scholarly journals Electrophysiological Evidence for Distinct Proactive Control Mechanisms in a Stop-Signal Task: An Individual Differences Approach

2020 ◽  
Vol 11 ◽  
Author(s):  
Woo-Tek Lee ◽  
Min-Suk Kang
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mario Paci ◽  
Giulio Di Cosmo ◽  
Mauro Gianni Perrucci ◽  
Francesca Ferri ◽  
Marcello Costantini

AbstractInhibitory control is the ability to suppress inappropriate movements and unwanted actions, allowing to regulate impulses and responses. This ability can be measured via the Stop Signal Task, which provides a temporal index of response inhibition, namely the stop signal reaction time (SSRT). At the neural level, Transcranial Magnetic Stimulation (TMS) allows to investigate motor inhibition within the primary motor cortex (M1), such as the cortical silent period (CSP) which is an index of GABAB-mediated intracortical inhibition within M1. Although there is strong evidence that intracortical inhibition varies during action stopping, it is still not clear whether differences in the neurophysiological markers of intracortical inhibition contribute to behavioral differences in actual inhibitory capacities. Hence, here we explored the relationship between intracortical inhibition within M1 and behavioral response inhibition. GABABergic-mediated inhibition in M1 was determined by the duration of CSP, while behavioral inhibition was assessed by the SSRT. We found a significant positive correlation between CSP’s duration and SSRT, namely that individuals with greater levels of GABABergic-mediated inhibition seem to perform overall worse in inhibiting behavioral responses. These results support the assumption that individual differences in intracortical inhibition are mirrored by individual differences in action stopping abilities.


2014 ◽  
Vol 26 (8) ◽  
pp. 1601-1614 ◽  
Author(s):  
Corey N. White ◽  
Eliza Congdon ◽  
Jeanette A. Mumford ◽  
Katherine H. Karlsgodt ◽  
Fred W. Sabb ◽  
...  

The stop-signal task, in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision making, a drift-diffusion model of simple decisions was fitted to stop-signal task data from go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the go stimulus correlated with greater activation in the right frontal pole for both go and stop trials. On stop trials, stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and BG. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.


2012 ◽  
Vol 107 (1) ◽  
pp. 384-392 ◽  
Author(s):  
Ian Greenhouse ◽  
Caitlin L. Oldenkamp ◽  
Adam R. Aron

Much research has focused on how people stop initiated response tendencies when instructed by a signal. Stopping of this kind appears to have global effects on the motor system. For example, by delivering transcranial magnetic stimulation (TMS) over the leg area of the primary motor cortex, it is possible to detect suppression in the leg when the hand is being stopped (Badry R et al. Suppression of human cortico-motoneuronal excitability during the stop-signal task. Clin Neurophysiol 120: 1717–1723, 2009). Here, we asked if such “global suppression” can be observed proactively, i.e., when people anticipate they might have to stop. We used a conditional stop signal task, which allows the measurement of both an “anticipation phase” (i.e., where proactive control is applied) and a “stopping” phase. TMS was delivered during the anticipation phase ( experiment 1) and also during the stopping phase ( experiments 1 and 2) to measure leg excitability. During the anticipation phase, we did not observe leg suppression, but we did during the stopping phase, consistent with Badry et al. (2009) . Moreover, when we split the subject groups into those who slowed down behaviorally (i.e., exercised proactive control) and those who did not, we found that subjects who slowed did not show leg suppression when they stopped, whereas those who did not slow did show leg suppression when they stopped. These results suggest that if subjects prepare to stop, then they do so without global effects on the motor system. Thus, preparation allows them to stop more selectively.


2003 ◽  
Vol 56 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Tim McGarry ◽  
Romeo Chua ◽  
Ian M. Franks

The ability to inhibit an unfolding action is usually investigated using a stop signal (or go—stop) task. The data from the stop-signal task are often described using a horse-race model whose key assumption is that each process (i.e., go, stop) exhibits stochastic independence. Using three variations of a coincident-timing task (i.e., go, go—stop, and go—stop—go) we extend previous considerations of stochastic independence by analysing the go latencies for prior effects of stopping. On random trials in the go—stop—go task the signal sweep was paused for various times at various distances before the target. Significant increases in latency errors were reported on those trials on which the signal was paused (p <.005). Further analyses of the pause trials revealed significant effects for both the stopping interval (p <.001) and the pause interval (p <.05). Tukey post hoc analyses demonstrated increased latency errors as a linear function of the stopping interval, as expected, and decreased latency errors as a nonlinear function of the pause interval. These latter results indicate that the latencies of the go process, as reflected in the latency errors, may not exhibit stochastic independence under certain conditions. Various control mechanisms were considered in an attempt to explain these data.


NeuroImage ◽  
2015 ◽  
Vol 121 ◽  
pp. 115-125 ◽  
Author(s):  
Hanne Schevernels ◽  
Klaas Bombeke ◽  
Liesbet Van der Borght ◽  
Jens-Max Hopf ◽  
Ruth M. Krebs ◽  
...  

2020 ◽  
Author(s):  
Fanny Grisetto ◽  
Pierre Le Denmat ◽  
Yvonne N. Delevoye-Turrell ◽  
Quentin Vantrepotte ◽  
Tanguy Davin ◽  
...  

According to the dual mechanisms of control (DMC), both reactive and proactive control are involved in adjusting behaviors when those are not appropriate to the environment. These control mechanisms have different costs and benefits, orienting the implementation of one or the other control mechanisms as a function of contextual and inter-individual factors. However, to our knowledge, no studies have investigated whether reactive control capacities modulate the use of proactive control. According to the DMC, poor reactive control capacities should be counterbalanced by greater proactive control involvement to efficiently adjust behaviors. We expected that maladaptive behaviors, such as risk-taking, would be characterized by an absence of such compensation. A total of 176 healthy adults performed two reaction time tasks (the Simon and the Stop Signal tasks) and a risk-taking assessment (the Balloon Analog Risk Taking, BART). For each individual, the Stop Signal Reaction Time (SSRT) was used to assess reactive inhibition capacities and the mean duration of the button press in the BART was used as an index of risk-taking propensity. The post-error slowing (PES) in the Simon task reflected the individuals’ tendency to proactively adjust behaviors after an error. Our results showed that smaller SSRT, revealing better reactive inhibition capacities, were associated with shorter PES, suggesting less involvement of proactive adjustments. Moreover, higher the risk-taking propensity, lesser was the proactive control counterbalance for poor reactive inhibition capacities. Risky behaviors, and more broadly maladaptive behaviors, could emerge from the absence of proactive control counterbalance for reactive control deficits


Author(s):  
Wuyi Wang ◽  
Sien Hu ◽  
Jaime S. Ide ◽  
Simon Zhornitsky ◽  
Sheng Zhang ◽  
...  

2016 ◽  
Vol 28 (1) ◽  
pp. 177-186 ◽  
Author(s):  
Ying Cai ◽  
Siyao Li ◽  
Jing Liu ◽  
Dawei Li ◽  
Zifang Feng ◽  
...  

Mounting evidence suggests that response inhibition involves both proactive and reactive inhibitory control, yet its underlying neural mechanisms remain elusive. In particular, the roles of the right inferior frontal gyrus (IFG) and inferior parietal lobe (IPL) in proactive and reactive inhibitory control are still under debate. This study aimed at examining the causal role of the right IFG and IPL in proactive and reactive inhibitory control, using transcranial direct current stimulation (tDCS) and the stop signal task. Twenty-two participants completed three sessions of the stop signal task, under anodal tDCS in the right IFG, the right IPL, or the primary visual cortex (VC; 1.5 mA for 15 min), respectively. The VC stimulation served as the active control condition. The tDCS effect for each condition was calculated as the difference between pre- and post-tDCS performance. Proactive control was indexed by the RT increase for go trials (or preparatory cost), and reactive control by the stop signal RT. Compared to the VC stimulation, anodal stimulation of the right IFG, but not that of the IPL, facilitated both proactive and reactive control. However, the facilitation of reactive control was not mediated by the facilitation of proactive control. Furthermore, tDCS did not affect the intraindividual variability in go RT. These results suggest a causal role of the right IFG, but not the right IPL, in both reactive and proactive inhibitory control.


2018 ◽  
Vol 39 (8) ◽  
pp. 3263-3276 ◽  
Author(s):  
Nicholas D'Alberto ◽  
Bader Chaarani ◽  
Catherine A. Orr ◽  
Philip A. Spechler ◽  
Matthew D. Albaugh ◽  
...  

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