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

Frontiers in Computational Neuroscience

Zonglei Zhen¹  Yiyuan Zhang¹  Shan Xu¹  Jia Liu² 

[1] Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, China;;Department of Psychology &

关键词: face perception;    face domain;    deep convolutional neural network;    visual deprivation;    experience;   

DOI  :  10.3389/fncom.2021.626259

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown