Temporal-difference (TD) learning models afford the neuroscientist a theory-driven roadmap in the quest for the neural mechanisms of reinforcement learning. The application of these models to understanding the role of phasic midbrain dopaminergic responses in reward prediction learning constitutes one of the greatest success stories in behavioural and cognitive neuroscience. Critically, the classic learning paradigms associated with TD are poorly suited to cast light on its neural implementation, thus hampering progress. Here, we present a serial blocking paradigm in rodents that overcomes these limitations and allows for the simultaneous investigation of two cardinal TD tenets; namely, that learning depends on the computation of a prediction error, and that reinforcing value, whether intrinsic or acquired, propagates back to the onset of the earliest reliable predictor. The implications of this paradigm for the neural exploration of TD mechanisms are highlighted.