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Modelling short- and long-term statistical learning of music as a process of predictive entropy reduction

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  • Niels Christian Hansen
  • Psyche Loui, Department of Psychology and Program in Neuroscience and Behavior, Wesleyan University, United States
  • Peter Vuust
  • Marcus Pearce, Queen Mary University of London, United Kingdom
Statistical learning underlies the generation of expectations with different degrees of uncertainty. In music, uncertainty applies to expectations for pitches in a melody. This uncertainty can be quantified by Shannon entropy from distributions of expectedness ratings for multiple continuations of each melody, as obtained with the probe-tone paradigm. We hypothesised that statistical learning of music can be modelled as a process of entropy reduction. Specifically, implicit learning of statistical regularities allows reduction in the relative entropy (i.e. symmetrised Kullback-Leibler Divergence) between listeners’ prior expectancy profiles and probability distributions of a musical style or of stimuli used in short-term experiments.

Five previous probe-tone experiments with musicians and non-musicians were revisited. In Experiments 1-2 participants rated expectedness for tonal melodies and Charlie Parker solos. Experiments 3-5 tested participants before and after 25-30 mins exposure to 5, 15 or 400 melodies generated from a finite-state grammar using the Bohlen-Pierce scale.

As predicted, we found between-participant differences in relative entropy corresponding to degree and relevance of musical training, and within-participant decreases in entropy after short-term statistical learning of novel music. Thus, whereas inexperienced listeners make high-entropy predictions, following the Principle of Maximum Entropy, statistical learning over varying timescales enables listeners to generate melodic expectations with reduced entropy. These findings are consistent with the Free-Energy Principle, which has been proposed as a unified theory for brain function.
Original languageEnglish
Publication year2015
Publication statusPublished - 2015
EventInternational Conference on Interdisciplinary Advances in Statistical Learning - Basque Center on Cognition, Brain, and Language, Ibaeta University, San Sebastian, Spain
Duration: 18 Jun 201527 Jun 2015


ConferenceInternational Conference on Interdisciplinary Advances in Statistical Learning
LocationBasque Center on Cognition, Brain, and Language, Ibaeta University
CitySan Sebastian

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