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

eLife

Tom Beckers¹  Christian J Merz²  Anna Gerlicher³  Anastasia Chalkia³  Rachel Sjouwerman⁴  Marta Andreatta⁵  Jan Richter⁶  Tina B Lonsdorf⁷  Maren Klingelhöfer-Jens⁷  Julia Wendt⁷  Valerie L Jentsch⁸  Gaetan Mertens⁹  Shira Meir Drexler⁹ 

[1] Instutute of Psychology, Education & Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands;Biological Psychology and Affective Science, University of Potsdam, Potsdam, Germany;Centre for the Psychology of Learning and Experimental Psychopathology and Leuven Brain Institute, KU Leuven, Leuven, Belgium;Department of Physiological and Clinical Psychology/Psychotherapy, University of Greifswald, Greifswald, Germany;Department of Psychology, Biological Psychology, Clinical Psychology and Psychotherapy, University of Würzburg, Würzburg, Germany;Department of Psychology, Utrecht University, Utrecht, Netherlands;Department of Systems Neuroscience, University Medical Center Hamburg Eppendorf, Hamburg, Germany;Faculty of Social and Behavioural Sciences, Programme group Clinical Psychology, University of Amsterdam, Amsterdam, Netherlands;Institute of Cognitive Neuroscience, Department of Cognitive Psychology, Ruhr University Bochum, Bochum, Germany;

关键词: learning;    memory;    exclusion;    outlier;    bias;    non-learner;   

DOI  :  10.7554/eLife.52465

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown