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 ≈65ms 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.
Highlights
Response inhibition is a core component of cognitive control that is associated with the cancelation or suppression of an inappropriate behaviour (Bari & Robbins, 2013) and is typically operationalised using the stop-signal task (Logan & Cowan, 1984)
We have previously described a parametric model of the stop-signal task in which the finishing times of the stop and go processes follow an ex-Gaussian distribution, and model parameters are estimated with the Bayesian Estimation of ex-Gaussian Stop-Signal Reaction Time Distribution (BEESTS) procedure (Matzke, Dolan, Logan, Brown, & Wagenmakers, 2013; Matzke, Love, et al, 2013)
Like trigger failure and go failure, together with stop-signal reaction time (SSRT) estimates that consider trial-by-trial variability, allow richer characterisation of inter- and intra-individual variability in stop-signal task behaviour. We found this more detailed approach to characterising the processes involved in response inhibition provided evidence that the early, attention-related N1 is sensitive to individual differences in both the speed and the reliability of the inhibition process
Summary
Response inhibition is a core component of cognitive control that is associated with the cancelation or suppression of an inappropriate behaviour (Bari & Robbins, 2013) and is typically operationalised using the stop-signal task (Logan & Cowan, 1984). In the stop-signal task, participants engage in a speeded choice response task. On a small proportion of randomly selected trials, a stop signal occurs after the choice stimulus and participants must inhibit their prepared response. The prominent horse-race model of the stop-signal task estimates the latency of the ‘stop process’ or stop-signal reaction time (SSRT), which is assumed to fully characterise stop-signal task performance, without the need to account for other processes, such as attention. Increased SSRT in people with attention deficit hyperactivity disorder, schizophrenia, and substance use disorders is thought to indicate less efficient response inhibition (for a review, see Lipszyc & Schachar, 2010), and to result in reduced impulse control in healthy cohorts (Sharma, Markon, & Clark, 2014; Skippen et al, 2019).
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