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.

The gamma model analysis (GMA): Introducing a novel scoring method for the shape of components of the event-related potential.

BACKGROUND: Research using the event-related potential (ERP) method to investigate cognitive processes has usually focused on the analysis of either individual peaks or the area under the curve as components of interest. These approaches, however, do not analyse or describe the substantial variation in size and shape across the entire individual waveforms. NEW METHOD: Here we show that the precision of ERP analyses can be improved by fitting gamma functions to components of interest. Gamma model analyses provide time-dependent and shape-related information about the component, such as the component's rise and decline. We demonstrated the advantages of the gamma model analysis in a simulation study and in a two-choice response task, as well as a force production task. RESULTS: The gamma model parameters were sensitive to experimental variations, as well as variations in behavioural parameters. COMPARISON WITH EXISTING METHODS: Gamma model analyses provide researchers with additional reliable indicators about the shape of an ERP component's waveform, which previous analytical techniques could not. CONCLUSION: This approach, therefore, provides a novel toolset to better understand the exact relationship between ERP components, behaviour and cognition.