Konferenzpaper

Familiarity Affects Early Perceptual Stages of Face Processing


AutorenlisteDobs, Katharina; Isik, Leyla; Pantazis, Dimitrios; Kanwisher, Nancy

Erschienen in2018 Conference on Cognitive Computational Neuroscience

Jahr der Veröffentlichung2018

Seiten1104-1104

DOI Linkhttps://doi.org/10.32470/CCN.2018.1104-0

Konferenz2018 Conference on Cognitive Computational Neuroscience


Abstract

From a brief glimpse of a face, we extract not just the presence of a person, but their gender, age, familiarity and specific identity. How quickly are these specific dimensions of face information represented, and which dimensions are affected by familiarity? To find out, we used magnetoencephalography (MEG) and representational similarity analysis (RSA) to measure the time course of extraction of each of these dimensions of face information and their modulation by familiarity. Subjects viewed 80 face images, 5 of each of 16 celebrities, varying in lightning, pose, expression, and eye gaze. Celebrities varied orthogonally in familiarity, gender and age. Subjects (n = 16) performed a 1-back task on upright and inverted images in separate sessions. RSA analyses showed that we could decode identity, gender and age of face images at similar latencies within 130 ms after stimulus onset. We further found that familiarity enhanced face representations even at this early stage. Importantly, when identity decoding was analyzed within age and gender, early identity decoding remained significant only for familiar faces, suggesting qualitatively different early processing for familiar versus unfamiliar faces.




Autoren/Herausgeber




Zitierstile

Harvard-ZitierstilDobs, K., Isik, L., Pantazis, D. and Kanwisher, N. (2018) Familiarity Affects Early Perceptual Stages of Face Processing, in 2018 Conference on Cognitive Computational Neuroscience. p. 1104. https://doi.org/10.32470/CCN.2018.1104-0

APA-ZitierstilDobs, K., Isik, L., Pantazis, D., & Kanwisher, N. (2018). Familiarity Affects Early Perceptual Stages of Face Processing. In 2018 Conference on Cognitive Computational Neuroscience. (pp. 1104). https://doi.org/10.32470/CCN.2018.1104-0


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