Impulsivity is regarded as a multifaceted construct that comprises two dimensions: rapid-response impulsivity and reward-delay impulsivity. It is unclear, however, which aspects of trait impulsivity, as assessed by self-report measures are related to rapid-response impulsivity and/or to reward-delay impulsivity, as different results have been reported in studies using both self-report and cognitive measures. This study aimed to directly relate self-report measures of impulsivity to cognitive measures of impulsivity in individuals at low- or high-levels on two impulsivity dimensions, specifically rapid-response impulsivity and reward-delay impulsivity. Participants were classified into high- or low-impulsivity groups based on (1) level of rapid-response impulsivity (determined by BIS-11 Motor subscale scores); (2) level of reward-delay impulsivity (determined by BIS/BAS subscale scores); and (3) a combination of rapid-response impulsivity and reward-delay impulsivity levels. Impulsivity was assessed using Go/No-Go, Stop-Signal and Delay-Discounting tasks and self-report measures. The high rapid-response impulsivity group showed significantly higher reward-delay impulsivity on both, the Delay-Discounting tasks and on self-report measures assessing reward-delay impulsivity, than the low-risk group. Based on the level of reward-delay impulsivity, the high reward-delay impulsivity group scored significantly higher on task-based (cognitive) and self-report measures assessing rapid-response inhibition than the low reward-delay impulsivity group. Combining both dimensions of impulsivity showed that the high-impulsivity group performed significantly worse in rapid-response paradigms and temporally discounted significantly more impulsively than the low-impulsivity group. Thus, combined impulsivity factors provide better assessment of impulsivity than each dimension alone. In conclusion, robust differences in impulsivity can be identified in non-clinical young adults.

Despite extensive coverage of a relationship between memory performance and executive function in the obsessive-compulsive disorder (OCD) literature, the relative contributions of specific aspects of executive control have remained elusive. We, therefore, extend our previous multilevel meta-analysis (Persson et al., 2021), where demand on executive function was the most significant determinant of memory deficits in OCD, and provide a finer-grained analysis of executive control via a segregation into top-down (attentional control, maintenance and updating, planning) and bottom-up (perceptual integration, perceptual salience) contributions. Our multilevel meta-analytic approach allowed us to accommodate the interdependency of 255 effect sizes from 131 studies, totaling 4,101 OCD patients. Results revealed that maintenance and updating (top-down) and perceptual integration (bottom-up) predicted memory performance generally, and specifically in those with clinical OCD. Exploratory analyses suggested that this effect may be somewhat different among subclinical OCD groups; however, these findings should be considered with conceptual and analytical caveats in mind. We explain these results via deficient sensory (perceptual integration) and working memory (maintenance and updating) gating mechanisms and propose a model to accommodate their expression in OC symptoms. In conclusion, our meta-analysis has expanded understanding of cognitive performance in OCD and identifies the possibility of untapped cognitive targets for intervention.

Inhibition, the ability to withhold a response or to stop an initiated response, is a necessary cognitive function that can be vulnerable to an impairment. High levels of impulsivity have been shown to impact response inhibition and/or cognitive task performance. The present study investigated the spectral and spatio-temporal dynamics of response inhibition, during a combined go/no-go/stop-signal task, using magnetoencephalography (MEG) in a healthy undergraduate student population. Participants were divided by their level of impulsivity, as assessed by self-report measures, to explore potential differences between high (n=17) and low (n=17) impulsivity groups. Results showed that individuals scoring high on impulsivity failed significantly more NOGO and STOP trials than those scoring low, but no significant differences were found between stop-signal reaction times. During NOGO and STOP conditions, high impulsivity individuals showed significantly smaller M1 components in posterior regions, which could suggest an attentional processing deficit. During NOGO trials, the M2 component was found to be reduced in individuals scoring high, possibly reflecting less pre-motor inhibition efficiency, whereas in STOP trials, the network involved in the stopping process was engaged later in high impulsivity individuals. The high impulsivity group also engaged frontal networks more during the STOP-M3 component only, possibly as a late compensatory process. The lack of response time differences on STOP trials could indicate that compensation was effective to some degree (at the expense of higher error rates). Decreased frontal delta and theta band power was observed in high impulsivity individuals, suggesting a possible deficit in frontal pathways involved in motor suppression, however, unexpectedly, increased delta and theta band power in central and posterior sensors was also observed, which could be indicative of an increased effort to compensate for frontal deficits. Individuals scoring highly also showed decreased alpha power in frontal sensors, suggesting decreased inhibitory processing, along with reduced alpha suppression in posterior regions, reflecting reduced cue processing. These results provide evidence for how personality traits, such as impulsivity, relate to differences in the neural correlates of response inhibition.