August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Neural representation of occluded objects in visual cortex
Author Affiliations
  • Courtney Mansfield
    University of East Anglia
  • Tim Kietzmann
    University of Osnabruck
  • Jasper van den Bosch
    University of Birmingham
  • Ian Charest
    Universite de Montreal
  • Marieke Mur
    Brain and Mind Institute, Western University
  • Nikolaus Kriegeskorte
    Zuckerman Institute, Columbia University
  • Fraser Smith
    University of East Anglia
Journal of Vision August 2023, Vol.23, 4594. doi:https://doi.org/10.1167/jov.23.9.4594
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      Courtney Mansfield, Tim Kietzmann, Jasper van den Bosch, Ian Charest, Marieke Mur, Nikolaus Kriegeskorte, Fraser Smith; Neural representation of occluded objects in visual cortex. Journal of Vision 2023;23(9):4594. https://doi.org/10.1167/jov.23.9.4594.

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

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Abstract

The ability of the human visual system to recognize occluded objects is striking, yet current models of vision struggle to account for this successfully. Previous studies investigating occlusion at both the behavioural and neural levels typically used simple shapes or cut outs as occluders, rather than other objects. The goal of the present study was to understand what best explains neural representations of occluded objects under more realistic occlusion i.e., when objects occlude other objects. We approached this by explicitly relating activity patterns of occluded objects (e.g. a cup occluding a face) with those generated when viewing the same objects in isolation (the cup or the face). In an event-related fMRI design, participants (N=12) performed a one-back task while being presented with objects presented in isolation (un-occluded), occluded by another object, or cut out by a corresponding object silhouette. We defined anatomical regions of interest in EVC (V1-V3), mid-visual regions (V4/LO1-3) and IT. Decoding analyses showed that EVC responses to occluded objects were better determined by the visible features whereas in IT inferred features also explained the responses well. Our data also showed strong effects of competition across multiple object representations in EVC, although these were significantly weaker in IT. Separate linear regression analyses further showed that the weights assigned to occluded objects in IT were well predicted by independent categorization judgements (higher weights corresponded to lower accuracy and slower RT). Whereas in EVC weights instead were predicted by the magnitude of occlusion, with smaller weights assigned as the percentage of object occluded increases. In sum our results demonstrate that IT better decouples responses to real-world occluded objects with robust representations evident across multiple competing objects. Thus, our data support the importance of investigating neural mechanisms underlying object recognition under more naturalistic occlusion scenarios.

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