Optimal quantification of fear conditioning from skin conductance data

Accurate quantification of fear-conditioned skin conductance responses (SCR) depends critically on the validity of the underlying model of the psychophysiological process. In the framework of Psychophysiological Modelling (PsPM), the peripheral response model linking sudomotor nerve activity (SNA) to observed skin conductance changes has been extensively studied. In contrast, the neural model linking conditioned stimuli (CS) to SNA has not been systematically evaluated. Here, we assessed the retrodictive validity of a comprehensive set of model specifications, preprocessing choices, and parameter mappings using seventeen publicly available fear-conditioning datasets (N = 543). We examined short (≤4 s) and long (>6 s) CS–US intervals separately, and for long intervals tested both single- and dual-component neural response models. For both interval types, several optimized PsPM methods substantially outperformed the current PsPM default and all traditional peak scoring approaches. Bootstrap analyses showed that at least 100-200 participants are required to reliably distinguish method performance. These findings provide empirically validated recommendations for SCR quantification and highlight the importance of calibration-based optimization in psychophysiological measurement.