September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Over-Connectivity in the Face-Processing Network is Related to Weaker Face Recognition Ability
Author Affiliations
  • Daniel Elbich
    Department of Psychology, Penn State University Social, Life, and Engineering Sciences Imaging Center, Penn State University
  • Suzy Scherf
    Department of Psychology, Penn State University Social, Life, and Engineering Sciences Imaging Center, Penn State University
Journal of Vision September 2015, Vol.15, 166. doi:10.1167/15.12.166
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      Daniel Elbich, Suzy Scherf; Over-Connectivity in the Face-Processing Network is Related to Weaker Face Recognition Ability. Journal of Vision 2015;15(12):166. doi: 10.1167/15.12.166.

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

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Abstract

Face recognition is a complex behavioral skill and requires a distributed neural network. There are limited findings linking individual differences in activation within distinct nodes of this network (e.g., FFA) and recognition behavior, which may be related to the notion that complex behavior likely emerges from activation across distributed networks and less from individual regions within networks. Importantly, there is no work investigating whether variations in face recognition behavior are related to variations in the patterns of functional connections within the face-processing network. To address this issue, we developed a battery of face recognition tasks that are highly sensitive to individual differences in face recognition behavior in typically developing young adults. We identified 40 individuals who varied in face recognition behavior on a continuum that spanned ± 1 SD around the mean of a sample of more than 200 individuals tested in this battery. These participants were scanned in an fMRI task in which they passively viewed blocks of dynamic movies of faces, objects, and navigational scenes. Regions in the face-processing network were functional defined at the group level and then fit to each participant’s individual activation. For connectivity, the best-fit model for 13 regions was assessed using unified structural equation modeling separately for high (> 1 SD), average (within 1SD), and low (< 1 SD) performers. Low performers had networks with more edges, higher global efficiency, and shorter path lengths compared to average and high performers. Furthermore, each of these network properties negatively correlated with behavioral performance on the face recognition tasks across the entire sample. In sum, people with weaker face recognition abilities have vastly over-connected and redundant network topologies. In contrast, relatively stronger recognizers have more sparsely connected networks. These results suggest that distributed, but not overly redundant, functional organization is required for proficient behavior.

Meeting abstract presented at VSS 2015

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