Bayesian Inference for Psychometric Functions. Kuss, Jakel, Wichmann. Journal of Vision, 2005

Main goal is to do Bayesian Inference on subject data to estimate parameters of their psychometric functions (relationship between stimulus and ability of subject to discriminate between stimuli)

Because the Bayesian inference here cannot be performed analytically, MCMC is used to sample from the posterior distribution over the parameters to the function describing the psychometric curve

Also allows for confidence intervals and other things to be extracted from the posterior

Traditional methods uses ML methods and bootstrapping

Because psychophysical experiments are tiring for subjects, methods exist that find only a single pt on the psychometric function somewhere between 50% and 90% performance, called the threshold

Called adaptive methods, exist as parametric and nonparametric

In theory, the data from this could be used to estimate the entire psychometric function, but it generally doesn’t work well because the methods only try to make an estimate for a point, as opposed to the whole curve

There are also methods that attempt to estimate the entire function, but relatively few that also build confidence intervals

The psychometric function is commonly assumed to have a sigmoidal shape