Psychophysiological modeling: Current state and future directions

Abstract

Psychologists often use peripheral physiological measures to infer a psychological variable. It is desirable to make this inverse inference in the most precise way, ideally standardized across research laboratories. In recent years, psychophysiological modeling has emerged as a method that rests on statistical techniques to invert mathematically formulated forward models (psychophysiological models, PsPMs). These PsPMs are based on psychophysiological knowledge and optimized with respect to the precision of the inference. Building on established experimental manipulations, known to create different values of a psychological variable, they can be benchmarked in terms of their sensitivity (e.g., effect size) to recover these values-we have termed this predictive validity. In this review, we introduce the problem of inverse inference and psychophysiological modeling as a solution. We present background and application for all peripheral measures for which PsPMs have been developed: skin conductance, heart period, respiratory measures, pupil size, and startle eyeblink. Many of these PsPMs are task invariant, implemented in open-source software, and can be used off the shelf for a wide range of experiments. Psychophysiological modeling thus appears as a potentially powerful method to infer psychological variables.

Impact of a reminder/extinction procedure on threat-conditioned pupil size and skin conductance responses

“…Several reasons may account for this heterogeneity across reminder/extinction studies using SCR, among which is the generally low signal-to-noise ratio of the dependent variable SCR (Staib et al 2015), which would consequently impact on statistical power (Bach et al 2018a) and reduce chances of replication (Goodman et al 2016). As another factor underlying this heterogeneity, small variations in experimental design have been suggested (Lee et al 2017).…”

“…To process and analyze the psychophysiological data we used MATLAB (Version R2018a, Math-Works) and PsPM (Psychophysiological modelling, http://pspm.sourceforge.net, Version 4.0.2), a MATLAB toolbox for model-based analysis of psychophysiological data (Bach and Friston 2013;Bach et al 2018a).…”

Pupil dilation as an implicit measure of appetitive Pavlovian learning

“…The weak relationships among measures emphasize the importance of a multi‐methodological approach when investigating appetitive Pavlovian conditioning. Furthermore, it is would be desirable to standardize approaches of analysis, for instance, by using psychophysiological modeling techniques (Bach et al, ).…”

“…All data were preprocessed using MATLAB R2016a. For HPR, SCR, and PSR, we used psychophysiological modeling techniques by means of the PsPM toolbox (Bach et al, ).…”

Critical evaluation of current data analysis strategies for psychophysiological measures of fear conditioning and extinction in humans

“…Clinical populations, such as those with PTSD, exhibit impaired reduction in CS+ responses during extinction and recall of learned extinction compared to healthy controls (Milad et al, 2009;Wessa & Flor, 2007). Despite the translational basis of fear conditioning and extinction research, there are some existing issues with its operationalization in the laboratory (Bach et al, 2018;Beckers, Krypotos, Boddez, Effting, & Kindt, 2013;Lonsdorf et al, 2017;Lonsdorf & Merz, 2017;Sjouwerman, Niehaus, Kuhn, & Lonsdorf, 2016). Group differences between healthy and clinical populations, such as anxiety for which the primary aetiological mechanism is believed to be fear extinction, are often undetectable in individual studies and may only emerge in meta-analysis as negligible, small or small-moderate effect sizes, which have been reported before correction for publication bias (Beckers et al, 2013;Duits et al, 2015;Lissek et al, 2005).…”