New paper on Bayesian estimation of psychometric functions

The paper Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data with Heiko Schuett, Stefan Harmeling and Felix Wichmann was accepted at Vision Research. Psychometric functions play an essential role in much of behavioural neuroscience and psychophysics. In this paper, we describe efficient and accurate methods for estimating psychometric functions using Bayesian inference techniques. In contrast to previous methods, they dont require any parameter tuning or fickly MCMC, as the posterior is integrated numerically. In addition, the model also includes a term that captures `overdispersion’, which can result from fluctuations in the performance of a behaviour. A (hopefully) easy-to-use matlab implementation is available here