August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Bi-phasic filter model can account for the Transient Twinkle Perception
Author Affiliations & Notes
  • Chang yeong Han
    Department of Biomedical Engineering, Ulsan National Institute of Science Technology, Ulsan 44919, South Korea
  • Seonggyu Choe
    Department of Biomedical Engineering, Ulsan National Institute of Science Technology, Ulsan 44919, South Korea
  • Hyosun Kim
    R&D center, Samsung Display, South Korea
  • Oh-Sang Kwon
    Department of Biomedical Engineering, Ulsan National Institute of Science Technology, Ulsan 44919, South Korea
  • Footnotes
    Acknowledgements  This work was supported by the Samsung Display Research Center.
Journal of Vision August 2023, Vol.23, 5219. doi:https://doi.org/10.1167/jov.23.9.5219
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Chang yeong Han, Seonggyu Choe, Hyosun Kim, Oh-Sang Kwon; Bi-phasic filter model can account for the Transient Twinkle Perception. Journal of Vision 2023;23(9):5219. https://doi.org/10.1167/jov.23.9.5219.

      Download citation file:


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

      ×
  • Supplements
Abstract

Humans can detect the transition of frequencies between two sequentially presented flickering stimuli even when the two stimuli appear to be steady, called the Transient Twinkle Perception (TTP). Nakajima and Sakaguchi (2015) suggested that a temporal integration model with a gaussian filter can explain the TTP. However, low-level temporal integration of the visual system was characterized by a bi-phasic filter (Kelly, 1971) especially when the mean luminance is relatively high as in the TTP study (65cd/m^2). We measured the magnitudes of TTPs in various conditions to critically evaluate the bi-phasic and the gaussian filter models. In Experiment 1, we examined the effect of the frequency difference between the first (72, 120Hz) and second epochs (60, 72, 80, 90, 120, and 144Hz) of stimuli on the TTP. The duration of stimuli (a circle diameter 1 ˚, mean luminance: 98cd/m^2) varies between 1000 and 1225 ms depending on conditions. The contrast threshold for the TTP increased as the frequency difference between the two epochs decreased (First epoch: 72Hz: adjusted r=0.71, p=1.26e^(-15), 120Hz: adjusted r=0.83, p<2.20e^(-16)). In Experiment 2, we inserted ‘in between’ frequency epochs to gradually change the flickering frequency, and found that the contrast threshold is significantly higher than the result of Experiment 1 (72Hz: p=3.98e^(-4), 120Hz: p=2.38e^(-4)). In Experiment 3, we modulated the speed of frequency changes by varying the length of ‘in between’ epochs. Interestingly, a complex pattern emerged: The magnitude of TTP was the lowest at the fastest speed (1 frame/epoch) and fluctuated as the length of epochs increased. Both the bi-phasic filter and gaussian filter models can explain the results of Experiment 1 and 2. However, only the by-phasic filter model can explain the complex pattern from Experiment 3. The TTP is a natural byproduct of low-level visual temporal integration which is characterized by a bi-phasic filter.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×