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Poster A131 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Temporal dynamics of face recognition: Insights from combining MEG and Artificial Neural Networks
Hamza Abdelhedi1,3 (), Shahab Bakhtiari1,2,3, Karim Jerbi1,2,3; 1Université de Montréal, 2UNIQUE (Unifying Neuroscience and AI), Montreal, Quebec, Canada, 3Mila (Quebec AI institute), Montreal, Quebec, Canada
Humans excel at face recognition, relying on a specialized system whose representation of familiar and unfamiliar faces have been debated. Some would argue that unfamiliar and familiar faces are processed in the same way, while others claim otherwise. Similarly, artificial neural networks (ANNs) have shown remarkable abilities, prompting discussions on their similarities to human face processing and their use as models of the brain. This study employs Convolutional Neural Networks (CNNs) and Magnetoencephalography (MEG) to explore the signatures of face recognition and familiarity, and investigate whether the face selective areas in the brain are specialized only for faces (domain-specific) or are developed for more general purposes (domain-general). Our findings reveal distinct brain responses: occipital areas distinguish faces from non-faces, while fusiform and inferior temporal areas engage in familiar face recognition. When training our ANNs on face recognition, we observe significant results supporting the idea of domain-specific brain regions for faces. Our findings shed new light on the temporal dynamics of familiar versus unfamiliar face processing in the visual cortex and more globally, highlight the potentials of combining ANNs and MEG to uncover the neural mechanisms that mediate face processing in humans.
Keywords: Face recognition Magnetoencephalography Artificial Neural Networks Similarity Analysis