June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
The quick Change Detection method: Bayesian adaptive assessment of the time course of perceptual sensitivity change
Author Affiliations & Notes
  • Yukai Zhao
    Psychology, the Ohio State University, Columbus, Ohio, United States
  • Luis A Lesmes
    Adaptive Sensory Technology, San Diego, California, United States
  • Zhong-Lin Lu
    Psychology, the Ohio State University, Columbus, Ohio, United States
  • Footnotes
    Commercial Relationships   Yukai Zhao, None; Luis Lesmes, Adaptive Sensory Technology (I), Adaptive Sensory Technology (P), Adaptive Sensory Technology (E); Zhong-Lin Lu, Adaptive Sensory Technology (I), Adaptive Sensory Technology (P)
  • Footnotes
    Support  National Eye Institute (EY021553)
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 5633. doi:
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      Yukai Zhao, Luis A Lesmes, Zhong-Lin Lu; The quick Change Detection method: Bayesian adaptive assessment of the time course of perceptual sensitivity change. Invest. Ophthalmol. Vis. Sci. 2017;58(8):5633.

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

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Abstract

Purpose : Perceptual sensitivity is usually estimated over some test-time intervals, which results in imprecise and biased estimates when it changes over time. A novel procedure, the quick Change Detection (qCD) method, is developed to accurately, precisely, and efficiently quantify the full time course of perceptual sensitivity change, and demonstrated in dark adaptation.

Methods : Based on Bayesian adaptive testing (Lesmes, et al, 2010), the qCD method selects the optimal stimulus, and updates, trial by trial, a joint probability distribution of the parameters that quantify both perceptual sensitivity and its change over time. In a dark adaptation experiment, the time course of visual sensitivity change was measured with qCD and quick Forced-Choice (qFC, Lesmes, et al, 2014) in separate sessions. Each session started with a 120-second exposure to high luminance (150 cd/m2) and followed by measurement of visual sensitivity during 600 seconds of dark adaption (0.0 cd/m2). Subjects identified the location of a 1.7° diameter luminance disk that randomly occurred in one of eight locations on an imaginary circle at 5° eccentricity. With qCD, the dark adaptation curve was estimated and updated in every trial. With qFC, threshold was estimated every 10 seconds. Simulations were performed to evaluate the two methods. Accuracy was quantified as average absolute bias, and precision as the standard deviation (STD) of repeated tests and half width of the 68.2% credible interval (HWCI) from a single test.

Results : Simulations showed that the bias, the STD and 68.2% HWCI of the dark adaptation curve fell below 0.1 and 0.02 (log10 unit) after 100 and 200 seconds of qCD test, respectively. Two and four repeated qFC tests, each taking 720 seconds, were necessary to achieve similar accuracy and precision. Furthermore, a 0.02 log10 bias persisted even after 10 repeated qFC tests. The experiment showed that the estimated dark adaptation curve obtained from a single qCD test was highly consistent with the average of four repeated qFC tests.

Conclusions : The qCD method can accurately, precisely, and efficiently quantify the time course of perceptual sensitivity change, as demonstrated in dark adaptation. This method can be extended and applied to perceptual learning, where measurement of the full time course of sensitivity change is critical but cannot be repeated.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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