Changes in corticostriatal connectivity during reinforcement learning in humans.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Computational models of reinforcement learning often propose that learning involves changes in synaptic efficacy between cortical and striatal regions.
• Empirical evidence for this assumption in humans, particularly regarding functional connectivity, has been limited.

Purpose of the Study:

• To investigate the neural basis of reinforcement learning in humans.
• To examine changes in brain activity and functional connectivity during learning in a virtual environment.
• To provide empirical support for computational models of reinforcement learning.

Main Methods:

• Functional magnetic resonance imaging (fMRI) was used to record hemodynamic signals.
• Participants navigated a virtual maze to find hidden rewards.
• A reinforcement-learning algorithm was fitted to choice behavior to identify trial-by-trial learning variables.
• Neural activity and functional connectivity changes related to learning were evaluated.

Main Results:

• Activity in the posterior putamen increased progressively during learning.
• Functional connectivity between the sensorimotor cortex and posterior putamen strengthened over the course of learning.
• These changes in corticostriatal connectivity distinguished successful learners from non-learners.

Conclusions:

• The findings provide direct evidence linking changes in corticostriatal functional connectivity to reinforcement learning in humans.
• This supports a key assumption of many computational models of reinforcement learning.
• The study highlights the role of sensorimotor cortex-posterior putamen interactions in adaptive behavior and learning.