# Believer-Skeptic meets Actor-Critic: Rethinking the role basal ganglia pathways in decision-making and reinforcement learning

## TL;DR

This paper explores how the brain's basal ganglia pathways dynamically compete during decision-making, challenging traditional views and linking brain function to reinforcement learning theories.

## Contribution

It proposes a new computational model where basal ganglia pathways dynamically encode action uncertainty through competition.

## Key findings

- Basal ganglia pathways may not act independently but compete during action decisions.
- This competition captures action uncertainty and aligns with reinforcement learning principles.
- The model bridges neurocomputational theories of decision-making and learning.

## Abstract

The flexibility of behavioral control is a testament to the brain’s capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning.