December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Adaptation to the slope of the amplitude spectrum in modified reality
Author Affiliations
  • Bruno Richard
    Rutgers University - Newark
  • Patrick Shafto
    Rutgers University - Newark
Journal of Vision December 2022, Vol.22, 3338. doi:
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      Bruno Richard, Patrick Shafto; Adaptation to the slope of the amplitude spectrum in modified reality. Journal of Vision 2022;22(14):3338.

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

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Scenes contain many statistical regularities that could benefit visual processing if accounted for by the visual system. One such statistic to consider is the orientation-averaged slope of the amplitude spectrum of natural scenes. Human observers show different discrimination sensitivity ⍺ values that peak for values between 1.0 and 1.2 and fall as ⍺ is steepened or shallowed. The range of ⍺ for peak discrimination sensitivity is concordant with the average ⍺ of natural scenes, which may indicate an ideal processing range, whereby visual mechanisms are optimized to process information within a narrow range of ⍺. Here we explore the association between peak discrimination sensitivity and the most viewed ⍺s in natural environments. Specifically, we verified if discrimination sensitivity depends on the recently viewed environments. Observers were immersed, using a Head-Mounted Display, in an environment that was either unaltered or had its average ⍺ steepened or shallowed by 0.4. Discrimination thresholds were affected by the average shift in ⍺. Steeper environments decreased thresholds for steep reference ⍺s, while shallower environments decreased thresholds for shallow reference ⍺s. We modeled these data with a Bayesian observer model and explored how different priors may influence the ability of the model to fit observer thresholds. Change in discrimination thresholds following adaptation could be explained by a shift in the mode of the prior concordant with the shift in the environment, in addition to a change in the likelihood. Our findings suggest that the prior for ⍺ is associated to the ⍺ of recently viewed environments and sufficiently malleable to accommodate for different environments with different ⍺s.


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