October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Multivariate pattern analysis reveals domain-general enhancement of visual representations in individuals with “super-recognition” of faces
Author Affiliations & Notes
  • Simon Faghel-Soubeyrand
    University of Montreal
    University of Birmingham
  • Meike Ramon
    University of Fribourg
  • Eva Bamps
    KU Leuven
  • Matteo Zoia
    University of Fribourg
  • Jessica Woodhams
    University of Birmingham
  • Arjen Alink
    University Medical Center Hamburg-Eppendorf
  • Frédéric Gosselin
    University of Montreal
  • Ian Charest
    University of Birmingham
  • Footnotes
    Acknowledgements  Natural Sciences and Engineering Research Council of Canada (NSERC); Réseau de Bio-Imagerie du Québec (RBIQ); Mitacs
Journal of Vision October 2020, Vol.20, 502. doi:https://doi.org/10.1167/jov.20.11.502
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      Simon Faghel-Soubeyrand, Meike Ramon, Eva Bamps, Matteo Zoia, Jessica Woodhams, Arjen Alink, Frédéric Gosselin, Ian Charest; Multivariate pattern analysis reveals domain-general enhancement of visual representations in individuals with “super-recognition” of faces. Journal of Vision 2020;20(11):502. doi: https://doi.org/10.1167/jov.20.11.502.

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

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Abstract

Individual differences in human vision are ubiquitous, but poorly understood. Here, we aimed to understand the neural dynamics and representational code underlying outstanding visual processing abilities. We recruited 15 “super-recognizers” (SRs; individuals in the top 2% of face-recognition ability spectrum) and their matched-controls. Participants completed two visual tasks totalling >80,000 trials per group while we measured their brain activity with high-density EEG. We performed multivariate analyses on the time-resolved brain patterns of both groups while they identified newly learned faces. Specifically, we produced the time-course of task-related representational distances between face identities. This analysis revealed more distinct identity representations in SRs after the first feedforward sweep of the visual system (200-500ms after face-onset), accompanied by stronger face-identification performance of experimentally learned identities. These results indicate that the real-life diagnostic advantage for faces in SRs is associated with richer brain representations for face stimuli. Next, we ask whether this superior visual processing extends beyond faces, i.e. whether it is domain-general. We assessed this in a second EEG experiment during which Representational Similarity Analysis was used to characterise the representational code behind their processing of a wide set of visual stimuli (including objects, animals, scenes). This showed increased distinction between face vs non-face category representations in SRs, but also revealed more distinct representations within non-face categories, indicating an enhancement that extends beyond face stimuli. Considering the time-course of all these findings (>200ms), we propose that super-recognition is underlied by visual representations that are enriched by a recurrent, domain-general mechanism. This study provides the first evidence for a direct link between real-life visual abilities and the richness of stimulus representations in the human brain. In addition, our findings suggest that the general quality of a person’s representations, even of simple objects, predicts their ability to recognize faces of different individuals around them.

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