Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions
- 1Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
- 2Laboratory of Neural Computation and Cognition, Brown University, Providence, RI, USA
- 3School of Psychology, University of Newcastle, Callaghan, NSW, Australia
- 4Department of Psycholgy, Vanderbilt University, Nashwille, TN, USA
The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time (RT) task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their response. The primary goal is to estimate the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke et al. (2013) have developed a Bayesian parametric approach (BPA) that allows for the estimation of the entire distribution of SSRTs. The BPA assumes that SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to estimate the parameters of the SSRT distribution. Here we present an efficient and user-friendly software implementation of the BPA—BEESTS—that can be applied to individual as well as hierarchical stop-signal data. BEESTS comes with an easy-to-use graphical user interface and provides users with summary statistics of the posterior distribution of the parameters as well various diagnostic tools to assess the quality of the parameter estimates. The software is open source and runs on Windows and OS X operating systems. In sum, BEESTS allows experimental and clinical psychologists to estimate entire distributions of SSRTs and hence facilitates the more rigorous analysis of stop-signal data.
Keywords: stop-signal paradigm, stop-signal RT distribution, ex-Gaussian distribution, hierarchical Bayesian modeling, statistical software
Citation: Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions. Front. Psychol. 4:918. doi: 10.3389/fpsyg.2013.00918
Received: 23 September 2013; Paper pending published: 29 October 2013;
Accepted: 19 November 2013; Published online: 10 December 2013.
Edited by:Holmes Finch, Ball State University, USA
Reviewed by:Richard S. John, University of Southern California, USA
Martin Lages, University of Glasgow, UK
Copyright © 2013 Matzke, Love, Wiecki, Brown, Logan and Wagenmakers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Dora Matzke, Department of Psychology, University of Amsterdam, Weesperplein 4, 1018 XA, Amsterdam, Netherlands e-mail: firstname.lastname@example.org