#EEGManyLabs: Investigating the replicability of influential EEG experiments

Yuri G. Pavlov (Lead / Corresponding author), Nika Adamian, Stefan Appelhoff, Mahnaz Arvaneh, Christopher S. Y. Benwell, Christian Beste, Amy R. Bland, Daniel E. Bradford, Florian Bublatzky, Niko A. Busch, Peter E. Clayson, Damian Cruse, Artur Czeszumski, Anna Dreber, Guillaume Dumas, Benedikt Ehinger, Giorgio Ganis, Xun He, José A. Hinojosa, Christoph Huber-HuberMichael Inzlicht, Bradley N. Jack, Magnus Johannesson, Rhiannon Jones, Evgenii Kalenkovich, Laura Kaltwasser, Hamid Karimi-Rouzbahani, Andreas Keil, Peter König, Layla Kouara, Louisa Kulke, Cecile D. Ladouceur, Nicolas Langer, Heinrich R. Liesefeld, David Luque, Annmarie MacNamara, Liad Mudrik, Muthuraman Muthuraman, Lauren B. Neal, Gustav Nilsonne, Guiomar Niso, Sebastian Ocklenburg, Robert Oostenveld, Cyril R. Pernet, Gilles Pourtois, Manuela Ruzzoli, Sarah M. Sass, Alexandre Schaefer, Magdalena Senderecka, Joel S. Snyder, Christian K. Tamnes, Emmanuelle Tognoli, Marieke K. van Vugt, Edelyn Verona, Robin Vloeberghs, Dominik Welke, Jan R. Wessel, Ilya Zakharov, Faisal Mushtaq

Research output: Contribution to journalArticlepeer-review

Abstract

There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

Original languageEnglish
Number of pages17
JournalCortex
Early online date2 Apr 2021
DOIs
Publication statusE-pub ahead of print - 2 Apr 2021

Keywords

  • Cognitive neuroscience
  • EEG
  • ERP
  • Many labs
  • Open science
  • Replication

Fingerprint Dive into the research topics of '#EEGManyLabs: Investigating the replicability of influential EEG experiments'. Together they form a unique fingerprint.

Cite this