SummaryLearned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While theoretical and computational frameworks for prediction exist, circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling and machine learning, we show that trial history and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual prediction task. Predictive beta feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, a NMDA receptor blocker – but not the control drug dexmedetomidine – perturbed predictions, their representation in higher-order cortex, and feedback to posterior cortex. This study suggests predictions depend on alpha activity in higher-order cortex, beta feedback and NMDA receptors, and ketamine blocks access to learned predictive information.