Predictive processing models and affective neuroscience. (December 2021)

• Record Type: Journal Article
• Authors: Lee, Kent M.; Ferreira-Santos, Fernando; Satpute, Ajay B.
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 131(2021)
• Page Start: 211
• Page End: 228
• Publication Date: 2021-12
• Subjects: Predictive processing -- Predictive coding -- Subjective experience -- Ecological validity -- External validity -- Valence -- Degeneracy -- Reverse inference -- Experimental design -- fMR -- Arousal -- Emotion -- MVPA; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2021.09.009
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: The neural basis of affective experience remains elusive. Common experimental paradigms are critiqued from a predictive processing view. Predictive processing may address issues of low reliability and selectivity in fMRI. Predictive processing reformulates "reverse inference" in cognitive neuroscience. Predictive processing models favor external/ecological validity in task design. Abstract: 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.