Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder.

ABSTRACT:

Background

Alcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.

Methods

Twenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent

SUBMITTER: Bagci B 

PROVIDER: S-EPMC9626515 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature