Human hippocampal ripples prioritize model-based learning

作者：Xiaoyu Zhou, Xiongfei Wang, Xiangyu Hu, Haiteng Wang, Jinbo Zhang, Qianqian Yu, Jiahua Xu, Zhibing Xiao, Li He, Yunzhe Liu · 发表于：Neuron · 年份：2026 · DOI：10.1016/j.neuron.2026.03.020 · 被引用次数：2 · 研究领域：Memory and Neural Mechanisms、Functional Brain Connectivity Studies、Neurogenesis and neuroplasticity mechanisms

Humans excel at learning from sparse experience by leveraging internal models of the world to infer the values of options they have never sampled, i.e., model-based learning. However, how the brain supports such learning remains largely unknown. Here, we recorded intracranial electrophysiology (iEEG) from 34 epilepsy patients performing a reinforcement-learning task that required using the task structure to infer the values of unvisited (non-local) paths. Hippocampal ripples were associated with prioritized non-local learning. After each outcome, ripple events carried information about which indirect experience was most valuable to update, with longer ripples showing stronger priority signals. These events coincided with stronger cortical reactivation of high-priority than low-priority paths. Importantly, lateral frontopolar cortex activity precisely synchronized with hippocampal ripples in this post-reward time window, which predicted more effective use of task structure and more accurate non-local value learning. Together, our findings suggest that ripple-centered hippocampal-prefrontal coordination supports efficient model-based learning.