August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Facial identity encoding, face space structure and neural-based image reconstruction in congenital prosopagnosia.
Author Affiliations
  • Dan Nemrodov
    Department of Psychology at Scarborough, University of Toronto
  • Adrian Nestor
    Department of Psychology at Scarborough, University of Toronto
  • Galia Avidan
    Department of Psychology, Ben-Gurion University of the Negev
  • David Plaut
    Department of Psychology, Carnegie Mellon University
  • Marlene Behrmann
    Department of Psychology, Carnegie Mellon University
Journal of Vision September 2016, Vol.16, 1234. doi:10.1167/16.12.1234
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      Dan Nemrodov, Adrian Nestor, Galia Avidan, David Plaut, Marlene Behrmann; Facial identity encoding, face space structure and neural-based image reconstruction in congenital prosopagnosia.. Journal of Vision 2016;16(12):1234. doi: 10.1167/16.12.1234.

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

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Abstract

The neural correlates of facial recognition deficits in congenital prosopagnosia (CP) have been extensively researched and debated. Here, we investigate the neural basis of face identification in two CP individuals with the aid of pattern analysis applied to a comprehensive set of neuroimaging (fMRI) and behavioral data. To this end, first, we employ information-based mapping to localize cortical regions able to support identity discrimination in these individuals. Second, we use behavioral and neural-based confusability matrices derived from activation patterns in such regions to estimate the structure and properties of individual-specific face space. And last, we exploit the resulting face space estimates for the purpose of facial image reconstruction separately from behavioral and neural data. Our results indicate that: (i) facial identity encoding in CP relies on a cortical network comparable to that found in normal controls but the magnitude of pattern discrimination appears to be comparatively lower in specific regions; (ii) the structure of face space in CP can be recovered from behavioral and neural data (but less robustly estimated than in the normal population); and (iii) this structure supports above-chance image reconstruction results thus helping to visualize face percepts in a visually impaired population. In summary, the present findings shed new light on the neural profile of visual face encoding in CP while they also showcase the benefit of new methodological tools (e.g., image reconstruction) in elucidating the nature and the specifics of high-level visual representations.

Meeting abstract presented at VSS 2016

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