| Literature DB >> 33965167 |
Yuri G Pavlov1, Nika Adamian2, Stefan Appelhoff3, Mahnaz Arvaneh4, Christopher S Y Benwell5, Christian Beste6, Amy R Bland7, Daniel E Bradford8, Florian Bublatzky9, Niko A Busch10, Peter E Clayson11, Damian Cruse12, Artur Czeszumski13, Anna Dreber14, Guillaume Dumas15, Benedikt Ehinger16, Giorgio Ganis17, Xun He18, José A Hinojosa19, Christoph Huber-Huber20, Michael Inzlicht21, Bradley N Jack22, Magnus Johannesson23, Rhiannon Jones24, Evgenii Kalenkovich25, Laura Kaltwasser26, Hamid Karimi-Rouzbahani27, Andreas Keil28, Peter König29, Layla Kouara30, Louisa Kulke31, Cecile D Ladouceur32, Nicolas Langer33, Heinrich R Liesefeld34, David Luque35, Annmarie MacNamara36, Liad Mudrik37, Muthuraman Muthuraman38, Lauren B Neal39, Gustav Nilsonne40, Guiomar Niso41, Sebastian Ocklenburg42, Robert Oostenveld20, Cyril R Pernet43, Gilles Pourtois44, Manuela Ruzzoli45, Sarah M Sass46, Alexandre Schaefer47, Magdalena Senderecka48, Joel S Snyder49, Christian K Tamnes50, Emmanuelle Tognoli51, Marieke K van Vugt52, Edelyn Verona11, Robin Vloeberghs53, Dominik Welke54, Jan R Wessel55, Ilya Zakharov56, Faisal Mushtaq57.
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.Entities:
Keywords: Cognitive neuroscience; EEG; ERP; Many labs; Open science; Replication
Mesh:
Year: 2021 PMID: 33965167 DOI: 10.1016/j.cortex.2021.03.013
Source DB: PubMed Journal: Cortex ISSN: 0010-9452 Impact factor: 4.027