Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders - Niit, Lizelle (Department of Mathematics and Applied Mathematics, 2023)
Mental illness causes enormous suffering for many people. Current treatments do not reliably alleviate that suffering. Unclear conceptualisations of mental disorders combined with little knowledge about their aetiology are roadblocks to developing better treatments. This dissertation reviews attempts to use reinforcement learning models to improve the way we conceptualise some of the processes happening in the brain in mental illness. The hope is that more clearly defining the problems we are dealing with will eventually have a positive impact on our ability to diagnose and treat them. I start by giving an overview of the reinforcement learning framework, and detail some of the reinforcement learning models that have been used to understand mental illness better. I explain the statistical techniques used to compare these models and to estimate parameters once a model has been chosen. This leads in to a survey of what researchers have learned about human behaviour using these techniques. I focus particularly on results related to depression. I argue that key parameters like learning rate and reward sensitivity are closely linked to depressive symptoms. Finally, I speculate about the impact that knowledge of this kind may have on the development of better diagnosis and treatment for mental illness in general and depression specifically.