A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task
- Ghent University, Belgium
- University of California, San Diego, United States
- Leiden University, Netherlands
- Technical University of Dresden, Germany
- Stanford University, United States
- University of Maryland, United States
- Indiana University, United States
- University of Cambridge, United Kingdom
- Cardiff University, United Kingdom
- Oldenburg University, Germany
- University of Western Ontario, Canada
- Monash University, Australia
- University of Toronto, Canada
- University of Vermont, United States
- University of Oregon, United States
- University of Tasmania, Australia
- University of Oslo, Norway
- Royal Netherlands Academy of Arts and Sciences (KNAW), Netherlands
- Utrecht University, Netherlands
- KU Leuven, Belgium
- Vanderbilt University, United States
- University of Amsterdam, Netherlands
- Anglia Ruskin University, United Kingdom
- Indian Institute of Science, India
- Yale University, United States
- Queen's University, Canada
- University of Maryland, College Park, United States
- King's College London, United Kingdom
- Dresden University of Technology, Germany
- Michigan State University, United States
- University of Iowa, United States
- Trinity College Dublin, Ireland
- Donders Institute, Netherlands
- Cited 62
- Views 4,212
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Abstract
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide twelve easy-to-implement consensus recommendations and point out the problems that can arise when these are not followed. Furthermore we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
Article and author information
Author details
Frederick Verbruggen
Russell A Poldrack
Funding
H2020 European Research Council (769595)
- Frederick Verbruggen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- David Badre, Brown University, United States
Publication history
- Received: February 22, 2019
- Accepted: April 9, 2019
- Accepted Manuscript published: April 29, 2019 (version 1)
- Version of Record published: May 23, 2019 (version 2)
Copyright
© 2019, Verbruggen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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It is thought that the brain does not simply react to sensory feedback, but rather uses an internal model of the body to predict the consequences of motor commands before sensory feedback arrives. Time-delayed sensory feedback can then be used to correct for the unexpected—perturbations, motor noise, or a moving target. The cerebellum has been implicated in this predictive control process. Here, we show that the feedback gain in patients with cerebellar ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. This difference is captured by a computational model incorporating a Smith predictor in healthy subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback control. Lastly, we improve cerebellar patients’ movement control by altering (phase advancing) the visual feedback they receive from their own self movement in a simplified virtual reality setup.
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- Developmental Biology
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In the hippocampus, a widely accepted model posits that the dentate gyrus improves learning and memory by enhancing discrimination between inputs. To test this model, we studied conditional knockout mice in which the vast majority of dentate granule cells (DGCs) fail to develop – including nearly all DGCs in the dorsal hippocampus – secondary to eliminating Wntless (Wls) in a subset of cortical progenitors with Gfap-Cre. Other cells in the Wlsfl/-;Gfap-Cre hippocampus were minimally affected, as determined by single nucleus RNA sequencing. CA3 pyramidal cells, the targets of DGC-derived mossy fibers, exhibited normal morphologies with a small reduction in the numbers of synaptic spines. Wlsfl/-;Gfap-Cre mice have a modest performance decrement in several complex spatial tasks, including active place avoidance. They were also modestly impaired in one simpler spatial task, finding a visible platform in the Morris water maze. These experiments support a role for DGCs in enhancing spatial learning and memory.
