Linking neurophysiological processes of action monitoring to post-response speed-accuracy adjustments in a neuro-cognitive diffusion model.

The cognitive system needs to continuously monitor actions and initiate adaptive measures aimed at increasing task performance and avoiding future errors. To investigate the link between the contributing cognitive processes, we introduce the neuro-cognitive diffusion model, a statistical approach that allows a combination of computational modelling of behavioural and electrophysiological data on a single-trial level. This unique combination of methods allowed us to demonstrate across three experimental datasets that early response monitoring (error negativity; Ne/c) was related to more response caution and increased attention on task-relevant features on the subsequent trial, thereby preventing future errors, whereas later response monitoring (error positivity, Pe/c) maintained the ability of responding fast under speed pressure. Our results suggest that Pe/c-related processes might keep Ne/c-related processes in check regarding their impact on post-response adaptation to reconcile the conflicting criteria of fast and accurate responding.

The influence of error detection and error significance on neural and behavioral correlates of error processing in a complex choice task.

Error detection and error significance form essential mechanisms that influence error processing and action adaptation. Error detection often is assessed by an immediate self-evaluation of accuracy. Our study used cognitive neuroscience methods to elucidate whether self-evaluation itself influences error processing by increasing error significance in the context of a complex response selection process. In a novel eight-alternative response task, our participants responded to eight symbol stimuli with eight different response keys and a specific stimulus-response assignment. In the first part of the experiment, the participants merely performed the task. In the second part, they also evaluated their response accuracy on each trial. We replicated variations in early and later stages of error processing and action adaptation as a function of error detection. The additional self-evaluation enhanced error processing on later stages, probably reflecting error evidence accumulation, whereas earlier error monitoring processes were not amplified. Implementing multivariate pattern analysis revealed that self-evaluation influenced brain activity patterns preceding and following the response onset, independent of response accuracy. The classifier successfully differentiated between responses from the self- and the no-self-evaluation condition several hundred milliseconds before response onset. Subsequent exploratory analyses indicated that both self-evaluation and the time on task contributed to these differences in brain activity patterns. This suggests that in addition to its effect on error processing, self-evaluation in a complex choice task seems to have an influence on early and general processing mechanisms (e.g., the quality of attention and stimulus encoding), which is amplified by the time on task.

Motor inhibition errors and interference suppression errors differ systematically on neural and behavioural features of response monitoring.

Action inhibition and error commission are prominent in everyday life. Inhibition comprises at least two facets: motor inhibition and interference suppression. When motor inhibition fails, a strong response impulse cannot be inhibited. When interference suppression fails, we become distracted by irrelevant stimuli. We investigated the neural and behavioural similarities and differences between motor inhibition errors and interference suppression errors systematically from stimulus-onset to post-response adaptation. To enable a direct comparison between both error types, we developed a complex speeded choice task where we assessed the error types in two perceptually similar conditions. Comparing the error types along the processing stream showed that the P2, an early component in the event-related potential associated with sensory gating, is the first marker for differences between the two error types. Further error-specific variations were found for the parietal P3 (associated with context updating and attentional resource allocation), for the lateralized readiness potential (LRP, associated with primary motor cortex activity), and for the Pe (associated with error evidence accumulation). For motor inhibition errors, the P2, P3 and Pe tended to be enhanced compared to successful inhibition. The LRP for motor inhibition errors was marked by multiple small response impulses. For interference suppression errors, all components were more similar to those of successful inhibition. Together, these findings suggest that motor inhibition errors arise from a deficient early inhibitory process at the perceptual and motor level, and become more apparent than interference suppression errors, that arise from an impeded response selection process.

The association of dispositional anxiety with the NoGo N2 under relaxation instruction vs. speed/accuracy instruction.

Prior research suggests that cognitive control, indicated by NoGo N2 amplitudes in Go/NoGo tasks, is associated with dispositional anxiety. This negative association tends to be reduced in anxiety-enhancing experimental conditions. However, anxiety-reducing conditions have not yet been investigated systematically. Thus, the present study compares the effect of a relaxation instruction with the conventional speed/accuracy instruction in a Go/NoGo task on the correlation of the NoGo N2 with two subconstructs of dispositional anxiety, namely anxious apprehension and anxious arousal. As the test of differences between correlations needs considerable statistical power, the present study was included into the multi-lab CoScience Project. The hypotheses, manipulation checks, and the main path of pre-processing and statistical analysis were preregistered. Complete data sets of 777 participants were available for data analysis. Preregistered general linear models revealed that the different instructions of the task (speed/accuracy vs. relaxation) had no effect on the association between dispositional anxiety and the NoGo N2 amplitude in general. This result was supported by Cooperative-Forking-Path analysis. In contrast, a preregistered latent growth model with categorical variables revealed that anxious arousal was a negative predictor of the NoGo N2 intercept and a positive predictor of the NoGo N2 slope. Non-preregistered growth models, allowing for correlations of anxious apprehension with anxious arousal, revealed that higher anxious apprehension scores were associated with more negative NoGo N2 amplitudes with increased relaxation. Results are discussed in the context of the compensatory error monitoring hypothesis and the revised Reinforcement Sensitivity Theory.

Narcissism and the perception of failure - evidence from the error-related negativity and the error positivity.

The literature on narcissism suggests two contradictory ways how highly narcissistic individuals deal with their failures: They might avoid consciously recognising their failures to protect their ego or they might vigilantly turn towards their failures to process cues that are important for maintaining their grandiosity. We tried to dissolve these contradictory positions by studying event-related potential components of error processing and their variations with narcissism. With a speeded go/no-go task, we examined how the error-related negativity (Ne; reflecting an early, automatic processing stage) and the error positivity (Pe; associated with conscious error detection) vary with Admiration and Rivalry, two narcissism dimensions, under ego-threatening conditions. Using multilevel models, we showed that participants with high Rivalry displayed higher Ne amplitudes suggesting a heightened trait of defensive reactivity. We did not find variations of either narcissism dimension with the Pe, which would have pointed to weaker error awareness. Thus, our results only supported the second position: a heightened vigilance to errors in narcissism at early, rather automatic processing stages.

Larger error negativity peak amplitudes for accuracy versus speed instructions may reflect more neuro-cognitive alignment, not more intense error processing.

Understanding human error processing is a highly relevant interdisciplinary goal. More than 30 years of research in this field have established the error negativity (Ne) as a fundamental electrophysiological marker of various types of erroneous decisions (e.g. perceptual, economic) and related clinically relevant variations. A common finding is that the Ne is more pronounced when participants are instructed to focus on response accuracy rather than response speed, an observation that has been interpreted as reflecting more thorough error processing. We challenge this wide-spread interpretation by demonstrating that when controlling for the level of non-event-related noise in the participant-average waveform and for single-trial peak latency variability, the significant speed-accuracy difference in the participant-average waveform vanishes. This suggests that the previously reported Ne differences may be mostly attributable to a more precise alignment of neuro-cognitive processes and not (only) to more intense error processing under accuracy instructions, opening up novel perspectives on previous findings.

Neural correlates of error detection during complex response selection: Introduction of a novel eight-alternative response task.

Error processing in complex decision tasks should be more difficult compared to a simple and commonly used two-choice task. We developed an eight-alternative response task (8ART), which allowed us to investigate different aspects of error detection. We analysed event-related potentials (ERP; N = 30). Interestingly, the response time moderated several findings. For example, only for fast responses, we observed the well-known effect of larger error negativity (Ne) in signalled and non-signalled errors compared to correct responses, but not for slow responses. We identified at least two different error sources due to post-experimental reports and certainty ratings: impulsive (fast) errors and (slow) memory errors. Interestingly, the participants were able to perform the task and to identify both, impulsive and memory errors successfully. Preliminary evidence indicated that early (Ne-related) error processing was not sensitive to memory errors but to impulsive errors, whereas the error positivity seemed to be sensitive to both error types.