December 2023
Volume 23, Issue 15
Open Access
Optica Fall Vision Meeting Abstract  |   December 2023
Poster Session I: Binocular contrast integration: Cortical and behavioral signals reflect different computations
Author Affiliations
  • Kimberly Meier
    Department of Psychology, University of Washington
  • Mark Pettet
    Department of Psychology, University of Washington
  • Taylor Garrison
    Department of Psychology, University of Washington
  • Kristina Tarczy-Hornoch
    Department of Ophthalmology, University of Washington
  • Geoffrey M. Boynton
    Department of Psychology, University of Washington
  • Ione Fine
    Department of Psychology, University of Washington
Journal of Vision December 2023, Vol.23, 30. doi:https://doi.org/10.1167/jov.23.15.30
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      Kimberly Meier, Mark Pettet, Taylor Garrison, Kristina Tarczy-Hornoch, Geoffrey M. Boynton, Ione Fine; Poster Session I: Binocular contrast integration: Cortical and behavioral signals reflect different computations. Journal of Vision 2023;23(15):30. https://doi.org/10.1167/jov.23.15.30.

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

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Abstract

Purpose: Although binocular contrast perception under dichoptic viewing conditions has been extensively characterized behaviorally, little is known about how the signals from each eye are combined in cortex. Here we compared simultaneously-collected behavioral and EEG measures of dichoptic contrast perception. Methods: Observers (n=16) dichoptically viewed a 2-cpd grating flickering at 7.5 Hz and rotating slowly (1º/s). The contrast of the grating shown to each eye modulated sinusoidally over time at independent rates (1/6 and 1/8 Hz). We recorded EEG activity while observers positioned a joystick lever to report perceived contrast as it changed over time. Analysis: A multiband filter was used to isolate the SSVEP responses to 7.5 Hz signals from electrode Oz (occipital pole), and the standard deviation of this signal provided a measure of neural response amplitude as a function of contrast. Results: A simple model was fit to VEP and behavioral responses, [(L^m+R^m)/2]^(1/m)], which essentially characterized whether responses were better fit by a mean (m≈1) or a max (m>1) model. VEP responses (m = 1.0; MSE = 0.013) were well fit by a mean model, suggesting the EEG signal may have been driven by the input layers of V1. In contrast, behavioral responses (m = 8.8; MSE = 0.011) were well fit by a model that was heavily shifted towards a max model, suggesting a significant non-linear transformation of the contrast signal between the input layers of V1 and conscious perception.

Footnotes
 Funding: Funding: Knights Templar Eye Foundation, Research to Prevent Blindness, Unrestricted grant from Research to Prevent Blindness to UW Department of Ophthalmology
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