October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Ideal spatial summation is contrast dependent
Author Affiliations & Notes
  • Chien-Chung Chen
    National Taiwan University
  • Christopher Tyler
    Smith-Kettlewell Eye Research Institute
  • Footnotes
    Acknowledgements  MOST(Taiwan) 108-2410-H-002 -105 -MY2
Journal of Vision October 2020, Vol.20, 206. doi:https://doi.org/10.1167/jov.20.11.206
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      Chien-Chung Chen, Christopher Tyler; Ideal spatial summation is contrast dependent. Journal of Vision 2020;20(11):206. doi: https://doi.org/10.1167/jov.20.11.206.

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

  • Supplements

We investigated the mechanisms underlying spatial summation with a masking paradigm. The targets were Gabor patterns placed at 3-deg eccentricity to either the left or right of the fixation and elongated along an arc of the same radius. The mask was either a concentric (iso-orientation mask) or a radial (orthogonal mask) Gabor pattern embedded in a ring with the same 3-deg center radius. The observers indicated whether the target in each trial was on the left or the right of the fixation. The Ψ staircase procedure was used to measure the threshold at 75% accuracy. With either the orthogonal or the low contrast iso-orientation masks, the target threshold first decreased with size with slope -1 up to a target length of 45’ (half-height full-width; HHFW) and further decreased with slope -1/2 on log-log coordinates. The latter is the signature of an ideal summation process with local independent noise. With a high-contrast iso-orientation mask, the target threshold, while showing the -1 slope up to 45’ HHFW, remained constant from 45’ to 210’ HHFW, suggesting that the presence of the mask eliminated summation. Beyond 210’, however, masked thresholds further decreased with -1 slope, suggesting the existence of a highly elongated summation channel that is not revealed by a conventional spatial summation paradigm. Our results can be explained by a divisive inhibition model in which a second-order filter sums responses across the linear excitation of the local channels raised by a power, and is rescaled by divisive inhibition from all local image components. Such divisive inhibition from the high-contrast iso-orientation masks swamps the response and eliminates the target size effects for ideal summation. The decision variable is a nonlinear combination of the the second order filter and the elongated filter responses.


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