Expectation networks have been proposed as a computationally simple method for learning tonal expectations associated with individual scale degrees (Verosky, 2019). Using principles of activation and decay, expectation networks infer the expectation of encountering a given event type followed in the near (but not necessarily immediate) future by any other event type. The current work outlines how these learned expectations can be used to predict melody continuations and tests the predictions against listener responses to a melodic cloze task previously used to compare two other models of melodic expectation, IDyOM and Temperley’s Gaussian model (Morgan, Fogel, Nair, & Patel, 2019; Pearce, 2005; Temperley, 2008). Results of multinomial logistic regression indicate that all three models account for unique variance in listener predictions, with coefficient estimates highest for expectation networks. Despite expectation networks’ computational simplicity relative to IDyOM, direct comparisons between IDyOM and expectation networks similarly yielded higher coefficient estimates for the latter. Although all three models are limited in their ability to incorporate global, hierarchical information about pitch structure, expectation networks seem to benefit from a tendency to predict all three notes of the tonic triad at cadence points while ranking the tonic as the most probable continuation. Our findings suggest that generalized scale degree expectations as captured by expectation networks, stereotypical pitch sequences as captured by IDyOM, and immediate intervallic expectations as captured by Temperley’s model all factor into real-time listener predictions to varying extents, highlighting several possible areas for future work.