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Spiking the expectancy profiles: Modelling short- and long-term statistical learning of music as a process of predictive entropy reduction

Research output: Contribution to conferencePosterResearchpeer-review

  • Niels Chr. Hansen
  • Psyche Loui, Department of Psychology and Program in Neuroscience and Behavior, Wesleyan University, United States
  • Peter Vuust
  • Marcus Pearce, Centre for Digital Music and Centre for Research in Psychology, Queen Mary, University of London, United Kingdom
Melodic expectations have long been quantified using expectedness ratings. Motivated by statistical learning and sharper key profiles in musicians, we model musical learning as a process of reducing the relative entropy between listeners' prior expectancy profiles and probability distributions of a given musical style or of stimuli used in short-term experiments.

Five previous probe-tone experiments with musicians and non-musicians are revisited. Exp. 1-2 used jazz, classical and hymn melodies. Exp. 3-5 collected ratings before and after exposure to 5, 15 or 400 novel melodies generated from a finite-state grammar using the Bohlen-Pierce scale.

We find group differences in entropy corresponding to degree and relevance of musical training and within-participant decreases after short-term exposure. Thus, whereas inexperienced listeners make high-entropy predictions by default, statistical learning over varying timescales enables listeners to generate expectations with reduced entropy.
Original languageEnglish
Publication year1 Jun 2014
Publication statusPublished - 1 Jun 2014
EventThe Neurosciences and Music - V: Cognitive Stimulation and Rehabilitation - Dijon, France
Duration: 29 May 20141 Jun 2014


ConferenceThe Neurosciences and Music - V

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