Success-efficient/failure-safe strategy for hierarchical reinforcement motor learning
Fig 5
Proposed Computational Model of Hierarchical Motor Learning.
The model integrates reinforcement learning, internal model adaptation, and feedback control to simulate human motor learning under ecological control. Reinforcement learning optimizes motor plans based on trial outcomes, the internal model adapts to new body-environment dynamics, and feedback control compensates for inaccuracies in inverse dynamics. The model follows the experimental protocol, enabling direct comparison with human data and capturing key aspects of adaptation, including trial-by-trial learning and the role of failures in shaping movement strategies.