Computational modelling of impaired control in addiction (TRR 265, Project B09)
Project B09 aims to develop and test a comprehensive computational model of addictive behavior integrating Pavlovian and instrumental mechanisms. In the theoretical part, we will build on the active inference framework that allows modelling action control during sequential decision-making. A key feature of the model will be the arbitration and dynamic allocation of habitual and goal-directed control. Importantly, the fully developed computational framework will allow to model dysfunctional execution of drug-associated incentive habits that can be elicited by Pavlovian cues, stress or priming. This model will also encompass various aspects of impulsive behavior, such as steep temporal discounting, insensitivity to risks and losses, as well as failures to mobilize inhibitory control when needed. In the experimental part of the project, we will use the to be extended computational model and novel, tailored behavioral tasks to address three key questions: First, we will test whether a static, trait-like bias to pursue short-term goals and execute habitual behavior at the expense of longterm goals in substance use disorders (SUD, alcohol and tobacco) is due to impaired forward planning under uncertainty
during sequential decision-making. Secondly, we will investigate the dynamic, state dependent allocation of habitual and goal-directed control. We will start to test whether goal-directed control is more affected by non-drug related Pavlovian cues in participants with alcohol us disorder (AUD) and tobacco use disorder (TUD) subjects compared to controls. Thirdly, we will test whether static and dynamic reductions in goal-directed control are associated with control over substance use and the resulting trajectories. Complementary to computational modelling of behavioral data, we will use model-based
fMRI to address these key questions also at the level of brain networks.
Principle Investigators
Prof. Dr. Stefan Kiebel, Technische Universität Dresden
Prof. Dr. Michael N. Smolka, Technische Universität Dresden
Staff
Pascale Fischbach (Ph.D student)
Sascha Frölich (Ph.D student)
Sia Hranova (Researcher)
Funding
402170461 – TRR 265