In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning, remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model employs bursts at theta frequency to flexibly bind appropriate task modules by synchrony. 64-channel EEG signal was recorded while 27 human subjects (Female: 21, Male: 6) performed a probabilistic reversal learning task. In line with the Sync model, post-feedback theta power showed a linear re...