Reward and punishment reversal learning in major depressive disorder

Depression has been associated with impaired reward and punishment processing, but the specific nature of these deficits is less understood and still widely debated. We analyzed reinforcement-based decision-making in individuals diagnosed with major depressive disorder (MDD) to identify the specific decision mechanisms contributing to poorer performance. Individuals with MDD (n = 64) and matched healthy controls (n = 64) performed a probabilistic reversal learning task in which they used feedback to identify which of two stimuli had the highest probability of reward (reward condition) or lowest probability of punishment (punishment condition). Learning differences were characterized using a hierarchical Bayesian reinforcement learning model. While both groups showed reinforcement learning-like behavior, depressed individuals made fewer optimal choices and adjusted more slowly to reversals in both the reward and punishment conditions. Our computational modeling analysis found that depressed individuals showed lower learning rates and, to a lesser extent, lower value sensitivity in both the reward and punishment conditions. Learning rates also predicted depression more accurately than simple performance metrics. These results demonstrate that depression is characterized by a hyposensitivity to positive outcomes, which influences the rate at which depressed individuals learn from feedback, but not a hypersensitivity to negative outcomes as has previously been suggested. Additionally, we demonstrate that computational modeling provides a more precise characterization of the dynamics contributing to these learning deficits, and offers stronger insights into the mechanistic processes affected by depression.