A large body of work has aimed to define the precise information encoded by dopaminergic projections innervating the nucleus accumbens (NAc). Prevailing models are based on reward prediction error (RPE) theory, in which dopamine updates associations between rewards and predictive cues by encoding perceived errors between predictions and outcomes. However, RPE cannot describe multiple phenomena to which dopamine is inextricably linked, such as behavior driven by aversive and neutral stimuli. We combined a series of behavioral tasks with direct, subsecond dopamine monitoring in the NAc of mice, machine learning, computational modeling, and optogenetic manipulations to describe behavior and related dopamine release patterns across multiple contingencies reinforced by differentially valenced outcomes. We show that dopamine release only conforms to RPE predictions in a subset of learning scenarios but fits valence-independent perceived saliency encoding across conditions. Here, we provide an extended, comprehensive framework for accumbal dopamine release in behavioral control.