Musical Expertise Is Associated with Improved Neural Statistical Learning in the Auditory Domain

Lerousseau, Jacques Pesnot; Schön, Daniele

doi:10.1093/cercor/bhab128

Cited by 16 publications

(13 citation statements)

References 94 publications

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“…The human auditory system exhibits a remarkable sensitivity to detect and learn statistical regularities in sound (Saffran et al, 1999; Skerritt-Davis & Elhilali, 2018). This capacity has been corroborated in statistical learning paradigms using behavioural (Barascud et al, 2016; Bianco et al, 2020), eye-tracking (Milne et al, 2021; Zhao et al, 2019), and neuroimaging techniques (Barascud et al, 2016; Moldwin et al, 2017; Pesnot Lerousseau & Schön, 2021). Furthermore, humans have extraordinary implicit memory for auditory patterns (Agres et al, 2018; Bianco et al, 2020).…”

Section: Discussionmentioning

confidence: 81%

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

Kern

Heilbron

Lange

et al. 2022

Preprint

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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 areas 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.

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Section: Discussionmentioning

confidence: 81%

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

Kern

Heilbron

Lange

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The human auditory system exhibits a remarkable sensitivity to detect and learn statistical regularities in sound ( Saffran et al, 1999 ; Skerritt-Davis and Elhilali, 2018 ). This capacity has been corroborated in statistical learning paradigms using behavioral ( Barascud et al, 2016 ; Bianco et al, 2020 ), eye-tracking ( Milne et al, 2021 ; Zhao et al, 2019 ), and neuroimaging techniques ( Barascud et al, 2016 ; Moldwin et al, 2017 ; Pesnot Lerousseau and Schön, 2021 ). Furthermore, humans have extraordinary implicit memory for auditory patterns ( Agres et al, 2018 ; Bianco et al, 2020 ).…”

Section: Discussionmentioning

confidence: 81%

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

Kern

Heilbron

Lange

et al. 2022

eLife

View full text Add to dashboard Cite

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.

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“…Relatedly, sound regularities appear to be extracted without a particular attentional focus (Batterink & Paller, 2019; Duncan & Theeuwes, 2020), but extensive experience with a specific auditory material may facilitate top‐down processing of the extracted regularities, especially with speech stimuli (Monte‐Ordoño & Toro, 2017; Sun et al, 2015). The specific functional role of experience in facilitating the deliberate processing of abstract regularities is not yet fully understood and has been linked with enhanced statistical learning abilities (Pesnot Lerousseau & Schön, 2021) or with the development of more efficient encoding strategies (Monte‐Ordoño & Toro, 2017). In our experiment, the enhanced theta ERS for the speech condition suggests that the presence of native phonemes and/or human‐like voices may have promoted a more efficient encoding strategy of the regularities.…”

Section: Discussionmentioning

confidence: 99%

Formant‐invariant voice and pitch representations are pre‐attentively formed from constantly varying speech and non‐speech stimuli

Dona

Scaltritti

Sulpizio

2022

Eur J of Neuroscience

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The present study investigated whether listeners can form abstract voice representations while ignoring constantly changing phonological information and if they can use the resulting information to facilitate voice change detection. Further, the study aimed at understanding whether the use of abstraction is restricted to the speech domain or can be deployed also in non‐speech contexts. We ran an electroencephalogram (EEG) experiment including one passive and one active oddball task, each featuring a speech and a rotated speech condition. In the speech condition, participants heard constantly changing vowels uttered by a male speaker (standard stimuli) which were infrequently replaced by vowels uttered by a female speaker with higher pitch (deviant stimuli). In the rotated speech condition, participants heard rotated vowels, in which the natural formant structure of speech was disrupted. In the passive task, the mismatch negativity was elicited after the presentation of the deviant voice in both conditions, indicating that listeners could successfully group together different stimuli into a formant‐invariant voice representation. In the active task, participants showed shorter reaction times (RTs), higher accuracy and a larger P3b in the speech condition with respect to the rotated speech condition. Results showed that whereas at a pre‐attentive level the cognitive system can track pitch regularities while presumably ignoring constantly changing formant information both in speech and in rotated speech, at an attentive level the use of such information is facilitated for speech. This facilitation was also testified by a stronger synchronisation in the theta band (4–7 Hz), potentially pointing towards differences in encoding/retrieval processes.

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Musical Expertise Is Associated with Improved Neural Statistical Learning in the Auditory Domain

Cited by 16 publications

References 94 publications

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

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

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

Formant‐invariant voice and pitch representations are pre‐attentively formed from constantly varying speech and non‐speech stimuli

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