Literature DB >> 29683495

Now you hear it: a predictive coding model for understanding rhythmic incongruity.

Peter Vuust1,2, Martin J Dietz3, Maria Witek1,2, Morten L Kringelbach1,2,4.   

Abstract

Rhythmic incongruity in the form of syncopation is a prominent feature of many contemporary musical styles. Syncopations afford incongruity between rhythmic patterns and the meter, giving rise to mental models of differently accented isochronous beats. Syncopations occur either in isolation or as part of rhythmic patterns, so-called grooves. On the basis of the predictive coding framework, we discuss how brain processing of rhythm can be seen as a special case of predictive coding. We present a simple, yet powerful model for how the brain processes rhythmic incongruity: the model for predictive coding of rhythmic incongruity. Our model proposes that a given rhythm's syncopation and its metrical uncertainty (precision) is at the heart of how the brain models rhythm and meter based on priors, predictions, and prediction error. Our minimal model can explain prominent features of brain processing of syncopation: why isolated syncopations lead to stronger prediction error in the brains of musicians, as evidenced by larger event-related potentials to rhythmic incongruity, and why we all experience a stronger urge to move to grooves with a medium level of syncopation compared with low and high levels of syncopation.
© 2018 New York Academy of Sciences.

Entities:  

Keywords:  brain; music; predictive coding; rhythm

Year:  2018        PMID: 29683495     DOI: 10.1111/nyas.13622

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  22 in total

Review 1.  The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior.

Authors:  Paul B Badcock; Karl J Friston; Maxwell J D Ramstead; Annemie Ploeger; Jakob Hohwy
Journal:  Cogn Affect Behav Neurosci       Date:  2019-12       Impact factor: 3.282

2.  Modeling enculturated bias in entrainment to rhythmic patterns.

Authors:  Thomas Kaplan; Jonathan Cannon; Lorenzo Jamone; Marcus Pearce
Journal:  PLoS Comput Biol       Date:  2022-09-29       Impact factor: 4.779

Review 3.  Music in the brain.

Authors:  Peter Vuust; Ole A Heggli; Karl J Friston; Morten L Kringelbach
Journal:  Nat Rev Neurosci       Date:  2022-03-29       Impact factor: 38.755

Review 4.  Time Perception for Musical Rhythms: Sensorimotor Perspectives on Entrainment, Simulation, and Prediction.

Authors:  Jessica M Ross; Ramesh Balasubramaniam
Journal:  Front Integr Neurosci       Date:  2022-07-05

5.  Pupil drift rate indexes groove ratings.

Authors:  Connor Spiech; George Sioros; Tor Endestad; Anne Danielsen; Bruno Laeng
Journal:  Sci Rep       Date:  2022-07-08       Impact factor: 4.996

6.  From random to regular: neural constraints on the emergence of isochronous rhythm during cultural transmission.

Authors:  Massimo Lumaca; Niels Trusbak Haumann; Peter Vuust; Elvira Brattico; Giosuè Baggio
Journal:  Soc Cogn Affect Neurosci       Date:  2018-09-05       Impact factor: 3.436

Review 7.  How Beat Perception Co-opts Motor Neurophysiology.

Authors:  Jonathan J Cannon; Aniruddh D Patel
Journal:  Trends Cogn Sci       Date:  2020-12-24       Impact factor: 24.482

8.  The Power of Smiling: The Adult Brain Networks Underlying Learned Infant Emotionality.

Authors:  Eloise A Stark; Joana Cabral; Madelon M E Riem; Marinus H Van IJzendoorn; Alan Stein; Morten L Kringelbach
Journal:  Cereb Cortex       Date:  2020-04-14       Impact factor: 5.357

Review 9.  Brain Connectivity Networks and the Aesthetic Experience of Music.

Authors:  Mark Reybrouck; Peter Vuust; Elvira Brattico
Journal:  Brain Sci       Date:  2018-06-12

10.  Expectancy-based rhythmic entrainment as continuous Bayesian inference.

Authors:  Jonathan Cannon
Journal:  PLoS Comput Biol       Date:  2021-06-09       Impact factor: 4.475

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