Key result
Combining autonomic threat measures improves effect size by ~0.09 Cohen's d over single measures.
Why the study?
Quantification of threat conditioning often relies on autonomic responses analysed separately, leaving open whether their variability is multidimensional and whether linear combination enhances inferential precision.
Does the optimal linear combination of multiple autonomic fear-conditioning measures improve the precision of inference regarding whether threat conditioning has occurred compared to single measures?
Meta-Analysis (n=256)
Does the optimal linear combination of multiple autonomic fear-conditioning measures improve the precision of inference regarding whether threat conditioning has occurred compared to single measures?
Effect estimate: average gain in Cohen's d of 0.09 ± 0.06
Combining diverse psychophysiological indices of threat conditioning optimizes statistical power and allows for a 24% reduction in study sample size for equivalent inference.
Supports efficient autonomic research designs; extends single-measure approaches by enabling ~24% smaller samples for equivalent power.
Fear conditioning, also termed threat conditioning, is a commonly used learning model with clinical relevance. Quantification of threat conditioning in humans often relies on conditioned autonomic responses such as skin conductance responses (SCR), pupil size responses (PSR), heart period responses (HPR), or respiration amplitude responses (RAR), which are usually analysed separately. Here, we investigate whether inter-individual variability in differential conditioned responses, averaged across acquisition, exhibits a multi-dimensional structure, and the extent to which their linear combination could enhance the precision of inference on whether threat conditioning has occurred. In a mega-analytic approach, we re-analyze 9 data sets including N = 256 individuals , acquired by the group of the last author, using standard routines in the framework of psychophysiological modelling (PsPM). Our analysis revealed systematic differences in effect size between measures across datasets, but no evidence for a multidimensional structure across various combinations of measures. We derive the statistically optimal weights for combining the four measures and subsets thereof, and we provide out-of-sample performance metrics for these weights, accompanied by bias-corrected confidence intervals. We show that to achieve the same statistical power, combining measures allows for a relevant reduction in sample size, which in a common scenario amounts to roughly 24%. To summarise, we demonstrate a one-dimensional structure of threat conditioning measures, systematic differences in effect size between measures, and provide weights for their optimal linear combination in terms of maximal retrodictive validity.
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Mancinelli et al. (2023) conducted a meta-analysis in Healthy individuals (threat conditioning) (n=256). Optimal linear combination of multiple autonomic threat-conditioning measures (SCR, PSR, HPR, RAR) vs. Best single autonomic measure alone was evaluated on Dimensionality and out-of-sample effect size (Cohen's d) gain of combined conditioned autonomic responses (average gain in Cohen's d of 0.09 ± 0.06). Optimal linear combination of autonomic threat conditioning measures yielded a one-dimensional structure and increased effect size (Cohen's d gain of 0.09) compared to single measures, allowing for a ~24% reduction in required sample size.
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