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Smith R, Lane RD, Parr T, et al. Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance. Neurosci Biobehav Rev. 2019;107:473-491doi: 10.1016/j.neubiorev.2019.09.002.
Smith, R., Lane, R. D., Parr, T., & Friston, K. J. (2019). Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance. Neuroscience and biobehavioral reviews, 107473-491. https://doi.org/10.1016/j.neubiorev.2019.09.002
Smith, Ryan, et al. "Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance." Neuroscience and biobehavioral reviews vol. 107 (2019): 473-491. doi: https://doi.org/10.1016/j.neubiorev.2019.09.002
Smith R, Lane RD, Parr T, Friston KJ. Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance. Neurosci Biobehav Rev. 2019 Dec;107:473-491. doi: 10.1016/j.neubiorev.2019.09.002. Epub 2019 Sep 10. PMID: 31518636.
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Neurosci Biobehav Rev. 2019 Dec;107:473-491. doi: 10.1016/j.neubiorev.2019.09.002. Epub 2019 Sep 10.

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

Neuroscience and biobehavioral reviews

Ryan Smith, Richard D Lane, Thomas Parr, Karl J Friston

Affiliations

  1. Laureate Institute for Brain Research, Tulsa, OK, USA. Electronic address: [email protected].
  2. Department of Psychiatry, University of Arizona, Tucson, AZ, USA.
  3. Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.

PMID: 31518636 DOI: 10.1016/j.neubiorev.2019.09.002

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.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords: Active inference; Computational neuroscience; Emotional awareness; Emotional working memory; Somatic misattribution

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