September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Task-Dependent Information Compression in Face, Object and Scene Categorization
Author Affiliations
  • Katarzyna Jaworska
    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
  • Oliver Garrod
    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
  • Nicola van Rijsbergen
    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
  • Arjen Alink
    Department of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
  • Ian Charest
    School of Psychology, University of Birmingham, Birmingham, UK
  • Philippe Schyns
    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
Journal of Vision September 2018, Vol.18, 325. doi:10.1167/18.10.325
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      Katarzyna Jaworska, Oliver Garrod, Nicola van Rijsbergen, Arjen Alink, Ian Charest, Philippe Schyns; Task-Dependent Information Compression in Face, Object and Scene Categorization. Journal of Vision 2018;18(10):325. doi: 10.1167/18.10.325.

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

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Abstract

Every day, we encounter multiple visual scenes, which contain vast amounts of information that must be selectively attended or inhibited to avoid sensory overload. Here, we sought to understand the selective information contents that underlie successful categorization of faces, objects and scenes within the same images. This approach confers the advantage of isolating the effects to the active observer resolving a categorization task because the input to the visual system is constant across tasks. Five observers each performed five categorization tasks across 4,482 trials (facial expression, identity, general scene, specific scene, and object) on the same set of complex naturalistic selfie images. We decomposed each selfie image with Gabor features at 6 orientations, 7 spatial scales, and 3,108 spatial locations. We kept the top 35% of the Gabor features as specified by power ranking range averaged across all selfies. Then, we randomly sampled 5% of the Gabor features (i.e. ~2,400 Gabor features) to produce a sparse stimulus shown on an individual trial (see Supplementary Figures). Following the experiment, independently for each observer we used binary linear regressions to reverse correlate the single trial relationship between random sampling of Gabor coefficients and the face, object or scene categorization responses in the task. We demonstrate selective task-dependent information compression. On average, observers used 10.38% of available Gabor features for categorizing expressions, 16.94% for identity, 39.62% for objects, 90.22% for general scenes, and 94.56% for specific scenes. Task-dependent information compression reveals the specific face, object and scene features that the brain must differentially represent from the same images to achieve successful categorization behavior. Therefore, as categorization tasks change the information content that the brain must process, they should play a prominent role in explanations of information processing mechanisms in brain and artificial networks.

Meeting abstract presented at VSS 2018

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