A Bayesian Parametric Approach for the Estimation of Stop-Signal Reaction Time Distributions

2011 ◽  
Author(s):  
Dora Matzke ◽  
Conor V. Dolan ◽  
Gordon D. Logan ◽  
Scott D. Brown ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reaction Time ◽  
Stop Signal ◽  
Parametric Approach ◽  
Stop Signal Reaction Time

scholarly journals Is motor inhibition involved in the processing of sentential negation? An assessment via the Stop-Signal Task

2021 ◽  
Author(s):  
Martina Montalti ◽  
Marta Calbi ◽  
Valentina Cuccio ◽  
Maria Alessandra Umiltà ◽  
Vittorio Gallese
Keyword(s):  
Reaction Time ◽  
Language Processing ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Motor Inhibition ◽  
Good Tool ◽  
Scientific Debate ◽  
Sentential Negation ◽  
Stop Signal Reaction Time ◽  
Methodological Considerations

AbstractIn the last decades, the embodied approach to cognition and language gained momentum in the scientific debate, leading to evidence in different aspects of language processing. However, while the bodily grounding of concrete concepts seems to be relatively not controversial, abstract aspects, like the negation logical operator, are still today one of the main challenges for this research paradigm. In this framework, the present study has a twofold aim: (1) to assess whether mechanisms for motor inhibition underpin the processing of sentential negation, thus, providing evidence for a bodily grounding of this logic operator, (2) to determine whether the Stop-Signal Task, which has been used to investigate motor inhibition, could represent a good tool to explore this issue. Twenty-three participants were recruited in this experiment. Ten hand-action-related sentences, both in affirmative and negative polarity, were presented on a screen. Participants were instructed to respond as quickly and accurately as possible to the direction of the Go Stimulus (an arrow) and to withhold their response when they heard a sound following the arrow. This paradigm allows estimating the Stop Signal Reaction Time (SSRT), a covert reaction time underlying the inhibitory process. Our results show that the SSRT measured after reading negative sentences are longer than after reading affirmative ones, highlighting the recruitment of inhibitory mechanisms while processing negative sentences. Furthermore, our methodological considerations suggest that the Stop-Signal Task is a good paradigm to assess motor inhibition’s role in the processing of sentence negation.

scholarly journals Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

2019 ◽  
Vol 7 (4) ◽  
pp. 856-872 ◽  
Author(s):  
Alexander Weigard ◽  
Andrew Heathcote ◽  
Dóra Matzke ◽  
Cynthia Huang-Pollock
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Reaction Time ◽  
Attention Deficit ◽  
Cognitive Modeling ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Unbiased Estimation ◽  
Children With Adhd ◽  
Hyperactivity Disorder ◽  
Stop Signal Reaction Time

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on attention deficit/hyperactivity disorder (ADHD). However, this measurement model is limited by two factors that may bias SSRT estimation in this population: (a) excessive skew in “go” RT distributions and (b) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the stop signal. We used a Bayesian parametric approach that allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both go RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that performance on the stop-signal task among children with ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.

scholarly journals Age-Related Structural and Functional Changes of the Hippocampus and the Relationship with Inhibitory Control

2020 ◽  
Vol 10 (12) ◽  
pp. 1013
Author(s):  
Sien Hu ◽  
Chiang-shan R. Li
Keyword(s):  
Reaction Time ◽  
Cognitive Aging ◽  
Gray Matter Volume ◽  
Stop Signal ◽  
Sequential Effect ◽  
Stop Signal Task ◽  
Functional Changes ◽  
Age Related ◽  
Stop Signal Reaction Time ◽  
Age Related Changes

Aging is associated with structural and functional changes in the hippocampus, and hippocampal dysfunction represents a risk marker of Alzheimer’s disease. Previously, we demonstrated age-related changes in reactive and proactive control in the stop signal task, each quantified by the stop signal reaction time (SSRT) and sequential effect computed as the correlation between the estimated stop signal probability and go trial reaction time. Age was positively correlated with the SSRT, but not with the sequential effect. Here, we explored hippocampal gray matter volume (GMV) and activation to response inhibition and to p(Stop) in healthy adults 18 to 72 years of age. The results showed age-related reduction of right anterior hippocampal activation during stop success vs. go trials, and the hippocampal activities correlated negatively with the SSRT. In contrast, the right posterior hippocampus showed higher age-related responses to p(Stop), but the activities did not correlate with the sequential effect. Further, we observed diminished GMVs of the anterior and posterior hippocampus. However, the GMVs were not related to behavioral performance or regional activities. Together, these findings suggest that hippocampal GMVs and regional activities represent distinct neural markers of cognitive aging, and distinguish the roles of the anterior and posterior hippocampus in age-related changes in cognitive control.

scholarly journals Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time

2018 ◽  
Vol 18 ◽  
pp. 793-801 ◽  
Author(s):  
Yihe Zhang ◽  
Sheng Zhang ◽  
Jaime S. Ide ◽  
Sien Hu ◽  
Simon Zhornitsky ◽  
...  
Keyword(s):  
Reaction Time ◽  
Dynamic Network ◽  
Cocaine Dependence ◽  
Stop Signal ◽  
Dependence Graph ◽  
Stop Signal Reaction Time

scholarly journals Stop-signal reaction time correlates with a compensatory balance response

2019 ◽  
Vol 71 ◽  
pp. 273-278 ◽  
Author(s):  
G. Rydalch ◽  
H.B. Bell ◽  
K.L. Ruddy ◽  
D.A.E. Bolton
Keyword(s):  
Reaction Time ◽  
Stop Signal ◽  
Stop Signal Reaction Time

scholarly journals Stop Signal Reaction Time measured with a portable device validates optimum STN-DBS programming

2020 ◽  
Vol 13 (6) ◽  
pp. 1609-1611
Author(s):  
Akash Roy ◽  
Supriyo Choudhury ◽  
Purba Basu ◽  
Mark R. Baker ◽  
Stuart N. Baker ◽  
...  
Keyword(s):  
Reaction Time ◽  
Stop Signal ◽  
Portable Device ◽  
Stop Signal Reaction Time ◽  
Stn Dbs

scholarly journals Stop-Signal Reaction-Time Task Performance: Role of Prefrontal Cortex and Subthalamic Nucleus

2007 ◽  
Vol 18 (1) ◽  
pp. 178-188 ◽  
Author(s):  
D. M. Eagle ◽  
C. Baunez ◽  
D. M. Hutcheson ◽  
O. Lehmann ◽  
A. P. Shah ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Reaction Time ◽  
Task Performance ◽  
Subthalamic Nucleus ◽  
Reaction Time Task ◽  
Stop Signal ◽  
Time Task ◽  
Stop Signal Reaction Time

scholarly journals Bayesian parametric estimation of stop-signal reaction time distributions.

2013 ◽  
Vol 142 (4) ◽  
pp. 1047-1073 ◽  
Author(s):  
Dora Matzke ◽  
Conor V. Dolan ◽  
Gordon D. Logan ◽  
Scott D. Brown ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reaction Time ◽  
Stop Signal ◽  
Parametric Estimation ◽  
Stop Signal Reaction Time

scholarly journals A stop-signal task for sheep: introduction and validation of a direct measure for the stop-signal reaction time

2017 ◽  
Vol 20 (4) ◽  
pp. 615-626 ◽  
Author(s):  
Franziska Knolle ◽  
Sebastian D. McBride ◽  
James E. Stewart ◽  
Rita P. Goncalves ◽  
A. Jennifer Morton
Keyword(s):  
Reaction Time ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Direct Measure ◽  
Stop Signal Reaction Time
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