August 2014
Volume 14, Issue 10
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Early Visual Cortex Assigns Border Ownership in Natural Scenes According to Image Context
Author Affiliations
  • Jonathan R Williford
    Department of Neuroscience, Johns Hopkins University, School of Medicine
  • Rudiger von der Heydt
    Department of Neuroscience, Johns Hopkins University, School of Medicine
Journal of Vision August 2014, Vol.14, 588. doi:10.1167/14.10.588
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      Jonathan R Williford, Rudiger von der Heydt; Early Visual Cortex Assigns Border Ownership in Natural Scenes According to Image Context. Journal of Vision 2014;14(10):588. doi: 10.1167/14.10.588.

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

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Abstract

Discerning objects from their backgrounds is a fundamental process of vision. The coding of border ownership in the early visual cortex is a neural correlate of this process. When stimulated with the contour of a figure, neurons with this correlate respond more strongly when the figure is on one side of their receptive field (the "preferred" side) versus the other (Williford & von der Heydt: Scholarpedia 8(10):30040, 2013). So far, border ownership coding has only been shown with simple displays of geometric shapes (e.g., squares). Here we studied border ownership coding with static images of natural scenes, using microelectrodes to record from isolated neurons in V1 and V2 of macaques. We found that subsets of V1 and V2 neurons indeed code for border ownership in complex natural scenes. Decomposition of local and context influences showed that the context-based border ownership signals correlated with those for the (locally ambiguous) edge of a square, but were weaker. We used stimuli with intermediate complexity along several dimensions to measure the relative influences of object shape, occlusion between objects, texture and color contrast to determine how they contribute to the border ownership signal strength. We found that border ownership signal decreases with the stimulus complexity. This was especially pronounced when comparing a simple isolated square with a C-shape, overlapping squares, and natural stimuli. There were also smaller decreases when changing from uniform squares to natural texture squares and from squares to silhouettes of natural shapes. In conclusion, subsets of neurons in V1 and V2 do code for the border ownership in natural scenes, however, the strength and accuracy of these early estimates of border ownership decreases with the complexity of the visual stimulus.

Meeting abstract presented at VSS 2014

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