Understanding others’ intentions amidst uncertainty is critical for effective social interactions, yet the neural mechanisms under-lying this process are not fully understood. Here, we combined computational modeling and single-trial EEG analysis to examinehow the brain dynamically updates beliefs about others’ intentions in volatile social contexts. A total of 43 healthy volunteers en-gaged in a deception-free advice-taking task, featuring alternating stable and volatile phases that systematically manipulated thereliability of an adviser’s intentions. Using the hierarchical Gaussian filter (HGF), a Bayesian model of learning, we quantifiedtrial-by-trial updates of participants’ beliefs and their neural correlates. EEG amplitudes systematically varied according to taskvolatility, engaging neural regions associated with uncertainty processing such as the fusiform gyrus and posterior cingulatecortex. Sensor-level EEG analyses confirmed a temporal sequence consistent with the hierarchical computations predicted by theHGF, whereby lower-level prediction errors were processed earlier than higher-order volatility-related signals. Moreover, indi-vidual differences in these hierarchical neural processes correlated significantly with psychosocial functioning, suggesting thatdisruptions in Bayesian belief updating may underlie functional impairments in clinical populations. Collectively, our resultsreveal novel neural evidence for hierarchical Bayesian inference during social learning, highlighting its critical role in adaptivesocial behavior and potential relevance to mental health.