The face module emerged in a deep convolutional neural network selectively deprived of face experience

Abstract

Can faces be accurately recognized with zero experience on faces? The answer to this question is critical because it examines the role of experiences in the formation of domain-specific modules in the brain. However, thorough investigation with human and non-human animals on this issue cannot easily dissociate the effect of the visual experience from that of genetic inheritance, i.e., the hardwired domain-specificity. The present study addressed this problem by building a model of selective deprivation of the experience on faces with a representative deep convolutional neural network (DCNN), AlexNet. We trained a new AlexNet with the same image dataset, except that all images containing faces of human and nonhuman primates were removed. We found that the experience-deprived AlexNet (d-AlexNet) did not show significant deficits in face categorization and discrimination, and face-selective modules also automatically emerged. However, the deprivation made the d-AlexNet to process faces in a more parts-based fashion, similar to the way of processing objects. In addition, the face representation of the face-selective module in the d-AlexNet was more distributed and the empirical receptive field was larger, resulting in less degree of selectivity of the module. In sum, our study provides undisputable evidence on the role of nature versus nurture in developing the domain-specific modules that domain-specificity may evolve from non-specific stimuli and processes without genetic predisposition, which is further fine-tuned by domain-specific experience.