Cortex and subcortex play distinct roles over learning when cortical memory is limited

2026-05-30

AI Summary

How the brain achieves efficient learning under cortical memory constraints through coordinated interactions between cortical and subcortical systems remains unclear. This work proposes a dual-module computational framework in which a memory-constrained model-based module—mimicking the cortex—focuses on learning the environment’s general structure, while a model-free module—representing subcortical systems—specializes in reward-associated learning. Through simulations in environments with frequently changing rewards and multiple memory allocation strategies, the study demonstrates that allocating limited cortical memory resources to representing environmental structure rather than immediate rewards substantially enhances overall learning performance. These findings provide a theoretical foundation for functional specialization across brain regions and propose testable behavioral experiments to validate this mechanism.

Abstract

It has been proposed that the brain integrates flexible, computationally expensive cortical processing with simpler, lower-cost subcortical mechanisms to achieve resource-efficient performance greater than that of either system alone. Despite the allure of this perspective, satisfying theoretical frameworks that explore this hypothesis are still limited. We extend existing frameworks in which a model-based module and model-free module learn in tandem by explicitly constraining the memory resources of the model-based module, and investigate the impact of this constraint in a simple decision-making setting. Memory constraints naturally give rise to strategies for allocating memory resources. We evaluate the performance of different strategies in different situations and demonstrate that when the rewarded states change often, it can be advantageous for the model-based module to focus its memory resources not on exploiting the current reward, but on capturing general structure of the environment. This work provides a theoretical foundation for a functional dissociation between cortical and subcortical systems during learning: the cortex supports general structure learning, while subcortical circuits specialize in reward-based learning. We further detail how these hypotheses can be tested on experimental data.

Problem

• cortex
• subcortex
• memory constraint
• model-based learning
• reward-based learning

Innovation

• model-based learning
• memory constraints
• cortico-subcortical dissociation
• structure learning
• resource-efficient cognition