September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Scene-Space Encoding within the Functional Scene-Selective Network
Author Affiliations
  • Elissa Aminoff
    Department of Psychology, Carnegie Mellon University Center for the Neural Basis of Cognition, Carnegie Mellon University
  • Mariya Toneva
    Center for the Neural Basis of Cognition, Carnegie Mellon University Department of Machine Learning, Carnegie Mellon University
  • Abhinav Gupta
    Robotics Institute, Carnegie Mellon University
  • Michael Tarr
    Department of Psychology, Carnegie Mellon University Center for the Neural Basis of Cognition, Carnegie Mellon University
Journal of Vision September 2015, Vol.15, 507. doi:
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      Elissa Aminoff, Mariya Toneva, Abhinav Gupta, Michael Tarr; Scene-Space Encoding within the Functional Scene-Selective Network. Journal of Vision 2015;15(12):507.

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

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High-level visual neuroscience has often focused on how different visual categories are encoded in the brain. For example, we know how the brain responds when viewing scenes as compared to faces or other objects – three regions are consistently engaged: the parahippocampal/lingual region (PPA), the retrosplenial complex (RSC), and the occipital place area/transverse occipital sulcus (TOS). Here we explore the fine-grained responses of these three regions when viewing 100 different scenes. We asked: 1) Can neural signals differentiate the 100 exemplars? 2) Are the PPA, RSC, and TOS strongly activated by the same exemplars and, more generally, are the “scene-spaces” representing how scenes are encoded in these regions similar? In an fMRI study of 100 scenes we found that the scenes eliciting the greatest BOLD signal were largely the same across the PPA, RSC, and TOS. Remarkably, the orderings, from strongest to weakest, of scenes were highly correlated across all three regions (r = .82), but were only moderately correlated with non-scene selective brain regions (r = .30). The high similarity across scene-selective regions suggests that a reliable and distinguishable feature space encodes visual scenes. To better understand the potential feature space, we compared the neural scene-space to scene-spaces defined by either several different computer vision models or behavioral measures of scene similarity. Computer vision models that rely on more complex, mid- to high-level visual features best accounted for the pattern of BOLD signal in scene-selective regions and, interestingly, the better-performing models exceeded the performance of our behavioral measures. These results suggest a division of labor where the representations within the PPA and TOS focus on visual statistical regularities within scenes, whereas the representations within the RSC focus on a more high-level representation of scene category. Moreover, the data suggest the PPA mediates between the processing of the TOS and RSC.

Meeting abstract presented at VSS 2015


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