Monday, 10 March 2014

Stop-Signal Task: Methods, Statistics, and Applications.

The School of Psychology is proudly hosting a talk by Dr. Dora Matzke,
Department of Psychological Methods, University van Amsterdam. See
the attached flyer for details, including:

TITLE: A Bayesian parametric approach for the estimation of stop-signal
reaction time distributions.

WHEN & WHERE: Thursday 13th March 12-1pm, AVLG17 (videoconferenced to room AV3 in Ourimbah).

ABSTRACT: The cognitive concept of response inhibition is frequently measured using the stop-signal paradigm. In this paradigm, participants perform a two-choice reaction time task where, on some of the trials, the primary task is interrupted by a stop-signal that instructs participants to withhold their response. The dependent variable of interest is the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke, Dolan, Logan, Brown and Wagenmakers (2013) have developed a Bayesian parametric approach that allows for the estimation of the entire distribution of SSRTs. The Bayesian parametric approach is based on the assumptions of the horse race model and rests on the concept of censored distributions. The method assumes that SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to obtain posterior distributions for the model parameters. First, I will illustrate the use of the Bayesian parametric approach with published stop-signal data. I will then introduce BEESTS, a user-friendly software implementation of the Bayesian parametric approach that can be applied to individual as well as hierarchical data structures. I will conclude by discussing possible extensions and future research directions.