The Effects of Working Memory and Interference Control on Reinforcement Learning: Evidence from Computational Modeling and Structural MRI

Reinforcement learning (RL) is crucial for adaptive decision-making in dynamic environments. Although prior studies have investigated the impact of working memory (WM) on RL, the joint contributions of WM and interference control (IC) to RL, as well as their underlying brain morphology, remain unclear. Here, 169 healthy young adults completed a probabilistic RL task manipulating WM load (low vs. high) and interference conditions (no interference vs. with interference), yielding measures of learning accuracy and key RL model parameters, including positive learning rate, inverse temperature, and forgetting parameter. A subset of 144 participants additionally underwent structural MRI to assess gray matter volume (GMV), cortical thickness, and sulcal depth. Results revealed that increasing WM load impaired learning accuracy and was accompanied by reduced learning rates and slower forgetting, with these effects associated with GMV, cortical thickness, and sulcal depth in limbic and frontoparietal control networks. Interference exposure decreased learning rates and accelerating forgetting, with IC-related differences in learning accuracy linked to sulcal depth in the cingulate gyrus. Notably, WM and IC interacted during learning, such that interference exposure selectively impaired learning accuracy under high WM load. In contrast, test accuracy was independently modulated by WM, indicating a dissociation between learning- and test-phase effects. The learning-phase interaction was further associated with thalamic GMV and sulcal depth in the temporal pole. These findings demonstrate how WM and IC jointly shape learning and emphasize the contributions of cortical and subcortical structural features to individual differences in learning.