4.8 Article

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience

Journal

ELIFE
Volume 11, Issue -, Pages -

Publisher

eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.80935

Keywords

naturalistic music listening; magnetoencephalography; predictive auditory processing; deep neural network; transformer; Human

Categories

Funding

  1. Nederlandse Organisatie voor Wetenschappelijk Onderzoek [016.Veni.198.065]
  2. European Research Council [101000942]
  3. European Research Council (ERC) [101000942] Funding Source: European Research Council (ERC)

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Expectations have a significant impact on our music experience. This study investigates the internal model that shapes melodic predictions during naturalistic music listening. They used various computational models of music, including a state-of-the-art transformer neural network, to quantify melodic surprise and uncertainty. The results suggest that neural surprise primarily reflects short-range musical contexts.
Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compo-sitions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal sensors tracked melodic surprise particularly around 200ms and 300-500ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorpo-rated long-term statistical learning-rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short- range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.

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