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A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm


Matzke, D and Love, J and Heathcote, A, A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm, Behavior Research Methods, 49, (1) pp. 267-281. ISSN 1554-3528 (2017) [Refereed Article]




Copyright Statement

The Author(s) 2016. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.3758/s13428-015-0695-8


Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at

Item Details

Item Type:Refereed Article
Keywords:Bayesian Hierarchical Modeling, ex-Gaussian distribution, response inhibition, stop-signal paradigm, stop-signal RT distribution, trigger failures
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Decision making
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:105120
Year Published:2017 (online first 2016)
Web of Science® Times Cited:60
Deposited By:Medicine
Deposited On:2015-12-08
Last Modified:2018-07-11
Downloads:140 View Download Statistics

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