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#EEGManyLabs: Investigating the replicability of influential EEG experiments

  • Yuri Pavlov
  • , Nika Adamian
  • , Stefan Appelhoff
  • , Mahnaz Arvaneh
  • , Christopher Benwell
  • , Christian Beste
  • , Amy Bland
  • , Daniel Bradford
  • , Florian Bublatzky
  • , Niko Busch
  • , Peter Clayson
  • , Damian Cruse
  • , Artur Czeszumski
  • , Anna Dreber
  • , Guillaume Dumas
  • , Benedikt Ehinger
  • , Giorgio Ganis
  • , Xun He
  • , Jose Hinojosa
  • , Christoph Huber-Huber
  • Michael Inzlicht, Bradley Jack, Magnus Johannesson, Rhiannon Jones, Evgenii Kalenkovich, Laura Kaltwasser, Hamid Karimi-Rouzbahani, Andreas Keil, Peter Konig, Layla Kouara, Louisa Kulke, Cecile Ladouceur, Nicolas Langer, Heinrich Liesefeld, David Luque, Annmarie MacNamara, Liad Mudrik, Muthuraman Muthuraman, Lauren Neal, Gustav Nilsonne, Guiomar Niso, Sebastian Ocklenburg, Robert Oostenvald, Cyril Pernet, Gilles Pourtois, Manuela Ruzzoli, Sarah Sass, Alexander Schaefer, Magdalena Senderecka, Joel Snyder, Christian Tamnes, Emmanuelle Tognoli, Marieke van Vugt, Edelyn Verona, Robin Vloeberghs, Dominik Welke, Jan 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
Pages (from-to)213-229
Number of pages16
JournalCortex
Volume144
DOIs
Publication statusPublished - 2 Apr 2021

Keywords

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

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