ArticleFrontiers in computational neuroscience2021
The Face Module Emerged in a Deep Convolutional Neural Network Selectively Deprived of Face Experience.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Article
- Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.bioRxiv : the preprint server for biology · 2025Article
- Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.PLoS computational biology · 2025Article
- 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
- Real-world size of objects serves as an axis of object space.Communications biology · 2022Article
- Guiding visual attention in deep convolutional neural networks based on human eye movements.Frontiers in neuroscience · 2022Article
- Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition.Frontiers in computational neuroscience · 2021Article
- THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks.Frontiers in neuroinformatics · 2021Article
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Authors and funding
4 authors.
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Abstract
Can we recognize faces with zero experience on faces? This question is critical because it examines the role of experiences in the formation of domain-specific modules in the brain. Investigation with humans and non-human animals on this issue cannot easily dissociate the effect of the visual experience from that of the hardwired domain-specificity. Therefore, the present study built a model of selective deprivation of the experience on faces with a representative deep convolutional neural network, AlexNet, by removing all images containing faces from its training stimuli. This model did not show significant deficits in face categorization and discrimination, and face-selective modules automatically emerged. However, the deprivation reduced the domain-specificity of the face module. In sum, our study provides empirical evidence on the role of nature vs. nurture in developing the domain-specific modules that domain-specificity may evolve from non-specific experience without genetic predisposition, and is further fine-tuned by domain-specific experience.
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