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Tracking melodic expectations during naturalistic music listening using M|EEG
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2021-12-14
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en
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Abstract
Expectations are widely thought to shape our experience of music. However, neural evidence mainly
stems from studies employing constrained paradigms and stimuli, which challenges the predictive
nature of naturalistic music listening. Furthermore, it is debated which sources and temporal
context melodic expectations rely on. In the current study, we presented human listeners with
naturalistic monophonic compositions from Western classical music, while recording neural activity
using MEG. We quantified note-level melodic surprise and uncertainty via computational models of
music, resembling different sources of melodic expectations: Gestalt principles, short-term
regularities or long-term statistical learning. For the first time, we applied a state-of-the-art
neural network, the Music Transformer, to study music cognition. We demonstrate its sensitivity to
long-range musical structure compared to previous models, which allowed us to probe the influence
of musical context length on neural music processing. A time- resolved regression analysis revealed
that melodic surprise increased neural responses over fronto-temporal areas particularly around 200
ms and 300–500 ms after note onset, which was dissociated from sensory-acoustic and repetition
suppression effects. According to a model comparison on cross-validated predictive performance,
neural surprise was best captured by long-term statistical learning and short-range musical
contexts of less than ten notes. Uncertainty, on the other hand, was not found to modulate neural
activity evoked by notes. All results were confirmed on a recently published EEG dataset (Di
Liberto et al., 2020). Our findings provide evidence for the role of melodic expectations during
naturalistic music listening, that emerge from musical enculturation.
Keywords: Music listening, naturalistic, computational modelling, prediction, statistical
learning
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Faculteit der Sociale Wetenschappen