Deep learning of shared perceptual representations for familiar and unfamiliar faces: Reply to commentaries

• Main Authors: Behrmann, M. (Author), Blauch, N.M (Author), Plaut, D.C (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2021
• Subjects: article; convolutional neural network; Deep convolutional neural networks; deep learning; Deep Learning; Expertise; Face recognition; facial recognition; Facial Recognition; Familiarity; human; human experiment; Humans; Perceptual representations; Recognition, Psychology; standing; theoretical study
• Online Access: View Fulltext in Publisher
• ISBN: 00100277 (ISSN)
• DOI: 10.1016/j.cognition.2020.104484

We recently argued that human unfamiliar face identity perception reflects substantial perceptual expertise, and that the advantage for familiar over unfamiliar face identity matching reflects a learned mapping between generic high-level perceptual features and a unique identity representation of each individual (Blauch, Behrmann and Plaut, 2020). Here we respond to two commentaries by Young and Burton (2020) and Yovel and Abudarham (2020), clarifying and elaborating our stance on various theoretical issues, and discussing topics for future research in human face recognition and the learning of perceptual representations. © 2020 The Author(s)