June 2007
Volume 7, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   June 2007
Decoding distributed patterns of activity associated with natural scene categorization
Author Affiliations
  • Eamon Caddigan
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, and Department of Psychology, University of Illinois at Urbana-Champaign
  • Dirk Walther
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
  • Justas Birgiolas
    Department of Psychology, University of Illinois at Urbana-Champaign, and Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
  • Jonathan Weissman
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
  • Diane Beck
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, and Department of Psychology, University of Illinois at Urbana-Champaign
  • Li Fei-Fei
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, and Department of Computer Science, Princeton University
Journal of Vision June 2007, Vol.7, 765. doi:10.1167/7.9.765
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      Eamon Caddigan, Dirk Walther, Justas Birgiolas, Jonathan Weissman, Diane Beck, Li Fei-Fei; Decoding distributed patterns of activity associated with natural scene categorization. Journal of Vision 2007;7(9):765. doi: 10.1167/7.9.765.

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

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Abstract

Human observers are able to quickly and efficiently perceive the content of natural scenes (Potter, 1976). Previous studies have examined the time course of this rapid classification (Thorpe et al, 1996) as well as the brain regions activated when subjects categorize natural scenes (Epstein & Higgins, 2006). Using statistical pattern recognition algorithms similar to those employed by Cox and Savoy (2002) to decode the neural states associated with object categories, we asked whether we can identify and discriminate distributed patterns of fMRI activity associated with particular natural scene categories (beaches, mountains, forests, tall buildings, highways, and industrial scenes). fMRI data was aquired while subjects viewed 100 images from each of six categories, in 6 blocks of 10 images each of the same category, organized into 10 runs. A subset of the voxels was selected via traditional univariate statistics comparing stimulus presentation versus blank screen conditions. We tested several pattern recognition algorithms (e.g. Support Vector Machines, Gaussian Naive Bayes, etc.) in a leave-one-run-out procedure on the selected voxels and found that all algorithms predict the natural scene category seen by the subject well above chance. Furthermore, prediction accuracy was still well above chance when retinotopic cortex was excluded from the analysis, suggesting that this multi-voxel analysis does not rely solely on differences in simple visual features or differences in the retinotopic representation of the stimuli.

Caddigan, E. Walther, D. Birgiolas, J. Weissman, J. Beck, D. Fei-Fei, L. (2007). Decoding distributed patterns of activity associated with natural scene categorization [Abstract]. Journal of Vision, 7(9):765, 765a, http://journalofvision.org/7/9/765/, doi:10.1167/7.9.765. [CrossRef]
Footnotes
 Financial support for this work was provided by UIUC Critical Research Initiative Planning Grant (to DB and LF) and a Beckman Postdoctoral Fellowship to DW
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