Proceedings of the Annual Meeting of the Cognitive Science Society

Humans have an intuitive understanding of music. We can predict the ensuing notes of a melody given the first few notes, but what exactly drives these predictions? Previous research on musical cognition explores probabilistic models of melody perception where a melody's structure can be inferred given its surface. Other research theorizes about “musical forces”, forces that are analogous to how we represent the physical world, and which inform the way we form expectations about music. We propose a single model of melodic expectation that combines both ideas using a structured generative model and sequential Monte Carlo inference. The generative model formalizes these musical forces, and combined with inference, enables predicting the last note of a melody given the beginning notes. This model explains human performance in an existing dataset of melodic predictions. The model explains more variance than its ablations, and suggests an “intuitive physics” basis for melodic expectation.