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The ability to cancel a motor response is critical for optimal functioning in various facets of daily life. Hence, efficient inhibitory motor control is a key function throughout the lifespan. Considering the fact that inhibitory motor function gradually declines with advancing age, it is not surprising that the study of motor inhibition in this age group is gaining considerable interest. In general, we can distinguish between two prominent types of motor inhibition, namely proactive and reactive inhibition. Whereas the anticipation for upcoming stops (proactive inhibition) appears readily preserved at older age, the ability to stop an already planned or initiated action (reactive inhibition) generally declines with advancing age. The differential impact of aging on proactive and reactive inhibition at the behavioral level prompts questions about the neural architecture underlying both types of inhibitory motor control. Here we will not only highlight the underlying structural brain properties of proactive and reactive inhibitory control but we will also discuss recent developments in brain-behavioral approaches, namely the registration of neurochemical compounds using magnetic resonance spectroscopy. This technique allows for the direct detection of the primary inhibitory neurotransmitter in the brain, i.e., γ-aminobutyric acid, across the broader cortical/subcortical territory, thereby opening new perspectives for better understanding the neural mechanisms mediating efficient inhibitory control in the context of healthy aging. Ultimately, these insights may contribute to the development of interventions specifically designed to counteract age-related declines in motor inhibition.

Stopping outright (reactive inhibition) and slowing down (proactive inhibition) are types of response inhibition which have mainly been investigated in the manual effector system. This study compared reactive inhibition across manual and vocal effector systems, examined the effects of excitatory anodal transcranial direct current stimulation (anodal tDCS) over the right prefrontal cortex (right-PFC) and looked at the relationship between reactive and proactive inhibition. We hypothesised (1) that vocal reactive inhibition would be less effective than manual reactive inhibition as evidenced by longer stop signal reaction times; (2) that anodal tDCS would enhance both vocal and manual reactive inhibitions and (3) that proactive and reactive inhibitions would be positively related. We tested 14 participants over two sessions (one session with anodal tDCS and one session with sham stimulation) and applied stimulation protocol in the middle of the session, i.e. only during the second of three phases. We used a stop signal task across two stop conditions: relevant and irrelevant stop conditions in which stopping was required or ignored, respectively. We found that reactive inhibition was faster during and immediately after anodal tDCS relative to sham. We also found that greater level of proactive inhibition enhanced reactive inhibition (indexed by shorter stop signal reaction times). These results support the hypothesis that the right-PFC is part of a core network for reactive inhibition and supports previous contention that proactive inhibition is possibly modulated via preactivating the reactive inhibition network.

Two hypotheses have been advanced for when motor sequence learning occurs: offline between bouts of practice or online concurrently with practice. A third possibility is that learning occurs both online and offline. A complication for differentiating between those hypotheses is a process known as reactive inhibition, whereby performance worsens over consecutively executed sequences, but dissipates during breaks. We advance a new quantitative modeling framework that incorporates reactive inhibition and in which the three learning accounts can be implemented. Our results show that reactive inhibition plays a far larger role in performance than is appreciated in the literature. Across four groups of participants in which break times and correct sequences per trial were varied, the best overall fits were provided by a hybrid model. The version of the offline model that does not account for reactive inhibition, which is widely assumed in the literature, had the worst fits. We discuss implications for extant hypotheses and directions for future research.

The aim of this selective review paper is to clarify potential confusion when referring to the term proactive inhibitory control. Illustrated by a concise overview of the literature, we propose defining reactive inhibition as the mechanism underlying stopping an action. On a stop trial, the stop signal initiates the stopping process that races against the ongoing action-related process that is triggered by the go signal. Whichever processes finishes first determines the behavioral outcome of the race. That is, stopping is either successful or unsuccessful in that trial. Conversely, we propose using the term proactive inhibition to explicitly indicate preparatory processes engaged to bias the outcome of the race between stopping and going. More specifically, these proactive processes include either pre-amping the reactive inhibition system (biasing the efficiency of the stopping process) or presetting the action system (biasing the efficiency of the go process). We believe that this distinction helps meaningful comparisons between various outcome measures of proactive inhibitory control that are reported in the literature and extends to experimental research paradigms other than the stop task.

Attenuated inhibitory control is one of the most robust findings in the neuropsychology of attention-deficit/hyperactivity disorder (ADHD). However, it is unclear whether this represents a deficit in outright stopping (reactive inhibition), whether it relates to a deficit in anticipatory response slowing (proactive inhibition), or both. In addition, children with other development disorders, such as autism spectrum disorder (ASD), often have symptoms of inattention, impulsivity, and hyperactivity similar to children with ADHD. These may relate to similar underlying changes in inhibitory processing. In this study, we used a modified stop-signal task to dissociate reactive and proactive inhibition. We included not only children with ADHD, but also children primarily diagnosed with an ASD and high parent-rated levels of ADHD symptoms. We replicated the well-documented finding of attenuated reactive inhibition in children with ADHD. In addition, we found a similar deficit in children with ASD and a similar level of ADHD symptoms. In contrast, we found no evidence for deficits in proactive inhibition in either clinical group. These findings re-emphasize the role of reactive inhibition in children with ADHD and ADHD symptoms. Moreover, our findings stress the importance of a trans-diagnostic approach to the relationship between behavior and neuropsychology.

Cognitive control is a critical feature in adapting our behavior to environmental and internal demands with two types of inhibition having been identified, namely the proactive and the reactive. Aiming to shed light on their respective neural correlates, we decided to focus on the cerebral activity before or after presentation of the target demanding a subject’s stop as a way to separate the proactive from the reactive components associated with the tasks. Accordingly, we performed three Activation Likelihood Estimation (ALE) meta-analyses of fMRI studies exploring proactive and reactive inhibitory phases of cognitive control. For this purpose, we searched for fMRI studies investigating brain activity preceding or following target stimuli. Eight studies (291 subjects, 101 foci) were identified for the proactive analysis. Five of these studies and those previously analyzed by others (348 subjects, 199 foci) were meta-analyzed to explore the neural correlates of reactive inhibition. Overall, our results showed different networks for the two inhibitory components. Notably, we observed a contiguity between areas in the right inferior frontal gyrus pertaining to proactive inhibition and in the right middle frontal gyrus regarding reactive inhibition. These neural correlates allow proposal of a new comprehensive model of cognitive control.

Response inhibition involves proactive and reactive modes. Proactive inhibition is goal-directed, triggered by warning cues, and serves to restrain actions. Reactive inhibition is stimulus-driven, triggered by salient stop-signals, and used to stop actions completely. Functional MRI studies have identified brain regions that activate during proactive and reactive inhibition. It remains unclear how these brain regions operate in functional networks, and whether proactive and reactive inhibition depend on common networks, unique networks, or a combination. To address this we analyzed a large fMRI dataset (N=65) of a stop-signal task designed to measure proactive and reactive inhibition, using independent component analysis (ICA). We found 1) three frontal networks that were associated with both proactive and reactive inhibition, 2) one network in the superior parietal lobe, which also included dorsal premotor cortex and left putamen, that was specifically associated with proactive inhibition, and 3) two right-lateralized frontal and fronto-parietal networks, including the right inferior frontal gyrus and temporoparietal junction as well as a bilateral fronto-temporal network that were uniquely associated with reactive inhibition. Overlap between networks was observed in dorsolateral prefrontal and parietal cortices. Taken together, we offer a new perspective on the neural underpinnings of inhibitory control, by showing that proactive inhibition and reactive inhibition are supported by a group of common and unique networks that appear to integrate and interact in frontoparietal areas. [Display omitted] •Proactive and reactive inhibitory controls are supported by multiple networks.•We found proactive (P), reactive (R), and proactive/reactive (PR) networks.•P and P/R networks are bilaterally organized; R network is right-dominant.•These networks may integrate and interact in dorsolateral frontoparietal areas.

According to the dual mechanisms of control (DMC), reactive and proactive control are involved in adjusting behaviors when maladapted to the environment. However, both contextual and inter-individual factors increase the weight of one control mechanism over the other, by influencing their cognitive costs. According to one of the DMC postulates, limited reactive control capacities should be counterbalanced by greater proactive control to ensure control efficiency. Moreover, as the flexible weighting between reactive and proactive control is key for adaptive behaviors, we expected that maladaptive behaviors, such as risk-taking, would be characterized by an absence of such counterbalance. However, to our knowledge, no studies have yet investigated this postulate. In the current study, we analyzed the performances of 176 participants on two reaction time tasks (Simon and Stop Signal tasks) and a risk-taking assessment (Balloon Analog Risk Taking, BART). The post-error slowing in the Simon task was used to reflect the spontaneous individuals’ tendency to proactively adjust behaviors after an error. The Stop Signal Reaction Time was used to assess reactive inhibition capacities and the duration of the button press in the BART was used as an index of risk-taking propensity. Results showed that poorer reactive inhibition capacities predicted greater proactive adjustments after an error. Furthermore, the higher the risk-taking propensity, the less reactive inhibition capacities predicted proactive behavioral adjustments. The reported results suggest that higher risk-taking is associated with a smaller weighting of proactive control in response to limited reactive inhibition capacities. These findings highlight the importance of considering the imbalanced weighting of reactive and proactive control in the analysis of risk-taking, and in a broader sense, maladaptive behaviors.

Proactive and reactive inhibitory processes are a fundamental part of executive functions, allowing a person to stop inappropriate responses when necessary and to adjust performance in in a long term in accordance to the goals of a task. In the current study, we manipulate, in a single task, both reactive and proactive inhibition mechanisms, and we investigate the within-subjects effect of increasing, by means of anodal transcranial direct current stimulation (tDCS), the involvement of the right inferior frontal cortex (rIFC). Our results show a simultaneous enhancement of these two cognitive mechanisms when modulating the neural activity of rIFC. Thus, the application of anodal tDCS increased reaction times on Go trials, indicating a possible increase in proactive inhibition. Concurrently, the stop-signal reaction time, as a covert index of the inhibitory process, was reduced, demonstrating an improvement in reactive inhibition. In summary, the current pattern of results validates the engagement of the rIFC in these two forms of inhibitory processes, proactive and reactive inhibition and it provides evidence that both processes can operate concurrently in the brain.

Introduction Video game addiction (VGA) is associated with physical and mental disorders, one of which is problem in executive function, particularly inhibitory control. The present study aimed to investigate reactive and proactive inhibitory controls by event‐related potential (ERP). Methods Thirty video game (action video games)‐addicted subjects and 30 matched healthy controls participated in the study, who were tested by the selective stop‐signal task. Results The main results revealed that the VGA group had significantly more problems in preparatory processes and proactive stop trials, showing that VGA has a negative effect on proactive inhibition. Conclusion Finding the problem in proactive inhibitory control might be helpful in developing new treatments and rehabilitation methods in these fields.