CoChat
Sign Up
Cited by 91Embodied and Extended CognitionPsychosomatic Disorders and Their TreatmentsNeural and Behavioral Psychology Studies

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

Open access
Smith, Ryan, Lane, Richard D., Parr, Thomas, Friston, Karl J.·2019·Neuroscience & Biobehavioral Reviews
DOI 10.1016/j.neubiorev.2019.09.002

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

Integrity signals

Registry

Crossref · 10.1016/j.neubiorev.2019.09.002

Cites

143 of 143 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Affective inference can be understood as an inference about allostatic control, manifesting computationally as precision on the action model that guides domain general policy selection and is closely related to interoceptive and emotional inference.

    “‘Affectivity’ extends this picture to argue that affective inference can similarly be understood as an inference about allostatic control, manifesting computationally as precision on the action model that guides domain general policy selection and is closely related to interoceptive and emotional inference (Seth and Friston 2016; Fotopoulou and Tsakiris 2017; Smith et al. 2019).”

    From Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution (2021) · cites this paper as Smith et al. 2019

Cites

Flagged first, then by citations

1 of the 144 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Smith, R., Lane, R. D., Parr, T., & Friston, K. J.
Affiliations
  • Laureate Institute for Brain Research (US)
  • University of Arizona (US)
  • Wellcome Centre for Human Neuroimaging (GB)
Corresponding author
Smith, Ryan
ORCID
Venue
Neuroscience & Biobehavioral Reviews
Publisher
Elsevier BV
Published
2019-12
Volume
107
Pages
473-491
ISSN
0149-7634 (ISSN-L)
DOI
10.1016/j.neubiorev.2019.09.002
OpenAlex
W2972527800
Cited by
89
References
144
Citations by year
2026: 6 · 2025: 6 · 2024: 10 · 2023: 15 · 2022: 18 · 2021: 13 · 2020: 18 · 2019: 3
FWCI
1.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuroscience & Biobehavioral Reviews (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Health Sciences › Medicine › Psychiatry and Mental health › Psychosomatic Disorders and Their Treatments
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
emotional awareness, active inference, computational psychiatry, reflective capacity, individual differences, mental illness
MeSH terms
Awareness, Mental Disorders, Emotions, Humans, Individuality, Problem Solving, Neuroimaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review