An Active Inference Model of Meter Perception and the Urge to Move to Music.

Area of Science:

• Cognitive Neuroscience
• Computational Auditory Neuroscience
• Psychology of Music

Background:

• The phenomenon of wanting to move to certain rhythms is well-documented but lacks a clear mechanistic explanation.
• Predictive processing theories propose that prediction errors drive this urge, yet remain underspecified.

Purpose of the Study:

• To operationalize the predictive processing account using a Bayesian model and active inference.
• To investigate the role of prediction errors and sensory feedback in the urge to move to rhythmic stimuli.

Main Methods:

• Developed a Bayesian model to infer rhythmic templates (metered vs. unmetered).
• Incorporated an active inference rule where movement reduces prediction errors.
• Calculated surprisal (prediction error) with and without a metronome, analyzing delta surprisal.

Main Results:

• Surprisal increased linearly with rhythmic complexity.
• Delta surprisal mirrored previous urge-to-move ratings, confirmed in an online study.
• The urge to move correlates with the potential to reduce meter-based prediction errors via movement feedback.

Conclusions:

• The urge to move to music is explained by the potential to reduce meter-based prediction errors through active inference.
• This study updates predictive processing accounts by highlighting active inference in musical experiences.
• Active inference and prediction-based learning play a key role in how we engage with musical rhythms.