Whether face processing depends on unique, domain-specific neurocognitive mechanisms or domain-general object recognition mechanisms has long been debated. Directly testing these competing hypotheses in humans has proven challenging due to extensive exposure to both faces and objects. Here, we systematically test these hypotheses by capitalizing on recent progress in convolutional neural networks (CNNs) that can be trained without face exposure (i.e., pre-trained weights). Domain-general mechanism accounts posit that face processing can emerge from a neural network without specialized pre-training on faces. Consequently, we trained CNNs solely on objects and tested their ability to recognize and represent faces as well as objects that look like faces (face pareidolia stimuli).... Due to the character limits, for more details see in attached pdf
View on arXiv@article{zhao2025_2405.18800,
title={ Face processing emerges from object-trained convolutional neural networks },
author={ Zhenhua Zhao and Ji Chen and Zhicheng Lin and Haojiang Ying },
journal={arXiv preprint arXiv:2405.18800},
year={ 2025 }
}