Bayesian Inference of Two-Dimensional Contrast Sensitivity Functions

The contrast sensitivity function (CSF) is crucial in predicting functional vision both in research and clinical areas. Recently, a group of novel strategies, multi-dimensional adaptive methods, were proposed and allowed more rapid measurements when compared to usual methods such as Ψ or staircase. Our study further presents a multi-dimensional Bayesian framework to estimate parameters of the CSF from experimental data obtained by classical sampling. We extensively simulated the framework's performance as well as validated the results in a psychophysical experiment. The results showed that the Bayesian framework significantly improves the accuracy and precision of parameter estimates from usual strategies, and requires about the same number of observations as the novel methods to obtain reliable inferences. Additionally, the improvement with the Bayesian framework was maintained when the prior poorly matched the observer's CSFs. The results indicated that the Bayesian framework is flexible and sufficiently precise for estimating CSFs.