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

Abstract
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Abstract

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 compositions 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 200 ms and 300–500 ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated 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.

Data availability

All data have been deposited into the Donders Repository under CC-BY-4.0 license, under identifier https://doi.org/10.34973/5qxw-nn97 . (NOTE: The persistent doi is not yet active during peer review! Data are available to reviewers with a special reviewer access link provided in the manuscript.)

The following data sets were generated

1. Kern P
2. Heilbron M
3. de Lange FP
4. Spaak E
(2022) Tracking predictions in naturalistic music listening using MEG and computational models of music
Donders Repository, doi:10.34973/5qxw-nn97.

https://doi.org/10.34973/5qxw-nn97

The following previously published data sets were used

1. DiLiberto et al
(2020) Cortical encoding of melodic expectations in human temporal cortex
Dryad Digital Repository, doi:10.5061/dryad.g1jwstqmh.

https://doi.org/10.5061/dryad.g1jwstqmh

Article and author information

Author details

Pius Kern

Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

Competing interests
No competing interests declared.
Micha Heilbron

Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

Competing interests
No competing interests declared.
Floris P de Lange

Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

Competing interests
Floris P de Lange, Senior editor, eLife.

"This ORCID iD identifies the author of this article:" 0000-0002-6730-1452
Eelke Spaak

Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

For correspondence
eelke.spaak@donders.ru.nl

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0002-2018-3364

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.Veni.198.065)

Eelke Spaak

European Research Council (101000942)

Floris P de Lange

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The study was approved under the general ethical approval for the Donders Centre for Cognitive Neuroimaging (Imaging Human Cognition, CMO2014/288) by the local ethics committee (CMO Arnhem-Nijmegen, Radboud University Medical Centre). Participants provided written informed consent before the experiment and received monetary compensation.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.