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Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience
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 regul...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9836393/ https://www.ncbi.nlm.nih.gov/pubmed/36562532 http://dx.doi.org/10.7554/eLife.80935 |
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author | Kern, Pius Heilbron, Micha de Lange, Floris P Spaak, Eelke |
author_facet | Kern, Pius Heilbron, Micha de Lange, Floris P Spaak, Eelke |
author_sort | Kern, Pius |
collection | PubMed |
description | 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 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 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. |
format | Online Article Text |
id | pubmed-9836393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-98363932023-01-13 Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience Kern, Pius Heilbron, Micha de Lange, Floris P Spaak, Eelke eLife Neuroscience 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 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 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. eLife Sciences Publications, Ltd 2022-12-23 /pmc/articles/PMC9836393/ /pubmed/36562532 http://dx.doi.org/10.7554/eLife.80935 Text en © 2022, Kern et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Kern, Pius Heilbron, Micha de Lange, Floris P Spaak, Eelke Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
title | Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
title_full | Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
title_fullStr | Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
title_full_unstemmed | Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
title_short | Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
title_sort | cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9836393/ https://www.ncbi.nlm.nih.gov/pubmed/36562532 http://dx.doi.org/10.7554/eLife.80935 |
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