Research · since 2022
Executive control
Can a world model (frontal cortex) gated by a model-free learner (basal ganglia) explain executive control, and give RL agents the benefits of both?
How we got here
- 2025Guixuan Jiang, Masters · Monash explicit belief drafts for SudokuAbout Guixuan’s project →
- 2026David Parker, Masters · Monash working-memory eviction in TransformersAbout David’s project →
Project notes
Executive control with Biological Model-free and Model-based RL (2022-)
In this project we will build a novel RL architecture inspired by the brain, with a world model representing the Frontal Cortex, gated by a model-free algorithm representing the Basal Ganglia. Together the agent has the benefits of model-free and model-based approaches, overcoming limitations of each. The functional connectivity between Basal Ganglia and Frontal Cortex is based on the PBWM model for working memory by O’Reilly and Frank.
The objectives are twofold:
- explain aspects of executive control better than Model-Based, Model-Free or other approaches of combining them, with implications for computational psychiatry
- show favourable properties for RL in AI (sample efficiency, better ability to generalize)