Dynamic causal modelling of anticipatory skin conductance responses

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.