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Related Experiment Video

Updated: Sep 5, 2025

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
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Bring a map when exploring the ERP data processing multiverse: A commentary on Clayson et al. 2021.

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  • 1Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.

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Summary

Multiverse analyses can reveal how data processing affects event-related potentials (ERPs). However, simply identifying differences is insufficient for optimal data processing recommendations, especially regarding baseline selection.

Keywords:
EEGERPsElectroencephalographyError positivityError-related negativityEvent-related potentialsMultiverse analyses

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Area of Science:

  • Cognitive Neuroscience
  • Psychophysiology
  • Computational Neuroscience

Background:

  • Event-related potentials (ERPs) are crucial for understanding cognitive processes.
  • Data processing choices significantly impact ERP component amplitudes.
  • Multiverse analysis offers a data-driven approach to explore processing effects.

Purpose of the Study:

  • To evaluate the sufficiency of multiverse analysis for deriving optimal ERP data processing pipelines.
  • To critique the recommendations made by Clayson et al. (2021) regarding ERP data processing.
  • To highlight the importance of understanding the mechanisms behind processing-induced ERP differences.

Main Methods:

  • Critique of a multiverse analysis approach applied to ERP data.
  • Focus on the impact of pre-response baseline selection on ERP measures.
  • Analysis of error-related negativity (ERN) and error positivity (Pe) components.

Main Results:

  • Multiverse analysis effectively identifies how data processing choices influence ERP results.
  • The use of pre-response baselines can lead to biased error positivity amplitude measures.
  • Data-driven identification of processing effects does not automatically yield optimal processing pipelines.

Conclusions:

  • Multiverse analyses are valuable for exploring data processing effects on ERPs.
  • Recommendations for optimal ERP data processing require deeper investigation beyond identifying differences.
  • Understanding the 'why' behind processing-induced ERP variations is essential for robust scientific conclusions.