Neural computations in prosopagnosia
We aimed to identify neural computations underlying the loss of face identification ability by modelling the brain activity of brain-lesioned patient PS, a well-documented case of acquired pure prosopagnosia. We collected a large dataset of high-density electrophysiological (EEG) recordings from PS and neurotypicals while they completed a one-back task on a stream of face, object, animal and scene images. We found reduced neural decoding of face identity around the N170 window in PS, and conjointly revealed normal non-face identification in this patient. We used Representational Similarity Analysis (RSA) to correlate human EEG representations with those of deep neural network (DNN) models of vision and caption-level semantics, offering a window into the neural computations at play in patient PSs deficits. Brain representational dissimilarity matrices (RDMs) were computed for each participant at 4 ms steps using cross-validated classifiers. PSs brain RDMs showed significant reliability across sessions, indicating meaningful measurements of brain representations with RSA even in the presence of significant lesions. Crucially, computational analyses were able to reveal PSs representational deficits in high-level visual and semantic brain computations. Such multi-modal data-driven characterisations of prosopagnosia highlight the complex nature of processes contributing to face recognition in the human brain. HighlightsO_LIWe assess the neural computations in the prosopagnosic patient PS using EEG, RSA, and deep neural networks C_LIO_LINeural dynamics of brain-lesioned PS are reliably captured using RSA C_LIO_LINeural decoding shows normal evidence for non-face individuation in PS C_LIO_LINeural decoding shows abnormal neural evidence for face individuation in PS C_LIO_LIPS shows impaired high-level visual and semantic neural computations C_LI