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Computational-Model-Based Analysis of Context Effects on Harmonic Expectancy.

Satoshi Morimoto1, Gerard B Remijn2,3, Yoshitaka Nakajima2,3

  • 1Department of Design, Graduate School of Design, Kyushu University, Fukuoka, Japan.

Plos One
|March 23, 2016
PubMed
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Area of Science:

  • Cognitive Science
  • Music Theory
  • Computational Auditory Perception

Background:

  • Harmonic expectancy, crucial for music perception, is often explained by Western music theory.
  • Short chord sequences pose challenges for traditional music theory interpretations.
  • Computational processes underlying harmonic expectancy require further investigation.

Purpose of the Study:

  • To computationally model harmonic expectancy using stochastic methods.
  • To investigate the influence of preceding chords on harmonic expectancy.
  • To clarify the relationship between harmonic expectancy and tonality.

Main Methods:

  • A behavioral experiment involving participants listening to short chord sequences.
  • Subjective evaluation of chord relatedness by participants.
  • Development and comparison of stochastic models based on behavioral data.

Main Results:

  • Stochastic models successfully captured harmonic expectancy in short chord sequences.
  • Internally constructed tonal assumptions were found to influence expectancy.
  • Model comparison revealed the explanatory power of computational approaches.

Conclusions:

  • Tonal assumptions, dynamically updated by the listener, guide harmonic expectancy.
  • Computational modeling offers valuable insights into music perception beyond traditional theory.
  • This study provides a data-driven approach to understanding harmonic processing.