A hierarchical Bayesian approach to adaptive vision testing: A case study with the contrast sensitivity function

• Published in: Journal of vision (Charlottesville, Va.) Vol. 16; no. 6; p. 15
• Main Authors: Gu, Hairong, Kim, Woojae, Hou, Fang, Lesmes, Luis Andres, Pitt, Mark A, Lu, Zhong-Lin, Myung, Jay I
• Format: Journal Article
• Language: English
• Published: United States The Association for Research in Vision and Ophthalmology 2016
• Subjects: Adult; Bayes Theorem; Contrast Sensitivity - physiology; Female; Humans; Male; Methods; Models, Theoretical; Vision Tests - methods; Young Adult
• ISSN: 1534-7362; 1534-7362
• DOI: 10.1167/16.6.15

Measurement efficiency is of concern when a large number of observations are required to obtain reliable estimates for parametric models of vision. The standard entropy-based Bayesian adaptive testing procedures addressed the issue by selecting the most informative stimulus in sequential experimental trials. Noninformative, diffuse priors were commonly used in those tests. Hierarchical adaptive design optimization (HADO; Kim, Pitt, Lu, Steyvers, & Myung, 2014) further improves the efficiency of the standard Bayesian adaptive testing procedures by constructing an informative prior using data from observers who have already participated in the experiment. The present study represents an empirical validation of HADO in estimating the human contrast sensitivity function. The results show that HADO significantly improves the accuracy and precision of parameter estimates, and therefore requires many fewer observations to obtain reliable inference about contrast sensitivity, compared to the method of quick contrast sensitivity function (Lesmes, Lu, Baek, & Albright, 2010), which uses the standard Bayesian procedure. The improvement with HADO was maintained even when the prior was constructed from heterogeneous populations or a relatively small number of observers. These results of this case study support the conclusion that HADO can be used in Bayesian adaptive testing by replacing noninformative, diffuse priors with statistically justified informative priors without introducing unwanted bias.