Rhythmic complexity and predictive coding: a novel approach to modeling rhythm and meter perception in music

Area of Science:

• Cognitive Neuroscience
• Music Psychology
• Computational Neuroscience

Background:

• Musical rhythm profoundly impacts human cognition and behavior.
• Understanding the neural basis of rhythm perception is crucial for explaining music's effect.
• Existing theories offer hierarchical and computational models of rhythm and meter perception.

Purpose of the Study:

• To propose predictive coding (PC) as a unifying framework for understanding musical rhythm processing in the brain.
• To explain how rhythmic complexity and tension in music are perceived and experienced.
• To explore the neural and behavioral mechanisms underlying our engagement with musical rhythm.

Main Methods:

• Reviewing theories of rhythm and meter perception.
• Presenting the principles of predictive coding (PC) theory.
• Developing a PC model for musical rhythm perception integrating auditory input and internal expectations (meter).
• Analyzing empirical studies on syncopation, polyrhythm, and groove within the PC framework.

Main Results:

• Musical rhythm perception can be conceptualized as an interaction between auditory input and the brain's predictive structuring (meter).
• Rhythmic complexity and tension are processed through the brain's hierarchical prediction error minimization.
• Studies on syncopation, polyrhythm, and groove align with PC theory's principles of prediction.

Conclusions:

• Musical rhythm leverages the brain's fundamental predictive mechanisms.
• The pleasure and desire for sensorimotor synchronization in music may arise from these predictive processes.
• PC offers a robust framework for future research into music cognition and the neuroscience of rhythm.