Exploring holistic face processing in the fusiform and occipital face area using incomplete faces.

Face recognition is essential for human social life, and two core face areas have been identified in the human brain: the fusiform face area (FFA) and occipital face area (OFA). Previous studies have explored the functional division of the FFA and OFA in holistic face processing, using variations of whole faces where holistic processing was disrupted. However, a main prediction of holistic face processing (i.e., nonlinear integration of parts into wholes) lacks direct evidence. Here, we tested this prediction in the FFA and OFA by comparing their responses to incomplete versus complete faces. We generated novel facial fragments using AlexNet, a deep convolutional neural network (DCNN), and presented the facial fragments, their occluded counterparts, and complete face during fMRI scanning. Results revealed that the FFA exhibited higher activation for complete than incomplete faces, while the OFA showed comparable or higher activation for some incomplete faces than complete one. Moreover, the FFA's activation for the complete face was higher than the weighted average activation for some of its constituent incomplete face pairs, while this difference was not observed in the OFA. These results suggest that the FFA favors holistic processing and combines facial parts nonlinearly, whereas the OFA focuses on local parts and processes parts linearly. Our findings shed new light on the neural basis of holistic face processing and the functional division of the FFA and OFA in face recognition. The approach combining DCNNs with neuroimaging techniques can be extended to broader visual domains in future studies.