Model-based action planning involves cortico-cerebellar and basal ganglia networks

Sci Rep. 2016 Aug 19:6:31378. doi: 10.1038/srep31378.


Humans can select actions by learning, planning, or retrieving motor memories. Reinforcement Learning (RL) associates these processes with three major classes of strategies for action selection: exploratory RL learns state-action values by exploration, model-based RL uses internal models to simulate future states reached by hypothetical actions, and motor-memory RL selects past successful state-action mapping. In order to investigate the neural substrates that implement these strategies, we conducted a functional magnetic resonance imaging (fMRI) experiment while humans performed a sequential action selection task under conditions that promoted the use of a specific RL strategy. The ventromedial prefrontal cortex and ventral striatum increased activity in the exploratory condition; the dorsolateral prefrontal cortex, dorsomedial striatum, and lateral cerebellum in the model-based condition; and the supplementary motor area, putamen, and anterior cerebellum in the motor-memory condition. These findings suggest that a distinct prefrontal-basal ganglia and cerebellar network implements the model-based RL action selection strategy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Basal Ganglia / physiology*
  • Brain Mapping / methods*
  • Cerebellar Cortex / physiology*
  • Female
  • Humans
  • Learning
  • Magnetic Resonance Imaging
  • Male
  • Models, Neurological
  • Psychomotor Performance
  • Young Adult