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Manon Grube

Weighting of neural prediction error by rhythmic complexity: A predictive coding account using Mismatch Negativity

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Weighting of neural prediction error by rhythmic complexity : A predictive coding account using Mismatch Negativity. / Lumaca, Massimo; Haumann, Niels Trusbak; Brattico, Elvira; Grube, Manon; Vuust, Peter.

In: European Journal of Neuroscience, Vol. 49, No. 12, 06.2019, p. 1597-1609.

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@article{d0bbd422208b464297a27731da1d0daf,
title = "Weighting of neural prediction error by rhythmic complexity: A predictive coding account using Mismatch Negativity",
abstract = "The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of five tones (four inter-onset intervals) and of a given level of entropy: zero (isochronous), medium entropy (two distinct interval durations), or high entropy (four distinct interval durations). Occasionally, the fourth tone was moved forward in time that is it occurred 100 ms (small deviation) or 300 ms early (large deviation). According to the predictive coding framework, high-entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding smaller prediction errors for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine-grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.",
keywords = "EEG, MMN, predictive timing, rhythmic complexity, Shannon Entropy, MECHANISM, predictive coding, DISTINCTIVENESS, EVENT-RELATED POTENTIALS, MASS UNIVARIATE ANALYSIS, REPETITION SUPPRESSION, Shannon entropy, SOUND, COMPONENT, SELECTIVE-ATTENTION, VIOLATIONS, INVOLUNTARY ATTENTION",
author = "Massimo Lumaca and Haumann, {Niels Trusbak} and Elvira Brattico and Manon Grube and Peter Vuust",
year = "2019",
month = "6",
doi = "10.1111/ejn.14329",
language = "English",
volume = "49",
pages = "1597--1609",
journal = "European Journal of Neuroscience",
issn = "0953-816X",
publisher = "Wiley-Blackwell Publishing Ltd",
number = "12",

}

RIS

TY - JOUR

T1 - Weighting of neural prediction error by rhythmic complexity

T2 - A predictive coding account using Mismatch Negativity

AU - Lumaca, Massimo

AU - Haumann, Niels Trusbak

AU - Brattico, Elvira

AU - Grube, Manon

AU - Vuust, Peter

PY - 2019/6

Y1 - 2019/6

N2 - The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of five tones (four inter-onset intervals) and of a given level of entropy: zero (isochronous), medium entropy (two distinct interval durations), or high entropy (four distinct interval durations). Occasionally, the fourth tone was moved forward in time that is it occurred 100 ms (small deviation) or 300 ms early (large deviation). According to the predictive coding framework, high-entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding smaller prediction errors for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine-grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.

AB - The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of five tones (four inter-onset intervals) and of a given level of entropy: zero (isochronous), medium entropy (two distinct interval durations), or high entropy (four distinct interval durations). Occasionally, the fourth tone was moved forward in time that is it occurred 100 ms (small deviation) or 300 ms early (large deviation). According to the predictive coding framework, high-entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding smaller prediction errors for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine-grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.

KW - EEG

KW - MMN

KW - predictive timing

KW - rhythmic complexity

KW - Shannon Entropy

KW - MECHANISM

KW - predictive coding

KW - DISTINCTIVENESS

KW - EVENT-RELATED POTENTIALS

KW - MASS UNIVARIATE ANALYSIS

KW - REPETITION SUPPRESSION

KW - Shannon entropy

KW - SOUND

KW - COMPONENT

KW - SELECTIVE-ATTENTION

KW - VIOLATIONS

KW - INVOLUNTARY ATTENTION

U2 - 10.1111/ejn.14329

DO - 10.1111/ejn.14329

M3 - Journal article

VL - 49

SP - 1597

EP - 1609

JO - European Journal of Neuroscience

JF - European Journal of Neuroscience

SN - 0953-816X

IS - 12

ER -