July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Effects of image content and content-selective attention on the form-evoked BOLD response in the ventral visual areas: a linear sum-of-components model
Author Affiliations
  • Pinglei Bao
    Neuroscience Graduate Program, University of Southern California
  • Bosco S. Tjan
    Neuroscience Graduate Program, University of Southern California\nDepartment of Psychology, University of Southern California
Journal of Vision July 2013, Vol.13, 768. doi:10.1167/13.9.768
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      Pinglei Bao, Bosco S. Tjan; Effects of image content and content-selective attention on the form-evoked BOLD response in the ventral visual areas: a linear sum-of-components model. Journal of Vision 2013;13(9):768. doi: 10.1167/13.9.768.

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

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Abstract

The neural activity along the ventral vision pathway is modulated by attention and the category of the attended image content. Unattended content also affects the neural activity. While the interaction between attention and image content can be convoluted, we found an exceedingly simple relationship between the fMRI BOLD response and the content of a complex image. We conducted three complementary experiments in which observers had to compare either the face or scene component of a pair of briefly, successively presented images, each an amalgamation of face, scene and random noise pattern. We found that the BOLD response to each non-noise image component (face or scene, attended or unattended) is linear in the "signal proportion" of the component, defined as the ratio of the contrast energy of the component to the contrast energy of the entire image. For a cortical area along the ventral visual pathway, the slope of this linear function depends on whether the component is attended and the category specificity of the cortical area. The net BOLD response of a cortical area is a simple sum of the responses to all of the non-noise components. We validated this linear sum-of-components model by showing that a model fitted to the data from any two of the three experiments can accurately predict the empirical results of the third. The model is compatible with the biased competition theory of attention and embeds a common form of divisive normalization (the Naka–Rushton expression with an exponent of 2).

Meeting abstract presented at VSS 2013

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