Bayesian Method for Repeated Threshold Estimation

Abstract:

We propose a method for estimating perceptual learning curves from long sequences of low-quality data. Perceptual learning experiments require naive observers and very long schedules. Motivation is low and lapse rates are high. The good news is that there are thousands of data points. Our objective is to track the threshold of interest with as fine temporal resolution as possible. The method uses all available data to obtain a joint posterior distribution of the lapse rate and other "nuisance" parameters of the psychometric function. With this information, the threshold can be estimated reliably from short segments of the data. A MATLAB routine approximates the Bayesian distributions on a 3-dimensional grid, assuming a Weibull psychometric function. Monte Carlo simulations validate the method and compare it with standard methods that treat each data segment independently. The method is applied on behavioral data from a perceptual learning study of orientation discrimination of Gabor patches embedded in visual noise.

Slides (pdf)