December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Computational brain dynamics in prosopagnosia
Author Affiliations & Notes
  • Simon Faghel-Soubeyrand
    Université de Montréal, Département de Psychologie, cerebrUM
    University of Birmingham, Center for Human Brain Health
  • Anne-Raphaelle Richoz
    Université de Fribourg, Département de Psychologie
  • Delphine Waeber
    Université de Fribourg, Département de Psychologie
  • Jessica Woodhams
    University of Birmingham, Centre for Crime, Justice and Policing
  • Frédéric Gosselin
    Université de Montréal, Département de Psychologie, cerebrUM
  • Roberto Caldara
    Université de Fribourg, Département de Psychologie
  • Ian Charest
    Université de Montréal, Département de Psychologie, cerebrUM
    University of Birmingham, Center for Human Brain Health
  • Footnotes
    Acknowledgements  This work was supported by NSERC, Mitacs and IVADO scholarships to S. F-S, by an ESRC IAAA grant to S. F-S, J. W and I. C, a NSERC Discovery grant to F. G, and an ERC-StG and NSERC Discovery grant to I. C.
Journal of Vision December 2022, Vol.22, 3418. doi:
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      Simon Faghel-Soubeyrand, Anne-Raphaelle Richoz, Delphine Waeber, Jessica Woodhams, Frédéric Gosselin, Roberto Caldara, Ian Charest; Computational brain dynamics in prosopagnosia. Journal of Vision 2022;22(14):3418.

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      © ARVO (1962-2015); The Authors (2016-present)

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The brains of the best face-recognisers (so-called “super-recognisers”) not only exhibit better mid-level visual computations, but also richer semantic computations (Faghel-Soubeyrand et al., 2021). To further examine the relationship between specific brain computations and recognition ability, we aimed to characterise the computational brain dynamics of an individual at the other end of the spectrum of face recognition abilities, PS, a well-documented case of “pure” acquired prosopagnosia. We collected a large dataset of high-density electrophysiological recordings from PS and neurotypicals (N=19 including 4 aged-matched, ~65K trials) while they completed a one-back task on a succession of face, object, animal and scene images. PS showed archetypal behavioural deficits in this setting, with a larger behavioural impairment for face vs. non-face visual categories. We used Representational Similarity Analysis (RSA) to correlate human EEG representations with those of deep neural networks (DNN) models of vision and semantics, offering a comprehensive characterisation of neural computations in PS and neurotypicals. EEG representational dissimilarity matrices (RDMs) were computed for each participant and time point using cross-validated classifiers. PS’ RDMs showed significant within-subject reliability, indicating meaningful measurements of brain representations with RSA even in the presence of significant lesions. Furthermore, PS’ brain displayed reduced face-identity evidence around 170 ms and 300 ms, as shown by lower RDM similarity with a face-identity model compared to neurotypicals. Crucially, we revealed two representational signatures of PS’ deficits: mid-level visual computations (representations in mid-layers of visual DNNs) and high-level semantic computations (representations of a DNN characterising sentence semantics). Both neuro-functional signatures were found around the N170 and P300 windows. Our results reinforce the view that common brain computations are modulated in individuals with skilled (Faghel-Soubeyrand et al., 2021), typical and now impaired face individuation ability. In other words, mid-level visual and semantic brain computations seem to be involved from prosopagnosia to super-recognition.


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