- Title
- A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm
- Creator
- Matzke, Dora; Love, Jonathon; Heathcote, Andrew
- Relation
- Behavior Research Methods Vol. 49, Issue 1, p. 267-281
- Publisher Link
- http://dx.doi.org/10.3758/s13428-015-0695-8
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2017
- Description
- 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 http://dora.erbe-matzke.com/software.html.
- Subject
- Bayesian hierarchical modeling; ex-Gaussian distribution; response inhibition; stop-signal paradigm; stop-signal RT distribution; trigger failure
- Identifier
- http://hdl.handle.net/1959.13/1386387
- Identifier
- uon:32411
- Identifier
- ISSN:1554-351X
- Rights
- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Language
- eng
- Full Text
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