Specific proactive and generic reactive inhibition

The effect of vagus nerve stimulation on response inhibition

In the current study, we explored whether vagus nerve stimulation (VNS) in patients with epilepsy, which is believed to increase norepinephrine (NE) levels via activation of the locus coeruleus, would positively affect response inhibition. Moreover, we tried to identify the dynamics of the underlying neural processes by investigating event-related potentials (ERPs) and pupil size. Patients performed a stop-signal task once when stimulation was switched on and once when it was switched off. We found a correlational pattern suggesting that patients who clinically benefit more from VNS treatment also show a larger behavioral advantage, in terms of faster response inhibition, when the vagus nerve is being stimulated. Event-related potential (ERP) results suggested more pronounced reactive inhibition when stimulation was switched on, independent of the individual amount of seizure reduction. Transient go-locked pupil size was increased from go trials to successful stop trials to unsuccessful stop trials but without displaying a clear VNS effect, which however, might relate to limited sensitivity. We conclude that VNS likely has a positive effect on response inhibition, at least in patients with epilepsy that benefit clinically from the treatment, presumably relating to enhancements of response-inhibition mechanisms and, therefore, identify enhanced response inhibition as a possible cognitive benefit of VNS.

Motivational context for response inhibition influences proactive involvement of attention

Motoric inhibition is ingrained in human cognition and implicated in pervasive neurological diseases and disorders. The present electroencephalographic (EEG) study investigated proactive motivational adjustments in attention during response inhibition. We compared go-trial data from a stop-signal task, in which infrequently presented stop-signals required response cancellation without extrinsic incentives (“standard-stop”), to data where a monetary reward was posted on some stop-signals (“rewarded-stop”). A novel EEG analysis was used to directly model the covariation between response time and the attention-related N1 component. A positive relationship between response time and N1 amplitudes was found in the standard-stop context, but not in the rewarded-stop context. Simultaneously, average go-trial N1 amplitudes were larger in the rewarded-stop context. This suggests that down-regulation of go-signal-directed attention is dynamically adjusted in the standard-stop trials, but is overridden by a more generalized increase in attention in reward-motivated trials. Further, a diffusion process model indicated that behavior between contexts was the result of partially opposing evidence accumulation processes. Together these analyses suggest that response inhibition relies on dynamic and flexible proactive adjustments of low-level processes and that contextual changes can alter their interplay. This could prove to have ramifications for clinical disorders involving deficient response inhibition and impulsivity.

Neural and behavioral mechanisms of proactive and reactive inhibition

Response inhibition is an important component of adaptive behavior. Substantial prior research has focused on reactive inhibition, which refers to the cessation of a motor response that is already in progress. More recently, a growing number of studies have begun to examine mechanisms underlying proactive inhibition, whereby preparatory processes result in a response being withheld before it is initiated. It has become apparent that proactive inhibition is an essential component of the overall ability to regulate behavior and has implications for the success of reactive inhibition. Moreover, successful inhibition relies on learning the meaning of specific environmental cues that signal when a behavioral response should be withheld. Proactive inhibitory control is mediated by stopping goals, which reflect the desired outcome of inhibition and include information about how and when inhibition should be implemented. However, little is known about the circuits and cellular processes that encode and represent features in the environment that indicate the necessity for proactive inhibition or how these representations are implemented in response inhibition. In this article, we will review the brain circuits and systems involved in implementing inhibitory control through both reactive and proactive mechanisms. We also comment on possible cellular mechanisms that may contribute to inhibitory control processes, noting that substantial further research is necessary in this regard. Furthermore, we will outline a number of ways in which the temporal dynamics underlying the generation of the proactive inhibitory signal may be particularly important for parsing out the neurobiological correlates that contribute to the learning processes underlying various aspects of inhibitory control.

Strategic down-regulation of attentional resources as a mechanism of proactive response inhibition

Efficiently avoiding inappropriate actions in a changing environment is central to cognitive control. One mechanism contributing to this ability is the deliberate slowing down of responses in contexts where full response cancellation might occasionally be required, referred to as proactive response inhibition. The present electroencephalographic (EEG) study investigated the role of attentional processes in proactive response inhibition in humans. To this end, we compared data from a standard stop-signal task, in which stop signals required response cancellation ('stop-relevant'), to data where possible stop signals were task-irrelevant ('stop-irrelevant'). Behavioral data clearly indicated the presence of proactive slowing in the standard stop-signal task. A novel single-trial analysis was used to directly model the relationship between response time and the EEG data of the go-trials in both contexts within a multilevel linear models framework. We found a relationship between response time and amplitude of the attention-related N1 component in stop-relevant blocks, a characteristic that was fully absent in stop-irrelevant blocks. Specifically, N1 amplitudes were lower the slower the response time, suggesting that attentional resources were being strategically down-regulated to control response speed. Drift diffusion modeling of the behavioral data indicated that multiple parameters differed across the two contexts, likely suggesting the contribution from independent brain mechanisms to proactive slowing. Hence, the attentional mechanism of proactive response control we report here might coexist with known mechanisms that are more directly tied to motoric response inhibition. As such, our study opens up new research avenues also concerning clinical conditions that feature deficits in proactive response inhibition.