ArticleProceedings of the National Academy of Sciences of the United States of America2023
Modeling naturalistic face processing in humans with deep convolutional neural networks.
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 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- Representations converge as brain maps diverge along the cortical hierarchy.bioRxiv : the preprint server for biology · 2026Article
- "Dense Amygdala": Extensive Complex-valued Functional MRI of the Ventral and Medial Temporal Lobe during Passive Movie Watching in Three Individuals.Scientific data · 2026Article
- Sensory encoding of emotion conveyed by the face and visual context.Social cognitive and affective neuroscience · 2026Article
- Article
- Circuit Design in Biology and Machine Learning. II. Anomaly Detection.Entropy (Basel, Switzerland) · 2025Article
- Inter-individual and inter-site neural code conversion without shared stimuli.Nature computational science · 2025Article
- Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision.Research square · 2024Article
- Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision.bioRxiv : the preprint server for biology · 2024Article
- Linking faces to social cognition: The temporal pole as a potential social switch.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neural computations in prosopagnosia.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Prediction error processing and sharpening of expected information across the face-processing hierarchy.Nature communications · 2024Article
- Comparison of the Capacity of Several Machine Learning Tools to Assist Immunofluorescence-Based Detection of Anti-Neutrophil Cytoplasmic Antibodies.International journal of molecular sciences · 2024Article
- Decoding face recognition abilities in the human brain.PNAS nexus · 2024Article
- Disentangled deep generative models reveal coding principles of the human face processing network.PLoS computational biology · 2024Article
- Article
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6 authors.
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Abstract
Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features.
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