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Computational Psychiatry - Neuropsychological Bayesian reinforcement learning
Cognitive science draws inspiration from a myriad of disciplines, and has become increasingly reliant on computational methods. In particular, theories of learning, operant conditioning and decision making have shown a natural synergy with statistical learning algorithms. This offers a unique opport...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2023
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| _version_ | 1867613226128113664 |
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| access_status_str | Open Access |
| author | Wolpe, Zach |
| author2 | Shock, Jonathan |
| author_browse | Shock, Jonathan Wolpe, Zach |
| author_facet | Shock, Jonathan Wolpe, Zach |
| author_sort | Wolpe, Zach |
| collection | Thesis |
| description | Cognitive science draws inspiration from a myriad of disciplines, and has become increasingly reliant on computational methods. In particular, theories of learning, operant conditioning and decision making have shown a natural synergy with statistical learning algorithms. This offers a unique opportunity to derive novel insight into the conditioning process by leveraging computational ideas. Specifically, ideas from Bayesian Inference and Reinforcement Learning. In this thesis, we examine the statistical properties of associative learning under uncertainty. We conducted a neuropsychological experiment on over 100 human subjects to measure a suite of executive functions. The primary experimental task (Card Sorting) gauges one's ability to learn, via inference, the structure of some latent pattern that drives the decision making process. We were able to successfully predict the subjects' behaviour in this task by fitting a Bayesian Reinforcement Learning model, alluding to the mechanics of the latent biological decision generating process and executive functions. Primarily, we detail the relationship between working memory capacity and associative learning. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36943 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:46.693Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36943 Computational Psychiatry - Neuropsychological Bayesian reinforcement learning Wolpe, Zach Shock, Jonathan Cowley, Benjamin Clark, Allan Advanced Analytics Cognitive science mathematical psychology computational psychiatry reinforcement learning Bayesian inference machine learning Cognitive science draws inspiration from a myriad of disciplines, and has become increasingly reliant on computational methods. In particular, theories of learning, operant conditioning and decision making have shown a natural synergy with statistical learning algorithms. This offers a unique opportunity to derive novel insight into the conditioning process by leveraging computational ideas. Specifically, ideas from Bayesian Inference and Reinforcement Learning. In this thesis, we examine the statistical properties of associative learning under uncertainty. We conducted a neuropsychological experiment on over 100 human subjects to measure a suite of executive functions. The primary experimental task (Card Sorting) gauges one's ability to learn, via inference, the structure of some latent pattern that drives the decision making process. We were able to successfully predict the subjects' behaviour in this task by fitting a Bayesian Reinforcement Learning model, alluding to the mechanics of the latent biological decision generating process and executive functions. Primarily, we detail the relationship between working memory capacity and associative learning. 2023-02-21T13:53:26Z 2023-02-21T13:53:26Z 2022 2023-02-21T07:33:09Z Master Thesis Masters MSc http://hdl.handle.net/11427/36943 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Advanced Analytics Cognitive science mathematical psychology computational psychiatry reinforcement learning Bayesian inference machine learning Wolpe, Zach Computational Psychiatry - Neuropsychological Bayesian reinforcement learning |
| thesis_degree_str | Master's |
| title | Computational Psychiatry - Neuropsychological Bayesian reinforcement learning |
| title_full | Computational Psychiatry - Neuropsychological Bayesian reinforcement learning |
| title_fullStr | Computational Psychiatry - Neuropsychological Bayesian reinforcement learning |
| title_full_unstemmed | Computational Psychiatry - Neuropsychological Bayesian reinforcement learning |
| title_short | Computational Psychiatry - Neuropsychological Bayesian reinforcement learning |
| title_sort | computational psychiatry neuropsychological bayesian reinforcement learning |
| topic | Advanced Analytics Cognitive science mathematical psychology computational psychiatry reinforcement learning Bayesian inference machine learning |
| url | http://hdl.handle.net/11427/36943 |
| work_keys_str_mv | AT wolpezach computationalpsychiatryneuropsychologicalbayesianreinforcementlearning |