Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders
| dc.contributor.advisor | Shock, Jonathan | |
| dc.contributor.author | Niit, Lizelle | |
| dc.date.accessioned | 2023-04-13T10:21:07Z | |
| dc.date.available | 2023-04-13T10:21:07Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-12T09:29:44Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Niit, L. (2022). <i>Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders</i>. (). ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/37709 | en_ZA |
| dc.identifier.chicagocitation | Niit, Lizelle. <i>"Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders."</i> ., ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2022. http://hdl.handle.net/11427/37709 | en_ZA |
| dc.identifier.citation | Niit, L. 2022. Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders. . ,Faculty of Science ,Department of Mathematics and Applied Mathematics. http://hdl.handle.net/11427/37709 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Niit, Lizelle AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Mathematics and Applied Mathematics LK - https://open.uct.ac.za PY - 2022 T1 - Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders TI - Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders UR - http://hdl.handle.net/11427/37709 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37709 | |
| dc.identifier.vancouvercitation | Niit L. Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders. []. ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37709 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Mathematics and Applied Mathematics | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Mathematics and Applied Mathematics | |
| dc.title | Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |