Dynamic models for musical rhythm perception and coordination

• Published in: Frontiers in computational neuroscience Vol. 17; p. 1151895
• Main Authors: Large, Edward W, Roman, Iran, Kim, Ji Chul, Cannon, Jonathan, Pazdera, Jesse K, Trainor, Laurel J, Rinzel, John, Bose, Amitabha
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 17.05.2023; Frontiers Media S.A
• Subjects: Adaptation; Bayesian analysis; Bayesian modeling; beat perception; Brain research; Computational neuroscience; dynamical systems; entrainment; Mathematical models; Music; Musicians & conductors; neuro-mechanistic modeling; Neuroscience; Perception; Perceptions; Rhythm; Rhythms; Synchronization; dynamical systems; music; neuro-mechanistic modeling; Bayesian modeling; entrainment; synchronization; beat perception
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2023.1151895

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.