Information-Theoretic Measures Predict the Human Judgment of Rhythm Complexity.

Area of Science:

• Cognitive Science
• Music Psychology
• Information Theory

Background:

• Human perception of rhythm complexity is subjective and difficult to quantify.
• Previous studies have explored factors influencing rhythm perception, but a formal link to complexity measures is lacking.

Purpose of the Study:

• To formalize human judgment of rhythm complexity using information theory and algorithmic complexity measures.
• To investigate the relationship between objective complexity measures and subjective human judgments (prediction accuracy, easiness).
• To explore the role of musical expertise and pattern identification ability in complexity perception.

Main Methods:

• Generated 48 artificial rhythmic sequences.
• Applied five measures from information theory and algorithmic complexity to quantify sequence complexity.
• Conducted a listening experiment with 32 participants to gather prediction accuracy and easiness judgments.
• Utilized logistic regression to analyze the effects of complexity measures and individual differences.

Main Results:

• Entropy rate and Kolmogorov complexity correlated with prediction accuracy.
• Entropy rate and Kolmogorov complexity showed a high correlation with easiness judgments.
• Musical training, entropy rate, and Kolmogorov complexity had main effects on complexity judgments.
• An interaction was found between musical training and both entropy rate and Kolmogorov complexity.

Conclusions:

• Information-theoretic and algorithmic complexity concepts capture key aspects of human rhythm complexity judgment.
• Musical expertise significantly modulates the perception and judgment of rhythm complexity.
• Objective complexity measures can serve as a basis for understanding subjective rhythm perception.