Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression

Summary: Facial repetition suppression, a well-studied phenomenon characterized by decreased neural responses to repeated faces in visual cortices, remains a subject of ongoing debate regarding its underlying neural mechanisms. Our research harnesses advanced multivariate analysis techniques and the prowess of deep convolutional neural networks (DCNNs) in face recognition to bridge the gap between human electroencephalogram (EEG) data and DCNNs, especially in the context of facial repetition suppression. Our innovative reverse engineering approach, manipulating the neuronal activity in DCNNs and conducted representational comparisons between brain activations derived from human EEG and manipulated DCNN activations, provided insights into the underlying facial repetition suppression. Significantly, our findings advocate the fatigue mechanism as the dominant force behind the facial repetition suppression effect. Broadly, this integrative framework, bridging the human brain and DCNNs, offers a promising tool for simulating brain activity and making inferences regarding the neural mechanisms underpinning complex human behaviors.