Belief updating in uncertain environments is differentially sensitive to reward and punishment learning: Evidence...

Learning from rewards and punishments relies on prediction errors and belief updating, yet it remains unclear how motivational context reshapes the precision-weighting of prediction errors and neural mechanism while learning in volatile environments. We employed a probabilistic classification task with electroencephalography (EEG) within a hierarchical Bayesian framework to compare reward and punishment learning. Our findings indicate that participants performed better in reward context compared to punishment context. Fitting the hierarchical Bayesian model revealed that punishment drives faster Bayesian belief updates, although these did not translate into improved behavioral outcomes. At the neural level, higher-level precision-weighted prediction error (pwPE₂) was significantly positively correlated with feedback related negativity (FRN) amplitude in the punishment context, and the positive effect of pwPE₂ on P300 amplitude was stronger in the punishment than the reward condition. These results reveal the characteristics of electrophysiological activities under different motivational contexts and highlight differences in the neural mechanisms underlying reward and punishment learning.