Computational modeling of rhythmic expectations: Perspectives, pitfalls, and prospects

Rhythmic structure enables precise temporal expectations that are essential to human communication, including speech and music. Computational models have been developed to account for how humans perceive, produce, and learn rhythmic sequences. However, it is unclear how different types of models relate to each other and how they can be evaluated. In this review and perspective, we discuss how three major classes of models—entrainment, probabilistic, and timekeeper models—have been used to study rhythmic expectations. We critically assess each model class in terms of its level of explanation, the rhythmic behaviors it captures, its ability to account for learning and enculturation, and its ability to integrate other features, such as pitch. We show that entrainment, probabilistic, and timekeeper models differ substantially in the aspects of rhythmic expectations they can capture. To move the field forward, we propose that model comparison and integration are crucial. We identify key challenges to this effort, such as the varying nature of the input and output signals and divergent modeling goals. To address these challenges, we arrive at several practical recommendations: to equate input and output signals when comparing models, to consider several model outcomes beyond goodness-of-fit measures in model evaluation, to use model-integration efforts to inform theory building, and to make code and data openly accessible. Ultimately, understanding how models of rhythmic expectations relate, and how features in these models account for behavioral, neural, and cognitive aspects of rhythmic expectations, will deepen our understanding of a core aspect of human behavior.