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Navigating the garden of forking paths for data exclusions in fear conditioning research

  • In this report, we illustrate the considerable impact of researcher degrees of freedom with respect to exclusion of participants in paradigms with a learning element. We illustrate this empirically through case examples from human fear conditioning research, in which the exclusion of ‘non-learners’ and ‘non-responders’ is common – despite a lack of consensus on how to define these groups. We illustrate the substantial heterogeneity in exclusion criteria identified in a systematic literature search and highlight the potential problems and pitfalls of different definitions through case examples based on re-analyses of existing data sets. On the basis of these studies, we propose a consensus on evidence-based rather than idiosyncratic criteria, including clear guidelines on reporting details. Taken together, we illustrate how flexibility in data collection and analysis can be avoided, which will benefit the robustness and replicability of research findings and can be expected to be applicable to other fields of research that involve aIn this report, we illustrate the considerable impact of researcher degrees of freedom with respect to exclusion of participants in paradigms with a learning element. We illustrate this empirically through case examples from human fear conditioning research, in which the exclusion of ‘non-learners’ and ‘non-responders’ is common – despite a lack of consensus on how to define these groups. We illustrate the substantial heterogeneity in exclusion criteria identified in a systematic literature search and highlight the potential problems and pitfalls of different definitions through case examples based on re-analyses of existing data sets. On the basis of these studies, we propose a consensus on evidence-based rather than idiosyncratic criteria, including clear guidelines on reporting details. Taken together, we illustrate how flexibility in data collection and analysis can be avoided, which will benefit the robustness and replicability of research findings and can be expected to be applicable to other fields of research that involve a learning element.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Tina B. LonsdorfORCiDGND, Maren Klingelhöfer-JensORCiD, Marta AndreattaORCiD, Tom BeckersORCiD, Anastasia ChalkiaORCiD, Anna GerlicherORCiDGND, Valerie L. JentschORCiD, Shira Meir DrexlerORCiDGND, Gaetan Mertens, Jan RichterORCiD, Rachel Sjouwerman, Julia WendtORCiDGND, Christian J. MerzORCiD
DOI:https://doi.org/10.7554/eLife.52465
ISSN:2050-084X
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/31841112
Titel des übergeordneten Werks (Englisch):eLife
Verlag:eLife Sciences Publications
Verlagsort:Cambridge
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Jahr der Erstveröffentlichung:2019
Erscheinungsjahr:2019
Datum der Freischaltung:24.09.2020
Band:8
Seitenanzahl:36
Fördernde Institution:Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [LO 1980/2-1, LO 1980/1-1, B07 44541416, 316803389 - SFB1280, WE 5873/1-1, WE 5873/5-1]
Organisationseinheiten:Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie
Peer Review:Referiert
Publikationsweg:Open Access
Open Access / Gold Open-Access
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