Effects of subclinical depression on prefrontal-striatal model-based and model-free learning

Abstract

Depression is characterized by deficits in the reinforcement learning (RL) process. Although many computational and neural studies have extended our knowledge of the impact of depression on RL, most focus on habitual control (model-free RL), yielding a relatively poor understanding of goal-directed control (model-based RL) and arbitration control to find a balance between the two. We investigated the effects of subclinical depression on modelbased and model-free learning in the prefrontal-striatal circuitry. First, we found that subclinical depression is associated with the attenuated state and reward prediction error representation in the insula and caudate. Critically, we found that it accompanies the disrupted arbitration control between model-based and model-free learning in the predominantly inferior lateral prefrontal cortex and frontopolar cortex. We also found that depression undermines the ability to exploit viable options, called exploitation sensitivity. These findings characterize how subclinical depression influences different levels of the decision-making hierarchy, advancing previous conflicting views that depression simply influences either habitual or goal-directed control. Our study creates possibilities for various clinical applications, such as early diagnosis and behavioral therapy design.