03.05.2016

New article published in the journal "Vision Research"

"Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data" by Heiko H. Schütt, Stefan Harmeling, Jakob H. Macke and Felix A. Wichmann

Abstract:

The psychometric function describes how an experimental variable, such as stimulus strength, influences
the behaviour of an observer. Estimation of psychometric functions from experimental data plays a central
role in fields such as psychophysics, experimental psychology and in the behavioural neurosciences.
Experimental data may exhibit substantial overdispersion, which may result from non-stationarity in the
behaviour of observers. Here we extend the standard binomial model which is typically used for psychometric
function estimation to a beta-binomial model. We show that the use of the beta-binomial model
makes it possible to determine accurate credible intervals even in data which exhibit substantial overdispersion.
This goes beyond classical measures for overdispersion—goodness-of-fit—which can detect
overdispersion but provide no method to do correct inference for overdispersed data. We use Bayesian
inference methods for estimating the posterior distribution of the parameters of the psychometric function.
Unlike previous Bayesian psychometric inference methods our software implementation—psignifit
4—performs numerical integration of the posterior within automatically determined bounds. This avoids
the use of Markov chain Monte Carlo (MCMC) methods typically requiring expert knowledge. Extensive
numerical tests show the validity of the approach and we discuss implications of overdispersion for
experimental design. A comprehensive MATLAB toolbox implementing the method is freely available;
a python implementation providing the basic capabilities is also available.

To see the whole article please visit our<link http: www.nip.uni-tuebingen.de publications internal-link internal link in current> publication page.