Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop‐signal task

Abstract

This study investigates the neural correlates underpinning response inhibition using a parametric ex‐Gaussian model of stop‐signal task performance, fit with hierarchical Bayesian methods, in a large healthy sample (N = 156). The parametric model accounted for both stop‐signal reaction time (SSRT) and trigger failure (i.e., failures to initiate the inhibition process). The returned SSRT estimate (SSRTEXG3) was attenuated by ≈65 ms compared to traditional nonparametric SSRT estimates (SSRTint). The amplitude and latency of the N1 and P3 event‐related potential components were derived for both stop‐success and stop‐failure trials and compared to behavioral estimates derived from traditional (SSRTint) and parametric (SSRTEXG3, trigger failure) models. Both the fronto‐central N1 and P3 peaked earlier and were larger for stop‐success than stop‐failure trials. For stop‐failure trials only, N1 peak latency correlated with both SSRT estimates as well as trigger failure and temporally coincided with SSRTEXG3, but not SSRTint. In contrast, P3 peak and onset latency were not associated with any behavioral estimates of inhibition for either trial type. While the N1 peaked earlier for stop‐success than stop‐failure trials, this effect was not found in poor task performers (i.e., high trigger failure/slow SSRT). These findings are consistent with attentional modulation of both the speed and reliability of the inhibition process, but not for poor performers. Together with the absence of any P3 onset latency effect, our findings suggest that attentional mechanisms are important in supporting speeded and reliable inhibition processes required in the stop‐signal task.

Latency and amplitude of the stop-signal P3 event-related potential are related to inhibitory GABAaactivity in primary motor cortex

“…Indeed, EMG and MEP recordings show that the first signs of motor-system inhibition in these measures emerge as early as ∼150ms after the onset of a stop-signal (Jana, Hannah, Muralidharan, & Aron, 2020; Raud & Huster, 2017). In line with this, recent studies have shown that cortical signals that are related to stopping success can also be found in similar time ranges (Huster et al, 2020; Jana et al, 2020; Skippen et al, 2020; Wessel, 2019). Just like the beforementioned EMG and MEP measurements of functional motor inhibition, these cortical signatures often precede both SSRT, as well as the typical onset latency of the fronto-central P3, by several dozens of milliseconds.…”

“…While this latter association has since been independently replicated (Huster, Messel, Thunberg, & Raud, 2020) the association between the stop-signal P3 and inhibitory processing still remains controversial. One argument is that additional ERPs, including ERPs that predate the P3 and likely reflect attentional processes, also correlate with SSRT (Huster et al, 2020; Skippen et al, 2020). In addition to this debate focusing on the electrophysiology of stopping, the fundamental assumptions underlying the SSRT computation have also recently been called into question themselves, with some authors arguing that SSRT systematically overestimates the latency of the stopping process – specifically because the classic SSRT computation does not take into account trials in which the inhibitory control process is not triggered at all (Matzke, Curley, Gong, & Heathcote, 2019; Patrick Skippen et al, 2019).…”

Dissociation of Medial Frontal β-Bursts and Executive Control

“…Second, compared with experiments with humans, monkeys are well trained and well accustomed to the task, typically completing up to 10 times more trials than humans. This may be demonstrated through our observation that monkeys have a much smaller proportion of trigger failures (;5%) compared with what has been previously observed in human (;20%) studies (Skippen et al, 2019(Skippen et al, , 2020. However, although RTs and SSRTs are typically shorter in macaques than humans, the relative relationship between the two is similar between species.…”

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

“…Another potential explanation is that we relied on the stopping speed derived from a button press to measure SSRT. While this is a widely used measure in response inhibition research, it has its drawbacks (e.g., Skippen et al, 2020). Most importantly for our study, the SSRT is only a summary measure for each participant and does not provide single trial information.…”