Information-Theoretic Measures Predict the Human Judgment of Rhythm Complexity

Cogn Sci. 2017 Apr;41(3):800-813. doi: 10.1111/cogs.12347. Epub 2016 Feb 17.


To formalize the human judgment of rhythm complexity, we used five measures from information theory and algorithmic complexity to measure the complexity of 48 artificially generated rhythmic sequences. We compared these measurements to human prediction accuracy and easiness judgments obtained from a listening experiment, in which 32 participants guessed the last beat of each sequence. We also investigated the modulating effects of musical expertise and general pattern identification ability. Entropy rate and Kolmogorov complexity were correlated with prediction accuracy, and highly correlated with easiness judgments. A logistic regression showed main effects of musical training, entropy rate, and Kolmogorov complexity, and an interaction between musical training and both entropy rate and Kolmogorov complexity. These results indicate that information-theoretic concepts capture some salient features of the human judgment of rhythm complexity, and they confirm the influence of musical expertise on complexity judgments.

Keywords: Entropy rate; Information theory measures; Kolmogorov complexity; Musical expertise; Rhythm complexity; Rhythm perception.

MeSH terms

  • Adult
  • Auditory Perception / physiology*
  • Entropy
  • Female
  • Humans
  • Judgment / physiology*
  • Male
  • Middle Aged
  • Music / psychology*
  • Young Adult