September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Deep-net-derived surface estimations from natural scenes predict voxel responses in scene-selective cortex
Author Affiliations & Notes
  • Anna Shafer-Skelton
    University of California, San Diego
  • Timothy Brady
    University of California, San Diego
  • John T Serences
    University of California, San Diego
  • Footnotes
    Acknowledgements  NSF GRFP and APA Dissertation Award awarded to AS, NSF BCS-1653457 awarded to TFB, and R01-EY025872 awarded to JTS
Journal of Vision September 2021, Vol.21, 2805. doi:https://doi.org/10.1167/jov.21.9.2805
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Anna Shafer-Skelton, Timothy Brady, John T Serences; Deep-net-derived surface estimations from natural scenes predict voxel responses in scene-selective cortex. Journal of Vision 2021;21(9):2805. doi: https://doi.org/10.1167/jov.21.9.2805.

      Download citation file:


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

      ×
  • Supplements
Abstract

Our world is full of diverse types of visual information, yet visual attention and memory experiments focus almost exclusively on understanding attention/memory of basic visual features and discrete objects, in part because our understanding of how people represent scene surface information cognitively and neurally is very limited. Recent work (e.g., Lescroart & Gallant, 2019) has made headway in quantifying such surface representations, finding 3D surface information in scene-selective cortex, yet their results were limited to artificially generated images for which ground-truth 3D information exists. Here, we use DNNs (Zamir et al., 2018) to estimate ground-truth distance and surface-direction information based only on RGB stimulus images in the publicly available BOLD5000 fMRI data set. This procedure yielded artifact-free distance and surface-direction estimates for 978 of the 1000 scene images in the BOLD5000 stimulus set. Using a similar encoding model to Lescroart & Gallant (2019), we found significant predictions of voxel responses in scene-selective cortex (occipital place area and parahippocampal place area: 3/3 participants significant; retrosplenial complex: 2-3/3 participants significant depending on hemisphere). These results lay the foundation for investigating scene surface processing in more naturalistic environments and tasks, a critical step towards understanding visual and cognitive processes in the real world.

×
×

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.

×