Key result
Data-driven multiverse analyses of ERP data processing choices are insufficient for devising optimal pipelines without further investigation into why differences occur across choices.
Key points are not available for this paper at this time.
Design
Editorial
Multiverse analyses of ERP data processing choices should be supplemented with mechanistic investigations before making general recommendations.
Clayson et al. (2021) describe an innovative multiverse analysis to evaluate effects of data processing choices on event-related potential (ERP) measures. Based on their results, they provide data processing recommendations for studies measuring the error-related negativity and error positivity components. We argue that, although their data-driven approach is useful for identifying how data processing choices influence ERP results, it is not sufficient for devising optimal data processing pipelines. As an example, we focus on the inappropriate use of pre-response ERP baselines in their analyses, which leads to biased error positivity amplitude measures. Results of multiverse analyses should be supplemented with further investigation into why differences in ERP results occur across data processing choices before devising general recommendations.
No takes yet. Share an insight, caveat, or question.
Feuerriegel et al. (2022) reported an editorial. Data-driven multiverse analyses of ERP data processing choices are insufficient for devising optimal pipelines without further investigation into why differences occur across choices.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: