Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance. (December 2019)

• Record Type: Journal Article
• Authors: Smith, Ryan; Lane, Richard D.; Parr, Thomas; Friston, Karl J.
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 107(2019)
• Page Start: 473
• Page End: 491
• Publication Date: 2019-12
• Subjects: Active inference -- Emotional awareness -- Somatic misattribution -- Emotional working memory -- Computational neuroscience; 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.2019.09.002
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: Low emotional awareness (EA) is associated with multiple clinical conditions. The neurocomputational processes underlying EA are poorly understood. We present a deep active inference model of EA that can simulate these processes. This model illustrates 7 distinct mechanisms whereby aberrant processing produces low EA. This may offer distinct targets that could inform individualized treatment selection. Abstract: Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena – such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity – characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals – and provide a principled basis for personalized precision medicine.