A Probabilistic Model of Meter Perception: Simulating Enculturation.

Area of Science:

• Cognitive Science
• Music Cognition
• Computational Auditory Neuroscience

Background:

• Enculturation significantly influences musical meter perception, yet current cognitive models lack explicit mechanisms to account for this.
• Predictive coding offers a framework where interpreting rhythmic onsets relative to a periodic meter enhances prediction of future events.

Purpose of the Study:

• To propose a predictive coding-based cognitive model for meter perception that incorporates cultural influences.
• To develop a probabilistic model that infers meter from rhythmic patterns based on statistical properties derived from exposure.

Main Methods:

• A probabilistic model was developed to infer meter from quantized rhythms using statistical relationships between rhythm and meter.
• The model was tested on quantized rhythmic patterns from folk melodies to predict annotated time signatures.
• A simulation of enculturation was performed to explore the model's behavior under varying rhythmic exposure histories.

Main Results:

• The model successfully predicted time signatures from folk melody rhythms, demonstrating its ability to infer meter.
• Inferring meter improved the prediction of future event onsets compared to models without meter inference.
• The enculturation simulation identified specific rhythm classes likely to be interpreted differently based on listener's rhythmic exposure.

Conclusions:

• Predictive coding provides a viable mechanism for understanding how enculturation shapes meter perception in music.
• The developed probabilistic model offers a computational approach to meter inference and prediction, accounting for learned rhythmic patterns.
• Individual differences in rhythmic experience can lead to distinct interpretations of musical meter, highlighting the role of enculturation.