Computational Modeling of Reinforcement Learning and Working Memory Systems
Learning is paramount to human success, but the complex processes that underlie it are poorly understood. While cognitive models adequately explain some learning mechanisms, much remains to be discovered about the interactions between different mechanisms. Reinforcement learning (RL) is a slow and robust process that relies on external rewards to guide behavior. Working memory (WM) is a system that can retain recently acquired information for short periods of time. While previous research has demonstrated that both systems contribute to learning, the two mechanisms have rarely been studied together. In this project, I seek to examine what factors may affect how we recruit WM vs. RL for learning. Specifically, I would like to understand how being able to easily label a stimulus modifies the balance of WM and RL for learning. I hope to contribute to our knowledge of learning disorders, best practices in education, and computational neuroscience by testing how the what of learning affects the how of learning.
Message to Sponsor
- Major: Cognitive Science and Computer Science
- Sponsor: Pergo L&S
- Mentor: Anne Collins