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The error‐related negativity (ERN), a neural response to errors, has been associated with several forms of psychopathology and assumed to represent a neural risk marker for obsessive–compulsive disorder (OCD) and anxiety disorders. Yet, it is still unknown which specific symptoms or traits best explain ERN variation. This study investigated performance‐monitoring in participants (N = 100) recruited across a spectrum of obsessive–compulsive characteristics (n = 26 patients with OCD; n = 74 healthy participants including n = 24 with low, n = 24 with medium, and n = 26 with high OC‐characteristics). Several compulsivity‐ and anxiety‐associated characteristics were assessed and submitted to exploratory principal axis factor analysis. Associations of raw measures and derived factors with ERN and correct‐related negativity (CRN) were examined. Patients with OCD showed increased ERN amplitudes compared to healthy participants. The ERN was associated with a variety of traits related to anxiety and negative affect. Factor analysis results revealed a most prominent association of the ERN with a composite measure of anxiety and neuroticism, whereas the CRN was specifically associated with compulsivity. Results support differential associations for the ERN and CRN and demonstrate that a dimensional recruitment approach and use of composite measures can improve our understanding of characteristics underlying variation in neural performance monitoring.

Monitoring one's own actions to identify errors and conflicts is an executive function providing humans with important information in a complex environment. It allows cognitive, motivational, and behavioral adjustments that improve upcoming behavior by avoiding errors and potential harm

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Enhanced amplitudes of the error-related negativity (ERN) have been suggested to be a transdiagnostic neural risk marker for internalizing psychopathology.Previous studies propose worry to be an underlying mechanism driving the association between enhanced ERN and anxiety. The present preregistered study focused on disentangling possible effects of trait and state worry on the ERN by utilizing a cross sectional observational and a longitudinal randomized controlled experimental design. To this end, we examined the ERN of n = 90 students during a flanker task (T0), which were then randomly assigned to one of three groups (worry induction, worry reduction, passive control group). Following the intervention, participants performed another flanker task (T1) to determine potential alterations of their ERN. Manipulation checks revealed that compared to the control group, state worry increased in the induction but also in the reduction group.ERN amplitudes did not vary as a function of state worry. An association of trait worry with larger ERN amplitudes was only observed in females. Furthermore, we found larger ERN amplitudes in participants with a current or lifetime diagnosis of internalizing disorders. In summary, our findings suggest that the ERN seems to be insensitive to variations in state worry, but that an elevated ERN is associated with the trait-like tendency to worry and internalizing psychopathology, which is consistent with the notion that the ERN likely represents a trait-like neural risk associated with anxiety.

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Despite a plethora of research, associations between individual differences in personality and electroencephalogram (EEG) parameters remain poorly understood due to concerns of low replicability and insufficiently powered data analyses due to relatively small effect sizes. The present article describes how a multi-laboratory team of EEG-personality researchers aims to alleviate this unsatisfactory status quo. In particular, the present article outlines the design and methodology of the project, provides a detailed overview of the resulting large-scale dataset that is available for use by future collaborators, and forms the basis for consistency and depth to the methodology of all resulting empirical articles. Through this article, we aim to inform researchers in the field of Personality Neuroscience of the freely available dataset. Furthermore, we assume that researchers will generally benefit from this detailed example of the implementation of cooperative forking paths analysis.