PUB - Publikationen an der Universität Bielefeld

**Abstract**
Electrodermal signals are commonly used outcome measures in research on arousal, emotion, and habituation. Recently, we reported on heterogeneity in skin conductance response quantification approaches and its impact on replicability. Here we provide complementary work focusing on within‐approach heterogeneity of specifications for skin conductance response quantification. We focus on heterogeneitywithin thebaseline‐correction approach (BLC) which appeared as particularly heterogeneous—for instance with respect to the pre‐CS baseline window duration, the start, and end of the peak detection window. We systematically scrutinize the robustness of results when applying different BLC approach specifications to one representative pre‐existing data set (N = 118) in a (partly) pre‐registered study. We report high agreement between different BLC approaches for US and CS+ trials, but moderate to poor agreement for CS− trials. Furthermore, a specification curve of the main effect of CS discrimination during fear acquisition training from all potential and reasonable combinations of specifications (N = 150) and a prototypical trough‐to‐peak (TTP) approach indicates that resulting effect sizes are largely comparable. A second specification curve (N = 605 specific combinations) highlights a strong impact of different transformation types. Crucially, however, we show that BLC approaches often misclassify the peak value—particularly for CS− trials, leading to stimulus‐specific biases and challenges for post‐processing and replicability of CS discrimination across studies applying different approaches. Lastly, we investigate how negative skin conductance values in BLC, appearing most frequently for CS− (CS− > CS+ > US), correspond to values in TTP quantification. We discuss the results considering prospects and challenges of the multiverse approach and future directions.

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This study navigates through the robustness of different non‐model based skin conductance quantification approaches. Systematic multiverse perspectives on different ways to quantify the data highlight that even with overall high agreement and replicability of large effects between different quantification approaches, caution is warranted in light of systematic biases.