ArticleScience advances2022
Emerged human-like facial expression representation in a deep convolutional neural network.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Facial expression discrimination emerges from partially overlapping neural subspaces of detection and identity.Nature communications · 2026Article
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- Article
- Human camouflage and expression via soft mask from reprogrammable chemical fluid skin.Science advances · 2025Article
- Language-specific representation of emotion-concept knowledge causally supports emotion inference.iScience · 2024Article
- Egocentric 3D Skeleton Learning in a Deep Neural Network Encodes Obese-like Motion Representations.Experimental neurobiology · 2024Article
- Profiles of visual perceptual learning in feature space.iScience · 2024Article
- Interpersonal Stress and Late Adolescent Depressive Symptoms: Moderation by Perceptual Sensitivity to Facial Expression of Anger.Journal of youth and adolescence · 2023Article
- Intracranial Electroencephalography and Deep Neural Networks Reveal Shared Substrates for Representations of Face Identity and Expressions.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.NeuroImage · 2023Article
- Challenging the Classical View: Recognition of Identity and Expression as Integrated Processes.Brain sciences · 2023Article
- Disrupted visual input unveils the computational details of artificial neural networks for face perception.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies found that the deep convolutional neural networks (DCNNs) trained to recognize facial identities spontaneously learned features that support facial expression recognition, and vice versa. Here, we showed that the self-emerged expression-selective units in a VGG-Face trained for facial identification were tuned to distinct basic expressions and, importantly, exhibited hallmarks of human expression recognition (i.e., facial expression confusion and categorical perception). We then investigated whether the emergence of expression-selective units is attributed to either face-specific experience or domain-general processing by conducting the same analysis on a VGG-16 trained for object classification and an untrained VGG-Face without any visual experience, both having the identical architecture with the pretrained VGG-Face. Although similar expression-selective units were found in both DCNNs, they did not exhibit reliable human-like characteristics of facial expression perception. Together, these findings revealed the necessity of domain-specific visual experience of face identity for the development of facial expression perception, highlighting the contribution of nurture to form human-like facial expression perception.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.