Simulating melodic and harmonic expectations for tonal cadences using probabilistic models

David R.W. Sears, Marcus T. Pearce, William E. Caplin, Stephen McAdams

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This study examines how the mind’s predictive mechanisms contribute to the perception of cadential closure during music listening. Using the Information Dynamics of Music model (or IDyOM) to simulate the formation of schematic expectations—a finite-context (or n-gram) model that predicts the next event in a musical stimulus by acquiring knowledge through unsupervised statistical learning of sequential structure—we predict the terminal melodic and harmonic events from 245 exemplars of the five most common cadence categories from the classical style. Our findings demonstrate that (1) terminal events from cadential contexts are more predictable than those from non-cadential contexts; (2) models of cadential strength advanced in contemporary cadence typologies reflect the formation of schematic expectations; and (3) a significant decrease in predictability follows the terminal note and chord events of the cadential formula.

Original languageEnglish
Pages (from-to)29-52
Number of pages24
JournalJournal of New Music Research
Issue number1
StatePublished - Jan 1 2018


  • Cadence
  • expectation
  • n-gram models
  • segmental grouping
  • statistical learning


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