Multiverse analyses in fear conditioning research
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Multiverse analyses in fear conditioning research. / Lonsdorf, Tina B; Gerlicher, Anna; Klingelhöfer-Jens, Maren; Krypotos, Angelos-Miltiadis.
In: BEHAV RES THER, Vol. 153, 104072, 06.2022.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Multiverse analyses in fear conditioning research
AU - Lonsdorf, Tina B
AU - Gerlicher, Anna
AU - Klingelhöfer-Jens, Maren
AU - Krypotos, Angelos-Miltiadis
N1 - Copyright © 2022 Elsevier Ltd. All rights reserved.
PY - 2022/6
Y1 - 2022/6
N2 - There is heterogeneity in and a lack of consensus on the preferred statistical analyses in light of a multitude of potentially equally justifiable approaches. Here, we introduce multiverse analysis for the field of experimental psychopathology research. We present a model multiverse approach tailored to fear conditioning research and, as a secondary aim, introduce the R package 'multifear' that allows to run all the models though a single line of code. Model specifications and data reduction approaches were identified through a systematic literature search. The heterogeneity of statistical models identified included Bayesian ANOVA and t-tests as well as frequentist ANOVA, t-test as well as mixed models with a variety of data reduction approaches. We illustrate the power of a multiverse analysis for fear conditioning data based on two pre-existing data sets with partial (data set 1) and 100% reinforcement rate (data set 2) by using CS discrimination in skin conductance responses (SCRs) during fear acquisition and extinction training as case examples. Both the effect size and the direction of effect was impacted by choice of the model and data reduction techniques. We anticipate that an increase in multiverse-type of studies will aid the development of formal theories through the accumulation of empirical evidence and ultimately aid clinical translation.
AB - There is heterogeneity in and a lack of consensus on the preferred statistical analyses in light of a multitude of potentially equally justifiable approaches. Here, we introduce multiverse analysis for the field of experimental psychopathology research. We present a model multiverse approach tailored to fear conditioning research and, as a secondary aim, introduce the R package 'multifear' that allows to run all the models though a single line of code. Model specifications and data reduction approaches were identified through a systematic literature search. The heterogeneity of statistical models identified included Bayesian ANOVA and t-tests as well as frequentist ANOVA, t-test as well as mixed models with a variety of data reduction approaches. We illustrate the power of a multiverse analysis for fear conditioning data based on two pre-existing data sets with partial (data set 1) and 100% reinforcement rate (data set 2) by using CS discrimination in skin conductance responses (SCRs) during fear acquisition and extinction training as case examples. Both the effect size and the direction of effect was impacted by choice of the model and data reduction techniques. We anticipate that an increase in multiverse-type of studies will aid the development of formal theories through the accumulation of empirical evidence and ultimately aid clinical translation.
KW - Bayes Theorem
KW - Extinction, Psychological/physiology
KW - Fear/physiology
KW - Galvanic Skin Response
KW - Humans
KW - Reinforcement, Psychology
U2 - 10.1016/j.brat.2022.104072
DO - 10.1016/j.brat.2022.104072
M3 - SCORING: Journal article
C2 - 35500540
VL - 153
JO - BEHAV RES THER
JF - BEHAV RES THER
SN - 0005-7967
M1 - 104072
ER -
