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Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses o...

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Autores principales: Smith, Ryan, Kirlic, Namik, Stewart, Jennifer L., Touthang, James, Kuplicki, Rayus, Khalsa, Sahib S., Feinstein, Justin, Paulus, Martin P., Aupperle, Robin L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Joule Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955838/
https://www.ncbi.nlm.nih.gov/pubmed/33119490
http://dx.doi.org/10.1503/jpn.200032
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author Smith, Ryan
Kirlic, Namik
Stewart, Jennifer L.
Touthang, James
Kuplicki, Rayus
Khalsa, Sahib S.
Feinstein, Justin
Paulus, Martin P.
Aupperle, Robin L.
author_facet Smith, Ryan
Kirlic, Namik
Stewart, Jennifer L.
Touthang, James
Kuplicki, Rayus
Khalsa, Sahib S.
Feinstein, Justin
Paulus, Martin P.
Aupperle, Robin L.
author_sort Smith, Ryan
collection PubMed
description BACKGROUND: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). METHODS: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. RESULTS: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). LIMITATIONS: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. CONCLUSION: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.
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spelling pubmed-79558382021-03-19 Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach Smith, Ryan Kirlic, Namik Stewart, Jennifer L. Touthang, James Kuplicki, Rayus Khalsa, Sahib S. Feinstein, Justin Paulus, Martin P. Aupperle, Robin L. J Psychiatry Neurosci Research Paper BACKGROUND: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). METHODS: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. RESULTS: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). LIMITATIONS: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. CONCLUSION: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders. Joule Inc. 2021-01 /pmc/articles/PMC7955838/ /pubmed/33119490 http://dx.doi.org/10.1503/jpn.200032 Text en © 2021 Joule Inc. or its licensors This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY-NC-ND 4.0) licence, which permits use, distribution and reproduction in any medium, provided that the original publication is properly cited, the use is non-commercial (i.e. research or educational use), and no modifications or adaptations are made. See: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Research Paper
Smith, Ryan
Kirlic, Namik
Stewart, Jennifer L.
Touthang, James
Kuplicki, Rayus
Khalsa, Sahib S.
Feinstein, Justin
Paulus, Martin P.
Aupperle, Robin L.
Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
title Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
title_full Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
title_fullStr Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
title_full_unstemmed Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
title_short Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
title_sort greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955838/
https://www.ncbi.nlm.nih.gov/pubmed/33119490
http://dx.doi.org/10.1503/jpn.200032
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