Material hardship, not household income, predicts impaired punishment learning: a computational reinforcement learning perspective.

Introduction: Socioeconomic disadvantage has been linked to neurocognitive alterations in reward and loss processing, which may contribute to adverse psychological outcomes. However, the mechanisms through which it influences reinforcement learning remain unclear. Methods: This study employed a Probabilistic Reversal Learning Task to examine how two distinct indicators of disadvantage-material hardship and low household income-affect reward and punishment-based learning in a sample of Chinese undergraduate students. Behavioral responses were analyzed through computational modeling within a reinforcement learning framework, estimating three key parameters: reward learning rate, punishment learning rate, and inverse temperature. Results: Results revealed that material hardship uniquely predicted individual differences in punishment learning rate, whereas household income showed no independent association with any of the model parameters. Discussion: The findings suggest that material hardship may specifically impair the ability to learn from negative outcomes. Furthermore, the study underscores the importance of distinguishing between material hardship and income-based adversity in research examining the cognitive impacts of socioeconomic disadvantage.