The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 2 of 35106
Back to Result List

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.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details: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
Title of parent work (English):eLife
Publisher:eLife Sciences Publications
Place of publishing:Cambridge
Publication type:Article
Language:English
Year of first publication:2019
Publication year:2019
Release date:2020/09/24
Volume:8
Number of pages:36
Funding 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]
Organizational units:Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie
Peer review:Referiert
Publishing method:Open Access
Open Access / Gold Open-Access
DOAJ gelistet
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.