Bring a map when exploring the ERP data processing multiverse: A commentary on Clayson et al. 2021

Daniel Feuerriegel; Stefan Bode

doi:10.1016/j.neuroimage.2022.119443

Bring a map when exploring the ERP data processing multiverse: A commentary on Clayson et al. 2021

Daniel Feuerriegel, Stefan Bode

Psychology

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

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.

Original language	English (US)
Article number	119443
Journal	NeuroImage
Volume	259
DOIs	https://doi.org/10.1016/j.neuroimage.2022.119443
State	Published - Oct 1 2022

Keywords

EEG
Electroencephalography
ERPs
Error positivity
Error-related negativity
Event-related potentials
Multiverse analyses

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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10.1016/j.neuroimage.2022.119443

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title = "Bring a map when exploring the ERP data processing multiverse: A commentary on Clayson et al. 2021",

abstract = "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.",

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Bring a map when exploring the ERP data processing multiverse: A commentary on Clayson et al. 2021

Abstract

Keywords

ASJC Scopus subject areas

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