#EEGManyLabs: Investigating the replicability of influential EEG experiments
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#EEGManyLabs: Investigating the replicability of influential EEG experiments. / Pavlov, Yuri G; Adamian, Nika; Appelhoff, Stefan; Arvaneh, Mahnaz; Benwell, Christopher S Y; Beste, Christian; Bland, Amy R; Bradford, Daniel E; Bublatzky, Florian; Busch, Niko A; Clayson, Peter E; Cruse, Damian; Czeszumski, Artur; Dreber, Anna; Dumas, Guillaume; Ehinger, Benedikt; Ganis, Giorgio; He, Xun; Hinojosa, José A; Huber-Huber, Christoph; Inzlicht, Michael; Jack, Bradley N; Johannesson, Magnus; Jones, Rhiannon; Kalenkovich, Evgenii; Kaltwasser, Laura; Karimi-Rouzbahani, Hamid; Keil, Andreas; König, Peter; Kouara, Layla; Kulke, Louisa; Ladouceur, Cecile D; Langer, Nicolas; Liesefeld, Heinrich R; Luque, David; MacNamara, Annmarie; Mudrik, Liad; Muthuraman, Muthuraman; Neal, Lauren B; Nilsonne, Gustav; Niso, Guiomar; Ocklenburg, Sebastian; Oostenveld, Robert; Pernet, Cyril R; Pourtois, Gilles; Ruzzoli, Manuela; Sass, Sarah M; Schaefer, Alexandre; Senderecka, Magdalena; Snyder, Joel S; Tamnes, Christian K; Tognoli, Emmanuelle; van Vugt, Marieke K; Verona, Edelyn; Vloeberghs, Robin; Welke, Dominik; Wessel, Jan R; Zakharov, Ilya; Mushtaq, Faisal.
in: CORTEX, Jahrgang 144, 11.2021, S. 213-229.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - #EEGManyLabs: Investigating the replicability of influential EEG experiments
AU - Pavlov, Yuri G
AU - Adamian, Nika
AU - Appelhoff, Stefan
AU - Arvaneh, Mahnaz
AU - Benwell, Christopher S Y
AU - Beste, Christian
AU - Bland, Amy R
AU - Bradford, Daniel E
AU - Bublatzky, Florian
AU - Busch, Niko A
AU - Clayson, Peter E
AU - Cruse, Damian
AU - Czeszumski, Artur
AU - Dreber, Anna
AU - Dumas, Guillaume
AU - Ehinger, Benedikt
AU - Ganis, Giorgio
AU - He, Xun
AU - Hinojosa, José A
AU - Huber-Huber, Christoph
AU - Inzlicht, Michael
AU - Jack, Bradley N
AU - Johannesson, Magnus
AU - Jones, Rhiannon
AU - Kalenkovich, Evgenii
AU - Kaltwasser, Laura
AU - Karimi-Rouzbahani, Hamid
AU - Keil, Andreas
AU - König, Peter
AU - Kouara, Layla
AU - Kulke, Louisa
AU - Ladouceur, Cecile D
AU - Langer, Nicolas
AU - Liesefeld, Heinrich R
AU - Luque, David
AU - MacNamara, Annmarie
AU - Mudrik, Liad
AU - Muthuraman, Muthuraman
AU - Neal, Lauren B
AU - Nilsonne, Gustav
AU - Niso, Guiomar
AU - Ocklenburg, Sebastian
AU - Oostenveld, Robert
AU - Pernet, Cyril R
AU - Pourtois, Gilles
AU - Ruzzoli, Manuela
AU - Sass, Sarah M
AU - Schaefer, Alexandre
AU - Senderecka, Magdalena
AU - Snyder, Joel S
AU - Tamnes, Christian K
AU - Tognoli, Emmanuelle
AU - van Vugt, Marieke K
AU - Verona, Edelyn
AU - Vloeberghs, Robin
AU - Welke, Dominik
AU - Wessel, Jan R
AU - Zakharov, Ilya
AU - Mushtaq, Faisal
N1 - Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Cognition
KW - Electroencephalography
KW - Humans
KW - Neurosciences
KW - Reproducibility of Results
U2 - 10.1016/j.cortex.2021.03.013
DO - 10.1016/j.cortex.2021.03.013
M3 - SCORING: Journal article
C2 - 33965167
VL - 144
SP - 213
EP - 229
JO - CORTEX
JF - CORTEX
SN - 0010-9452
ER -