September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Structural, not spectral, representation of shape in lateral occipital complex
Author Affiliations
  • Haluk Tokgozoglu
    Department of Computer Science, Johns Hopkins University
  • Anthony Sali
    Department of Psychological and Brain Sciences, Johns Hopkins University
  • Brian Anderson
    Department of Psychological and Brain Sciences, Johns Hopkins University
  • Steven Yantis
    Department of Psychological and Brain Sciences, Johns Hopkins University
  • Charles Connor
    Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Zanvyl Krieger Mind/Brain Institute
Journal of Vision September 2015, Vol.15, 244. doi:10.1167/15.12.244
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      Haluk Tokgozoglu, Anthony Sali, Brian Anderson, Steven Yantis, Charles Connor; Structural, not spectral, representation of shape in lateral occipital complex. Journal of Vision 2015;15(12):244. doi: 10.1167/15.12.244.

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

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Abstract

Neural recording studies in monkeys have shown that shape representation in high-level ventral pathway cortex (including inferotemporal cortex or IT) is structural: ensembles of neurons encode shapes as spatial configurations of structural fragments. The putative homologue for IT in the human brain is the lateral occipital complex (LOC), which is defined by differential responses to intact and scrambled object photographs. Here, we used fMRI to test whether shape representation in human LOC is similarly structural, or is better explained by spatial frequency tuning, which has been the standard quantitative model for shape sensitivity in human cortex. Our stimuli were letter-like combinations of medial axis fragments (straight and curved line segments). Stimuli were presented in random order while subjects performed a one-back shape-matching task. We used a generalized linear model (GLM) to estimate the response pattern of each LOC voxel across stimuli. We parameterized the stimuli as combinations of medial axis fragments, defined by their curvatures, orientations, and object-relative positions. For each voxel, we used stepwise regression to fit a structural tuning model that explained stimulus responses in terms of sensitivity to component fragments. Model complexity was limited to components that explained at least 5% additional variance. Typical models comprised 3–6 components and explained 45–65% of the response variance across stimuli. In contrast, comparable spectral models, based on components from a Gabor wavelet pyramid decomposition of the stimuli, typically explained only 5–15% of the response variance. The explanatory power of the structural models suggests that LOC represents shapes as configurations of structural components. These results also support the homology between human LOC and monkey IT, where neural mechanisms of shape coding can be studied in detail.

Meeting abstract presented at VSS 2015

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