The state of modelling face processing in humans with deep learning.
Deep learning models trained for facial recognition now surpass the highest performing human participants. Recent evidence suggests that they also model some qualitative aspects of face processing in humans. This review compares the current understanding of deep learning models with psychological mo...
| Publicado en: | British Journal of Psychology Vol. 117; no. 2; pp. 656 - 677 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192785889&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192785889 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00071269 BJP jtl: British Journal of Psychology issn: 00071269 maglogo: Y pubinfo: dt: May2026 vid: 117 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 192785889 10.1111/bjop.12794 ppf: 656 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 942KB tig: atl: The state of modelling face processing in humans with deep learning. aug: au: Phillips, P. Jonathon White, David affil: National Institute of Standards and Technology, Gaithersburg Maryland,, USA School of Psychology, UNSW Sydney, Sydney New South Wales,, Australia su: Psychology Cognition Conceptual models Research funding Convolutional neural networks Neurosciences Deep learning Neuropsychology Artificial neural networks Face perception Thought & thinking sug: subj: Psychology Cognition Conceptual models Research funding Convolutional neural networks Neurosciences Deep learning Neuropsychology Artificial neural networks Face perception Thought & thinking keyword: AI computational modelling computer vision face processing facial recognition foundation models neuropsychology perception person perception AI computational modelling computer vision face processing facial recognition foundation models neuropsychology perception person perception ab: Deep learning models trained for facial recognition now surpass the highest performing human participants. Recent evidence suggests that they also model some qualitative aspects of face processing in humans. This review compares the current understanding of deep learning models with psychological models of the face processing system. Psychological models consist of two components that operate on the information encoded when people perceive a face, which we refer to here as 'face codes'. The first component, the core system, extracts face codes from retinal input that encode invariant and changeable properties. The second component, the extended system, links face codes to personal information about a person and their social context. Studies of face codes in existing deep learning models reveal some surprising results. For example, face codes in networks designed for identity recognition also encode expression information, which contrasts with psychological models that separate invariant and changeable properties. Deep learning can also be used to implement candidate models of the face processing system, for example to compare alternative cognitive architectures and codes that might support interchange between core and extended face processing systems. We conclude by summarizing seven key lessons from this research and outlining three open questions for future study. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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