No abstract

Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis.

Inhibitory control, i.e., the ability to stop or suppress actions, thoughts, or memories, represents a prevalent and popular concept in basic and clinical neuroscience as well as psychology. At the same time, it is notoriously difficult to study as successful inhibition is characterized by the absence of a continuously quantifiable direct behavioral marker. It has been suggested that the P3 latency, and here especially its onset latency, may serve as neurophysiological marker of inhibitory control as it correlates with the stop signal reaction time (SSRT). The SSRT estimates the average stopping latency, which itself is unobservable since no overt response is elicited in successful stop trials, based on differences in the distribution of go reaction times and the delay of the stop-relative to the go-signal in stop trials.In a meta-analysis and an independent EEG experiment, we found that correlations between the P3-latency and the SSRT are indeed replicable, but also unspecific. Not only does the SSRT also correlate with the N2-latency, but both P3and N2-latency measures show similar or even higher correlations with other behavioral parameters such as the go reaction time or stopping accuracy. The missing specificity of P3-SSRT correlations, together with the general pattern of associations, suggests that these manifest effects are driven by underlying latent processes other than inhibition, such as those associated with the speed-accuracy trade-off. P3 and inhibitionHuster et al.3

The ability to cancel an already initiated response is central to flexible behavior. While several different behavioral and neural markers have been suggested to quantify the latency of the stopping process, it remains unclear if they quantify the stopping process itself, or other supporting mechanisms such as visual and/or attentional processing. the present study sought to investigate the contributions of inhibitory and sensory processes to stopping latency markers by combining transcranial direct current stimulation (tDcS), electroencephalography (eeG) and electromyography (eMG) recordings in a withinparticipant design. Active and sham tDcS were applied over the inferior frontal gyri (ifG) and visual cortices (VC), combined with both online and offline EEG and EMG recordings. We found evidence that neither of the active tDCS condition affected stopping latencies relative to sham stimulation. Our results challenge previous findings suggesting that anodal tDCS over the IFG can reduce stopping latency and demonstrates the necessity of adequate control conditions in tDcS research. Additionally, while the different putative markers of stopping latency showed generally positive correlations with each other, they also showed substantial variation in the estimated latency of inhibition, making it unlikely that they all capture the same construct exclusively.

A few studies have examined neuropsychological functions, sleep, and mental health combined in Klinefelter syndrome (KS; 47,XXY). We investigated neuropsychological functions with standard tests, sleep with actigraphy, and self‐reported mental health in 30 men with KS (Mean age = 36.7 years) compared to 21 controls (Mean age = 36.8 years). Men with KS scored significantly lower on mental speed, attention span, working memory, inhibition, and set‐shifting tests, as well as overall IQ (mean effect size difference Cohen's d = 0.79). Men with KS had significantly longer night wakes, with no differences in other sleep variables (mean d = 0.34). Men with KS reported poorer mental health than controls (mean d = 1.16). Regression analyses showed neuropsychological functions explained variance in some sleep domains for men with KS but not for controls. Neuropsychological functions explained variance in some mental health domains for controls. For men with KS, however, verbal IQ was the only significant predictor of mental health. Altogether, men with KS display problems in neuropsychological functions and mental health but do not appear different from controls on most sleep parameters. Our findings indicate that relations between neuropsychological functions, sleep, and mental health differ between men with KS and controls.