September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Computational Study of Changes to Cortical Vision with Age
Author Affiliations
  • Sarah Cavanagh
    Department of Integrative Neuroscience, Fordham University
  • Daniel Leeds
    Department of Integrative Neuroscience, Fordham UniversityDepartment of Computer and Information Sciences, Fordham University
Journal of Vision September 2018, Vol.18, 770. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Sarah Cavanagh, Daniel Leeds; Computational Study of Changes to Cortical Vision with Age. Journal of Vision 2018;18(10):770. doi:

      Download citation file:

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

  • Supplements

Computational models of cortical visual perception have become a major area of focus in recent years. Convolutional neural networks currently dominate the field, reporting significant abilities to account for cortical representations from V1 to inferotemporal cortex (e.g., Yamins 2014). However, Medial Axis/Shock Graph representations is a more compact visual model that shows promise explaining activity in targeted cortical regions (e.g., Leeds 2013 and Lescroart 2013). The varying matching strengths between visual model representations and cortical region representations suggest diverse cognitive strategies employed across the brain, and across subjects. Correlates of age and cognitive acumen with strategies for visual representation have been explored to some extent, but are generally limited to age-related changes to the size of cortical receptive fields (Brewer 2014, Chang 2015), rather than broader shifts in encoding strategies. In the present study, we explore the effects of age on shifting representations employed in cortical vision. We adapt behavioral data and fMRI neuroimaging data from Stern (2014) to model cortical responses to 111 line patterns from six subjects aged 20 through 80. Subjects performed a pattern comparison task in the scanner. We divide line stimuli into seven classes of increasing shape complexity, computed by the Shock Graph vision model (Kimia 1995). Voxel activity was correlated with the onset of stimuli from each class separately. A division in voxel-level preferred stimulus groups was observed in the early and mid-level ventral visual pathway, favoring more complex shapes at earlier stages. Locations of "simple" versus "complex" class-selective voxels in visual cortex varied with unclear connection to age. However, simple shapes appeared to more strongly correlate to activity in older subjects and complex shapes appeared to more strongly correlate to activity in younger subjects. Shock-graph representations indicate shape-based cortical selectivities in early and mid-level vision, with potential variations influenced by age.

Meeting abstract presented at VSS 2018


This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.