Abstract
Learning the causes of rewards is crucial for survival. Cue-reward associative learning is controlled in the brain by mesolimbic dopamine. It is widely believed that dopamine drives learning by conveying a reward prediction error. Dopamine-based learning algorithms are generally 'trial-based': learning progresses sequentially across individual cue-outcome experiences. A foundational assumption of these models is that the more cue-reward pairings one experiences over a fixed duration, the more one learns this association. By identifying a new biological principle governing learning, we disprove this assumption. Specifically, across many conditions in mice, we show that behavioral and dopaminergic learning rates are proportional to the duration between rewards (or punishments). Due to this rule, the overall learning over a fixed duration is independent of the number of cue-outcome experiences. A dopamine-based model of retrospective learning explains these findings, thereby providing a unified account of the biological mechanisms of learning.