[not published] 1. Protocol to investigate the strategic manipulation of human causal inference through in-silico task design 2. Improving the Adaptivity of Reinforcement Learning Agent Based on the Prefrontal Cortex Meta-Control Theory of the Human Brain
summary : This study introduces a cognitive model and task controller to enhance human causal inference abilities through controlled learning strategies, including one-shot and incremental learning. It aims to optimize the efficiency of learning causal relationships by manipulating the presentation sequence of stimulus-outcome pairs, with potential applications in cognitive training.
summary : This thesis investigates methods to enhance the adaptivity of reinforcement learning agents based on the prefrontal cortex meta-control theory of the human brain. The proposed Meta-Dyna algorithm is designed to adapt flexibly to changes in the environment and has demonstrated optimal performance in various settings.