AUTHOR=Xu Shan , Zhang Yiyuan , Zhen Zonglei , Liu Jia TITLE=The Face Module Emerged in a Deep Convolutional Neural Network Selectively Deprived of Face Experience JOURNAL=Frontiers in Computational Neuroscience VOLUME=15 YEAR=2021 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2021.626259 DOI=10.3389/fncom.2021.626259 ISSN=1662-5188 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.