Reward processing plays a crucial role in the development and maintenance of maladaptive addictive behaviors. Our research focuses on identifying the various aspects of reward processing and learning that contribute to the risk of developing addictive disorders, with the goal of identifying those that can be targeted for clinical intervention.

Inspired by the network theory of mental disorders, which posits that mental health conditions arise from causal interactions among symptoms, our research utilizes advanced network analysis approaches. These statistical tools allow us to map and analyze the dynamic associations between symptoms and relevant cognitive processes.

Our research focuses on Digital Behavior Change Interventions for health promotion and risk prevention among adolescents. By exploring motivations behind risk-taking, it for example integrates personality-based and motivational approaches with game-based learning to encourage self-reflection and positive behavior change.

Individuals with addictions often exhibit cognitive biases—attentional, behavioral, and evaluative processes triggered by addiction-related cues. These biases play a crucial role in maintaining addictive behaviors. Cognitive Bias Modification (CBM) utilizes a variety of computerized training paradigms tailored to modify these biases, offering a potential low-threshold and easy-to-administer group of add-on interventions for treating addictive behaviors.