September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Two distinct scene processing networks connecting vision and memory
Author Affiliations
  • Christopher Baldassano
    Department of Computer Science, Stanford University
  • Andre Esteva
    Department of Computer Science, Stanford University
  • Diane Beck
    Psychology Department and Beckman Institute, University of Illinois Urbana-Champaign
  • Li Fei-Fei
    Department of Computer Science, Stanford University
Journal of Vision September 2015, Vol.15, 571. doi:10.1167/15.12.571
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      Christopher Baldassano, Andre Esteva, Diane Beck, Li Fei-Fei; Two distinct scene processing networks connecting vision and memory. Journal of Vision 2015;15(12):571. doi: 10.1167/15.12.571.

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

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Abstract

Research on visual scene understanding has identified a number of regions involved in processing natural scenes, but has lacked a unifying framework for understanding how these different regions are organized and interact. We propose a new organizational principle, in which scene processing relies on two distinct networks at the edge of visual cortex. The first network consists of the Transverse Occipital Sulcus (TOS, or the Occipital Place Area) and the posterior portion of the Parahippocampal Place Area (PPA). These regions have a well-defined retinotopic organization and do not show strong memory or context effects, suggesting that this network primarily processes visual features from the current view of a scene. The second network consists of the caudal Inferior Parietal Lobule (cIPL), Retrosplenial Cortex (RSC), and the anterior portion of the PPA. These regions are involved in a wide range of both visual and non-visual tasks involving episodic memory, navigation, imagination, and default mode processing, and connect information about a current scene view with a much broader temporal and spatial context. We provide evidence for this division from a diverse set of sources. Using a data-driven approach to parcellate resting-state fMRI data, we identify coherent functional regions corresponding to scene-processing areas. We then show that a network clustering analysis separates these scene-related regions into two adjacent networks, which exhibit sharp changes in connectivity properties across their narrow border. Additionally, we argue that the cIPL has been previously overlooked as a critical region for full scene understanding, based on a meta-analysis of previous functional studies as well as diffusion tractography results showing that cIPL is well-positioned to connect visual cortex with many other cortical systems. This new framework for understanding the neural substrates of scene processing bridges results from many lines of research, and makes specific predictions about functional properties of these regions.

Meeting abstract presented at VSS 2015

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