Material Hardship, Not Household Income, Predicts Impaired Punishment Learning: A Computational Reinforcement Learning Perspective

Zhen Wang¹Xu He^2,3Yunsheng Su⁴Laijun Bu⁵Yi Wang^6*

• ¹Guangzhou Xinhua University - Dongguan Campus, Dongguan, China
• ²Guangzhou University of Chinese Medicine, Guangzhou, China
• ³South China Normal University, Guangzhou, China
• ⁴Jinan University, Guangzhou, China
• ⁵Guangdong Pharmaceutical University, Guangzhou, China
• ⁶Guangzhou University, Guangzhou, China

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 socioeconomic disadvantage influences reinforcement learning remain unclear. To address this gap, this study employed a Probabilistic Reversal Learning Task to examine how two distinct indicators of disadvantage—material hardship and low household income—affect both reward and punishment-based learning in a sample of Chinese undergraduate students. Behavioral responses were analyzed through computational modeling using a reinforcement learning framework, estimating three key parameters: reward learning rate (adaptation to positive outcomes), punishment learning rate (adaptation to negative outcomes), and inverse temperature (choice stochasticity). 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. 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.