ArticleProceedings of the National Academy of Sciences of the United States of America2023
Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
- Article
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- A solution to generalized learning from small training sets found in infants' repeated visual experiences of individual objects.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Learned foveal representations, not gaze-mediated information access, underlie perceptual advantages for canonical face configurations.Journal of vision · 2026Article
- How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026Article
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- E/I imbalance and internal noise cause weak neural representations and face recognition challenges in ASD.Communications biology · 2026Article
- Dual computational systems in the development and evolution of mammalian brains.Science advances · 2026Article
- Experience Alone Can Generate Human Face Specialization: Evidence From Deep Learning Models.Open mind : discoveries in cognitive science · 2026Article
- Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.Journal of vision · 2025Article
- Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.PLoS computational biology · 2025Article
- A computational deep learning investigation of animacy perception in the human brain.Communications biology · 2024Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Deep convolutional neural networks are sensitive to face configuration.Journal of vision · 2024Article
- Contrastive learning explains the emergence and function of visual category-selective regions.Science advances · 2024Article
- The face inversion effect through the lens of deep neural networks.Proceedings. Biological sciences · 2024Article
- Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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Abstract
Human face recognition is highly accurate and exhibits a number of distinctive and well-documented behavioral "signatures" such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is "special". But why does human face perception exhibit these properties in the first place? Here, we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As we found for face perception, the car-trained network showed a drop in performance for inverted vs. upright cars. Similarly, CNNs trained on inverted faces produced an inverted face inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so special after all.
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