To efficiently represent all of the possible rewards in the world, dopaminergic midbrain neurons dynamically adapt their coding range to the momentarily available rewards. Specifically, these neurons increase their activity for an outcome that is better than expected and decrease it for an outcome worse than expected, independent of the absolute reward magnitude. Although this adaptive coding is well documented, it remains unknown how this rescaling is implemented. To investigate the adaptive coding of prediction errors and its underlying rescaling process, we used human functional magnetic resonance imaging (fMRI) in combination with a reward prediction task that involved different reward magnitudes. We demonstrate that reward prediction errors in the human striatum are expressed according to an adaptive coding scheme. Strikingly, we show that adaptive coding is gated by changes in effective connectivity between the striatum and other reward-sensitive regions, namely the midbrain and the medial prefrontal cortex. Our results provide evidence that striatal prediction errors are normalized by a magnitude-dependent alteration in the interregional connectivity within the brain's reward system.