Information-theoretic measures predict the human judgment of rhythm complexity

de Fleurian, Remi, Blackwell, Tim, Ben-Tal, Oded and Müllensiefen, Daniel (2017) Information-theoretic measures predict the human judgment of rhythm complexity. Cognitive Science, 41(3), pp. 800-813. ISSN (print) 0364-0213

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Abstract

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.

Item Type: Article
Research Area: Music
Psychology
Faculty, School or Research Centre: Faculty of Arts and Social Sciences (until 2017)
Faculty of Arts and Social Sciences (until 2017) > School of Performance and Screen Studies
Related URLs:
Depositing User: Susan Miles
Date Deposited: 21 Jan 2016 10:13
Last Modified: 31 Aug 2017 10:03
DOI: https://doi.org/10.1111/cogs.12347
URI: http://eprints.kingston.ac.uk/id/eprint/33965

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