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The state of modelling face processing in humans with deep learning.

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Title: The state of modelling face processing in humans with deep learning.
Authors: Phillips, P. Jonathon (AUTHOR), White, David (AUTHOR)
Source: British Journal of Psychology. May2026, Vol. 117 Issue 2, p656-676. 21p.
Subjects: Conceptual models, Research funding, Convolutional neural networks, Neurosciences, Psychology, Deep learning, Neuropsychology, Artificial neural networks, Face perception, Thought & thinking, Cognition
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: The state of modelling face processing in humans with deep learning.
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Psychology%22">British Journal of Psychology</searchLink>. May2026, Vol. 117 Issue 2, p656-676. 21p.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/bjop.12794
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Research funding
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      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Neurosciences
        Type: general
      – SubjectFull: Psychology
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Neuropsychology
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Face perception
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      – SubjectFull: Cognition
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      – TitleFull: The state of modelling face processing in humans with deep learning.
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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