biostudies-literatureSmith RNIMH NIH HHSNIGMS NIH HHSE74-E87https://www.ebi.ac.uk/biostudies/studies/S-EPMC7955838biostudies-literatureUnknown46(1)<h4>Background</h4>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).<h4>Methods</h4>A previously validated AAC task was completed by 478 participants, including healthy controls (<i>n</i> = 59), people with substance use disorders (<i>n</i> = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (<i>n</i> = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflJournal of psychiatry & neuroscience : JPNGreater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach.PMC7955838P20 GM121312K23 MH108707Kuplicki RKirlic NStewart JLTouthang JPaulus MPAupperle RLKhalsa SSSmith RFeinstein JfalseGreater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach.<h4>Background</h4>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).<h4>Methods</h4>A previously validated AAC task was completed by 478 participants, including healthy controls (<i>n</i> = 59), people with substance use disorders (<i>n</i> = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (<i>n</i> = 260). Using an active inference model, we estimated individual-level values for a model parameter that refl2021-01-01T00:00:00Z2021 Jan2025-04-25T19:28:58.623Z2025-04-06T07:58:21.773ZS-EPMC79558383311949010.1503/jpn.200032