A Time Series-Based Point Estimation of Stop Signal Reaction Times: More Evidence on the Role of Reactive Inhibition-Proactive Inhibition Interplay on the SSRT Estimations
Abstract:The Stop Signal Reaction Time (SSRT) is a latency measurement for the unobservable human brain stopping process, and was formulated by Logan (1994) without consideration of the nature (go/stop) of trials that precede the stop trials. Two asymptotically equivalent and larger indices of mixture SSRT and weighted SSRT were proposed in 2017 to address this issue from time in task longitudinal perspective, but estimation based on the time series perspective has still been missing in the literature. A time series-ba… Show more
“…Inhibitory control can also be conceptualized as reactive or proactive [ 17 , 20 ]. Proactive inhibition refers to how knowing that there may be an upcoming response that should be cancelled affects performance in the task.…”
The main aim of this study was to investigate the development of selective inhibitory control in middle childhood, a critical period for the maturation of inhibition-related processes. To this end, 64 children aged 6–7 and 56 children aged 10–11 performed a stimulus-selective stop-signal task, which allowed us to estimate not only the efficiency of response inhibition (the stop-signal reaction time or SSRT), but also the strategy adopted by participants to achieve task demands. We found that the adoption of a non-selective (global) strategy characterized by stopping indiscriminately to all stimuli decreased in older children, so that most of them were able to interrupt their ongoing responses selectively at the end of middle childhood. Moreover, compared to younger children, older children were more efficient in their ability to cancel an initiated response (indexed by a shorter SSRT), regardless of which strategy they used. Additionally, we found improvements in other forms of impulsivity, such as the control of premature responding (waiting impulsivity), and attentional-related processes, such as intra-individual variability and distractibility. The present results suggest that middle childhood represents a milestone in the development of crucial aspects of inhibitory control, including selective stopping.
“…Inhibitory control can also be conceptualized as reactive or proactive [ 17 , 20 ]. Proactive inhibition refers to how knowing that there may be an upcoming response that should be cancelled affects performance in the task.…”
The main aim of this study was to investigate the development of selective inhibitory control in middle childhood, a critical period for the maturation of inhibition-related processes. To this end, 64 children aged 6–7 and 56 children aged 10–11 performed a stimulus-selective stop-signal task, which allowed us to estimate not only the efficiency of response inhibition (the stop-signal reaction time or SSRT), but also the strategy adopted by participants to achieve task demands. We found that the adoption of a non-selective (global) strategy characterized by stopping indiscriminately to all stimuli decreased in older children, so that most of them were able to interrupt their ongoing responses selectively at the end of middle childhood. Moreover, compared to younger children, older children were more efficient in their ability to cancel an initiated response (indexed by a shorter SSRT), regardless of which strategy they used. Additionally, we found improvements in other forms of impulsivity, such as the control of premature responding (waiting impulsivity), and attentional-related processes, such as intra-individual variability and distractibility. The present results suggest that middle childhood represents a milestone in the development of crucial aspects of inhibitory control, including selective stopping.
“…There are only two studies in the SST literature that partially answered this question when SSRT is considered as constant index [14,15]. Here, it was shown when considering SST data in a longitudinal context SSRT c…”
Section: Constant Indexmentioning
confidence: 93%
“…Within each context, these methods mentioned below refined the earlier proposed methods given their associated contexts (see Figure 2). Referring to Figure 2 (path 1-1,1-2), there are four estimation methods of SSRT as a constant index: the mean crude method, the Logan 1994 integration method [3], the time series based state-space method [14] (path 1-1), and the weighted method and its mixture equivalent [15] (path 1-2) . Given a subject with go reaction time (GORT) random variable in the go trials with quantile function Q GORT , n stop signal delays T d , and the probability of successful inhibition(SI) denoted by P(SI|T d ).…”
Section: Estimation Methods: Context and Componentsmentioning
The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) has been modelled with two general methods: a nonparametric method by Hans Colonius (1990) and a Bayesian parametric method by Dora Matzke, Gordon Logan and colleagues (2013). These methods assume an equal impact of the preceding trial type (go/stop) in the SST trials on the SSRT distributional estimation without addressing the relaxed assumption. This study presents the required model by considering a two-state mixture model for the SSRT distribution. It then compares the Bayesian parametric single SSRT and mixture SSRT distributions in the usual stochastic order at the individual and the population level under ex-Gaussian (ExG) distributional format. It shows that compared to a single SSRT distribution, the mixture SSRT distribution is more varied, more positively skewed, more leptokurtic and larger in stochastic order. The size of the results’ disparities also depends on the choice of weights in the mixture SSRT distribution. This study confirms that mixture SSRT indices as a constant or distribution are significantly larger than their single SSRT counterparts in the related order. This result offers a vital improvement in the SSRT estimations.
“…The relationship between age-related changes in hippocampal volume and activities remains to be investigated. Fourth, besides the sequential effect, proactive inhibition can also be quantified as a frequentist measure [ 87 ]. As shown in our earlier work, this and the Bayesian measure were highly correlated [ 88 ] and demonstrated similar age-related changes ( Figure A2 ).…”
Section: Limitations Other Considerations and Conclusionmentioning
confidence: 99%
“…Hippocampus Overall model −3.6526 0.0004 −16.594 4 145 0.0090 0.0574 SEQ −1.5427 0.1251 −16.534 Age 3.0107 0.0031 0.3475 Sex −0.4914 0.6239 −1.3584 Figure A1 Frequency distribution of age and its break-down by sex. Figure A2 Correlation between ( a ) age and proactive control represented by the sequential effect quantified as the correlation between the estimated probability of stop signal and go reaction time (goRT) (a reproduction of Figure 1 a in Hu et al [ 45 ]); ( b ) age and proactive control represented by the delta goRT quantified as the difference between goRT of go trials following stop trials and those of go trials following go trials described in Soltanifar et al [ 87 ]; and ( c ) the two proactive control indices. …”
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.
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