Predictive processing models and affective neuroscience

The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.