ArticleNature communications2019
Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Tracing aesthetic experience from perception and conception to appraisal using deep convolutional neural networks.iScience · 2026Article
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- Computational single-neuron mechanisms of visual object coding in the human temporal lobe.Nature communications · 2026Article
- Characterization of the spatiotemporal representations of visual, semantic, and memorability features in the human brain.PLoS biology · 2026Article
- Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus.Nature human behaviour · 2025Article
- Invariant inter-subject relational structures in high order human visual cortex.Nature communications · 2025Article
- Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence.Communications biology · 2025Article
- Adversarial testing of global neuronal workspace and integrated information theories of consciousness.Nature · 2025Article
- A neural computational framework for face processing in the human temporal lobe.Current biology : CB · 2025Article
- Configural processing as an optimized strategy for robust object recognition in neural networks.Communications biology · 2025Article
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
- A neuronal code for object representation and memory in the human amygdala and hippocampus.Nature communications · 2025Article
- Article
- A human single-neuron dataset for object recognition.Scientific data · 2025Article
- Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Contrastive learning explains the emergence and function of visual category-selective regions.Science advances · 2024Article
- Neural evidence for attentional capture by salient distractors.Nature human behaviour · 2024Article
- Prediction error processing and sharpening of expected information across the face-processing hierarchy.Nature communications · 2024Article
2 more citing papers are in PubMed but not listed here.
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11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery that deep convolutional neural networks (DCNNs) achieve human performance in realistic tasks offers fresh opportunities for linking neuronal tuning properties to such tasks. Here we show that the face-space geometry, revealed through pair-wise activation similarities of face-selective neuronal groups recorded intracranially in 33 patients, significantly matches that of a DCNN having human-level face recognition capabilities. This convergent evolution of pattern similarities across biological and artificial networks highlights the significance of face-space geometry in face perception. Furthermore, the nature of the neuronal to DCNN match suggests a role of human face areas in pictorial aspects of face perception. First, the match was confined to intermediate DCNN layers. Second, presenting identity-preserving image manipulations to the DCNN abolished its correlation to neuronal responses. Finally, DCNN units matching human neuronal group tuning displayed view-point selective receptive fields. Our results demonstrate the importance of face-space geometry in the pictorial aspects of human face perception.
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