{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Weigard AS"],"funding":["NIDA NIH HHS","NIMH NIH HHS","NIAAA NIH HHS"],"pagination":["2629-2644"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8452274"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["238(9)"],"pubmed_abstract":["<h4>Rationale</h4>Substance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology.<h4>Objectives</h4>We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance "],"journal":["Psychopharmacology"],"pubmed_title":["Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood."],"pmcid":["PMC8452274"],"funding_grant_id":["R01 AA025790","K01 DA044270","K01 AA024804","T32 AA007477","R01 AA007065","K01 AA027558","R01 MH107741"],"pubmed_authors":["Sripada C","Cope LM","Zucker RA","Brislin SJ","Weigard AS","Hardee JE","Ly A","Martz ME","Heitzeg MM"],"additional_accession":[]},"is_claimable":false,"name":"Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood.","description":"<h4>Rationale</h4>Substance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology.<h4>Objectives</h4>We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Sep","modification":"2025-04-04T09:37:24.266Z","creation":"2025-04-04T09:37:24.266Z"},"accession":"S-EPMC8452274","cross_references":{"pubmed":["34173032"],"doi":["10.1007/s00213-021-05885-w"]}}