Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Decision Science

Background:

• Neural and behavioral data indicate multiple brain systems for behavioral choice.
• The prefrontal cortex and dorsolateral striatum are implicated in distinct choice systems.
• Arbitrating between competing control systems presents a significant challenge.

Purpose of the Study:

• To explore dual-action choice systems from a normative perspective using reinforcement learning theory.
• To identify the trade-off between computational simplicity and flexible, efficient use of experience.
• To propose a principle for arbitrating between the dorsolateral striatal and prefrontal systems.

Main Methods:

• Utilized the computational theory of reinforcement learning.
• Analyzed the trade-offs in dual-action choice systems.
• Proposed a Bayesian arbitration principle based on uncertainty.

Main Results:

• Identified a trade-off between computational simplicity and statistical efficiency in decision-making.
• The competition between dorsolateral striatal and prefrontal systems embodies this trade-off.
• A Bayesian principle for arbitration based on uncertainty was formulated.

Conclusions:

• The proposed Bayesian arbitration principle offers a unifying account for system dominance.
• This principle explains experimental evidence regarding factors favoring prefrontal or dorsolateral striatal control.
• Uncertainty-based arbitration ensures optimal deployment of each choice system.