{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Smith R"],"funding":["NIMH NIH HHS","NIGMS NIH HHS"],"pagination":["E74-E87"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7955838"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["46(1)"],"pubmed_abstract":["<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 refl"],"journal":["Journal of psychiatry & neuroscience : JPN"],"pubmed_title":["Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach."],"pmcid":["PMC7955838"],"funding_grant_id":["P20 GM121312","K23 MH108707"],"pubmed_authors":["Kuplicki R","Kirlic N","Stewart JL","Touthang J","Paulus MP","Aupperle RL","Khalsa SS","Smith R","Feinstein J"],"additional_accession":[]},"is_claimable":false,"name":"Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach.","description":"<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 refl","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jan","modification":"2025-04-25T19:28:58.623Z","creation":"2025-04-06T07:58:21.773Z"},"accession":"S-EPMC7955838","cross_references":{"pubmed":["33119490"],"doi":["10.1503/jpn.200032"]}}