Contractions in Human Cerebellar-Cortical Manifold Structure Underlie Motor Reinforcement Learning

Abstract

How the brain learns new motor commands through reinforcement involves distributed neural circuits beyond known frontal-striatal pathways, yet a comprehensive understanding of this broader neural architecture remains elusive. Here, using human functional MRI ( N  = 46, 27 females) and manifold learning techniques, we identified a low-dimensional neural space that captured the dynamic changes in whole-brain functional organization during a reward-based trajectory learning task. By quantifying participants’ learning rates through an Actor–Critic model, we discovered that periods of accelerated learning were characterized by significant manifold contractions across multiple brain regions, including areas of limbic and hippocampal cortex, as well as the cerebellum. This contraction reflected enhanced network integration, with notably stronger connectivity between several of these regions and the sensorimotor cerebellum correlating with higher learning rates. These findings challenge the traditional view of the cerebellum as solely involved in error-based learning, supporting the emerging view that it coordinates with other brain regions during reinforcement learning.