Dynamic causal modelling of anticipatory skin conductance responses

Dominik R Bach,Jean Daunizeau,Karl J Friston,Raymond J Dolan

doi:10.1016/j.biopsycho.2010.06.007

Dominik R Bach, Jean Daunizeau + Show 2 more

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https://doi.org/10.1016/j.biopsycho.2010.06.007

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Abstract

Anticipatory skin conductance responses [SCRs] are a widely used measure of aversive conditioning in humans. Here, we describe a dynamic causal model [DCM] of how anticipatory, evoked, and spontaneous skin conductance changes are generated by sudomotor nerve activity. Inversion of this model, using variational Bayes, provides a means of inferring the most likely sympathetic nerve activity, given observed skin conductance responses. In two fear conditioning experiments, we demonstrate the predictive validity of the DCM by showing it has greater sensitivity to the effects of conditioning, relative to alternative (conventional) response estimates. Furthermore, we establish face validity by showing that trial-by-trial estimates of anticipatory sudomotor activity are better predicted by formal learning models, relative to response estimates from peak-scoring approaches. The model furnishes a potentially powerful approach to characterising SCR that exploits knowledge about how these signals are generated.

Highlights

Anticipatory skin conductance responses [aSCRs] are a widely used index of aversive Pavlovian conditioning in humans much like anticipatory freezing behaviour used in animal studies
ASCRs to upcoming rewards and punishments are important in the study of human decision making, where they may reflect characteristics of a choice situation, such as variance in expected outcomes (Tomb et al, 2002)
We have described a dynamic causal model for SC changes that includes anticipatory, evoked, and spontaneous skin conductance changes and allows, via model inversion, estimation of the most likely neural contributions to each of these components

Summary

Introduction

Anticipatory skin conductance responses [aSCRs] are a widely used index of aversive Pavlovian conditioning (or fear conditioning) in humans (see e.g. Boucsein, 1992) much like anticipatory freezing behaviour used in animal studies. ASCRs to upcoming rewards and punishments are important in the study of human decision making, where they may reflect characteristics of a choice situation, such as variance in expected outcomes (Tomb et al, 2002). ASCRs form a methodical cornerstone of human associative learning and decision making research. Their quantification relies on detecting a peak or computing the mean response over an anticipation time window, relative to a baseline. Such approaches require a robust baseline, and lengthy inter-trial intervals, a requirement not often met in cognitive neuroscience research. Closely spaced events in cognitive paradigms often lead to

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