The Face Module Emerged in a Deep Convolutional Neural Network Selectively Deprived of Face Experience

• Published in: Frontiers in computational neuroscience 2021-05, Vol.15, p.626259-626259
• Main Authors: Xu, Shan, Zhang, Yiyuan, Zhen, Zonglei, Liu, Jia
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; Datasets; deep convolutional neural network; experience; Face; face domain; face perception; Hypotheses; Neural networks; Neuroscience; visual deprivation; Visual discrimination
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2021.626259

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.