September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Linking brain activity to visual attentional behavior considering multiple spatial-scales of measurement
Author Affiliations
  • Justin Gardner
    Department of Psychology, Stanford University
Journal of Vision September 2015, Vol.15, 1412. doi:10.1167/15.12.1412
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      Justin Gardner; Linking brain activity to visual attentional behavior considering multiple spatial-scales of measurement. Journal of Vision 2015;15(12):1412. doi: 10.1167/15.12.1412.

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

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Abstract

Understanding the human neural mechanisms that underly behavioral enhancement due to visual spatial attention requires synthesis of knowledge gained across many different spatial scales of measurement and species. Our lab has focused on the measurement of contrast-response and how it changes with attention in humans. Contrast is a key visual variable in that it controls visibility and measurements from single-units to optical-imaging to fMRI find general consistency in that cortical visual areas respond in monotonically increasing functions to increases in contrast. Building on this commonality across multiple spatial-scales of measurement, we have implemented computational models that predict behavioral performance enhancement from fMRI measurements of contrast-response, in which we tested various linking hypotheses, from sensory enhancement, noise reduction to efficient selection. Our analysis of the human data using fMRI suggested a prominent role for efficient selection in determining behavior. Our work is heavily informed by the physiology literature particularly because some properties of neural response, such as efficiency of synaptic transmission or correlation of activity are difficult if not impossible to determine in humans. Nonetheless, discrepancies across measurements suggests potential difficulties of interpretation of results from any single measurement modality. We will discuss our efforts to address these potential discrepancies by adapting computational models used to explain disparate effects across different single-unit studies to larger spatial-scale population measures such as fMRI.

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