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-learn...
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Published in | eLife Vol. 8 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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eLife Sciences Publications Ltd
16.12.2019
eLife Sciences Publications, Ltd |
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Abstract | 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 a learning element. |
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AbstractList | 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 a learning element. 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 a learning element.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 a learning element. |
Author | Lonsdorf, Tina B Jentsch, Valerie L Gerlicher, Anna Meir Drexler, Shira Merz, Christian J Mertens, Gaetan Andreatta, Marta Richter, Jan Chalkia, Anastasia Wendt, Julia Sjouwerman, Rachel Klingelhöfer-Jens, Maren Beckers, Tom |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31841112$$D View this record in MEDLINE/PubMed |
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Copyright | 2019, Lonsdorf et al. 2019, Lonsdorf et al. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019, Lonsdorf et al 2019 Lonsdorf et al |
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Keywords | non-learner memory neuroscience bias outlier exclusion learning human |
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SubjectTerms | Adult bias Conditioning, Psychological - physiology exclusion Fear - physiology Fear conditioning Female Humans Learning Male memory Neuroscience non-learner outlier Researchers |
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Title | Navigating the garden of forking paths for data exclusions in fear conditioning research |
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