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Abstract

The stop-signal paradigm is a popular procedure to investigate responseinhibition–the ability to stop ongoing responses. It consists of a choice responsetime (RT) task that is occasionally interrupted by a stop stimulussignaling participants to withhold their response. Performance in the stopsignalparadigm is often formalized as race between a set of go runners triggeredby the choice stimulus and a stop runner triggered by the stop signal.We investigated whether evidence-accumulation processes, which have beenwidely used in choice RT analysis, can serve as the runners in the stop-signalrace model and support the estimation of psychologically meaningful parameters.We examined two types of the evidence-accumulation architectures:the racing Wald model (Logan, Van Zandt, Verbruggen, & Wagenmakers, 2014) and a novel proposal based on the Lognormal race (Heathcote & Love,2012). Using a series of simulation studies and fits to empirical data, wefound that these models are not measurement models in the sense that thedata-generating parameters cannot be recovered in realistic experimentaldesigns.

Item Type:	Article
Authors/Creators:	Matzke, D and Logan, GD and Heathcote, A
Keywords:	evidence-accumulation models, lognormal distribution, response inhibition, stop-signal paradigm, Wald Distribution
Journal or Publication Title:	Computational Brain & Behavior
Publisher:	Springer
ISSN:	2522-0861
DOI / ID Number:	10.1007/s42113-020-00075-x
Copyright Information:	Copyright 2020 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Related URLs:	Google Scholar: Heathcote, A UTAS Profile: Heathcote, A
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