2013). How experience shapes memory for faces: An event-related potential study on the own-age bias. Biological Psychology, 94(2), 369-379.

Wuttke, S.J., & Schweinberger, S.R. (2019). The P200 predominantly reflects distance-to-norm in face space whereas the N250 reflects activation of identity-specific representations of known faces. Biological Psychology, 140, 86-95. (Link to PDF)

Zhou, X., Itz, M.L., Kaufmann, J.M., Schweinberger, S.R., & Mondloch, C.J. (in press). The other-race effect is not modulated by differential use of shape and texture cues during face learning and recognition. Vision Research.

Funding

  • BBSRC, British Academy
  • DFG Grant: KA 2997/ 2-1