Just like the sun’s daily cycle or a catchy tune, our ability to perceive and synchronize with rhythms is a fundamental part of human experience. This paper explores various theoretical and computational models that shed light on how we generate, perceive, and coordinate with rhythmic structures in music. From dynamical systems theory to Bayesian inference, different approaches offer unique insights into our rhythmic abilities. Some models focus on brain rhythms and synchronization, while others incorporate real-time adaptation and learning mechanisms. Each approach, though originally designed to tackle specific questions, has the potential to be integrated into a broader framework that deepens our understanding of how we perceive and create rhythm. To learn more about these fascinating models and their implications, check out the full research article!

Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, “the beat.” Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.

Read Full Article (External Site)