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A data multiverse analysis investigating non-model based SCR quantification approaches.
Psychophysiology. 2022 12; 59(12):e14130.P

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 heterogeneity within the baseline-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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Publisher Full Text (DOI)

Authors+Show Affiliations

Sjouwerman R
Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Experimental Health Psychology, Maastricht University, Maastricht, The Netherlands.
Illius S
Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany.
Kuhn M
Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Department of Psychiatry, Harvard Medical School, Center for Depression, Anxiety and Stress, Research, McLean Hospital, Belmont, Massachusetts, USA.
Lonsdorf TB
Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

MeSH

HumansConditioning, ClassicalGalvanic Skin ResponseFearArousalExtinction, Psychological

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

35780077

Citation

Sjouwerman, Rachel, et al. "A Data Multiverse Analysis Investigating Non-model Based SCR Quantification Approaches." Psychophysiology, vol. 59, no. 12, 2022, pp. e14130.
Sjouwerman R, Illius S, Kuhn M, et al. A data multiverse analysis investigating non-model based SCR quantification approaches. Psychophysiology. 2022;59(12):e14130.
Sjouwerman, R., Illius, S., Kuhn, M., & Lonsdorf, T. B. (2022). A data multiverse analysis investigating non-model based SCR quantification approaches. Psychophysiology, 59(12), e14130. https://doi.org/10.1111/psyp.14130
Sjouwerman R, et al. A Data Multiverse Analysis Investigating Non-model Based SCR Quantification Approaches. Psychophysiology. 2022;59(12):e14130. PubMed PMID: 35780077.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A data multiverse analysis investigating non-model based SCR quantification approaches. AU - Sjouwerman,Rachel, AU - Illius,Sabrina, AU - Kuhn,Manuel, AU - Lonsdorf,Tina B, Y1 - 2022/07/02/ PY - 2022/03/11/revised PY - 2021/08/09/received PY - 2022/04/28/accepted PY - 2022/7/3/pubmed PY - 2022/11/11/medline PY - 2022/7/2/entrez KW - SCL KW - SCR KW - baseline correction KW - multiverse KW - specification curve KW - transformations KW - trough-to-peak SP - e14130 EP - e14130 JF - Psychophysiology JO - Psychophysiology VL - 59 IS - 12 N2 - 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 heterogeneity within the baseline-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. SN - 1540-5958 UR - https://www.unboundmedicine.com/medline/citation/35780077/A_data_multiverse_analysis_investigating_non_model_based_SCR_quantification_approaches_ DB - PRIME DP - Unbound Medicine ER -