Depression has been associated with impaired reward and punishment processing, but the specific nature of these deficits is still widely debated. We analyzed reinforcement-based decision making in individuals 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. Depressed individuals made fewer optimal choices and adjusted more slowly to reversals in both the reward and punishment conditions. Computational modeling revealed 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, but not a hypersensitivity to negative outcomes. Additionally, we demonstrate that computational modeling provides a more precise characterization of the dynamics contributing to these learning deficits, offering stronger insights into the mechanistic processes affected by depression.
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health
- Biological Psychiatry