The effect of familiarity on neural tracking of music stimuli is modulated by mind wandering

Joan Belo; Maureen Clerc; Daniele Schön; Joan Belo; Maureen Clerc; Daniele Schön

doi:10.3934/Neuroscience.2023025

AIMS Neuroscience

2023, Volume 10, Issue 4: 319-331. doi: 10.3934/Neuroscience.2023025

Previous Article Next Article

Brief report Topical Sections

The effect of familiarity on neural tracking of music stimuli is modulated by mind wandering

1.
Athena Project Team, INRIA, Université Côte d'Azur, Nice, France
2.
Aix Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
3.
Institute for Language, Communication, and the Brain, Aix-en-Provence, France

Received: 14 July 2023 Revised: 29 October 2023 Accepted: 06 November 2023 Published: 10 November 2023

One way to investigate the cortical tracking of continuous auditory stimuli is to use the stimulus reconstruction approach. However, the cognitive and behavioral factors impacting this cortical representation remain largely overlooked. Two possible candidates are familiarity with the stimulus and the ability to resist internal distractions. To explore the possible impacts of these two factors on the cortical representation of natural music stimuli, forty-one participants listened to monodic natural music stimuli while we recorded their neural activity. Using the stimulus reconstruction approach and linear mixed models, we found that familiarity positively impacted the reconstruction accuracy of music stimuli and that this effect of familiarity was modulated by mind wandering.
- auditory attention detection,
- stimulus reconstruction,
- EEG,
- familiarity,
- mind wandering,
- auditory attention
Citation: Joan Belo, Maureen Clerc, Daniele Schön. The effect of familiarity on neural tracking of music stimuli is modulated by mind wandering[J]. AIMS Neuroscience, 2023, 10(4): 319-331. doi: 10.3934/Neuroscience.2023025

Related Papers:

Abstract

One way to investigate the cortical tracking of continuous auditory stimuli is to use the stimulus reconstruction approach. However, the cognitive and behavioral factors impacting this cortical representation remain largely overlooked. Two possible candidates are familiarity with the stimulus and the ability to resist internal distractions. To explore the possible impacts of these two factors on the cortical representation of natural music stimuli, forty-one participants listened to monodic natural music stimuli while we recorded their neural activity. Using the stimulus reconstruction approach and linear mixed models, we found that familiarity positively impacted the reconstruction accuracy of music stimuli and that this effect of familiarity was modulated by mind wandering.

Abbreviations

amplitude envelope

stimulus reconstruction

RMS

root mean square

EEG

electroencephalography

ICA

independent component analysis

SBR

stepwise backward regression

Acknowledgments

J.B. is funded by Oticon Medical (research agreement n°14294-CRI04) and Region Provence-Alpes Côte-d'Azur. D.S. was supported by APA foundation (RD-2016-9), ANR-16-CE28-0012-01 (RALP), ANR-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and the Excellence Initiative of Aix-Marseille University (A*MIDEX).

Conflict of interest

The authors declare no conflict of interest.

References

[1]	Chandrasekaran B, Kraus N (2010) The scalp-recorded brainstem response to speech: Neural origins and plasticity. Psychophysiology 47: 236-246. https://doi.org/10.1111/j.1469-8986.2009.00928.x
[2]	Giraud AL, Poeppel D (2012) Cortical oscillations and speech processing: Emerging computational principles and operations. Nat Neurosci 15: 511-517. https://doi.org/10.1038/nn.3063
[3]	Mesgarani N, David SV, Fritz JB, et al. (2009) Influence of context and behavior on stimulus reconstruction from neural activity in primary auditory cortex. J Neurophysiol 102: 3329-3339. https://doi.org/10.1152/jn.91128.2008
[4]	Nourski KV, Reale RA, Oya H, et al. (2009) Temporal envelope of time-compressed speech represented in the human auditory cortex. J Neurosci 29: 15564-15574. https://doi.org/10.1523/JNEUROSCI.3065-09.2009
[5]	Pasley BN, David SV, Mesgarani N, et al. (2012) Reconstructing speech from human auditory cortex. PLoS Biol 10. https://doi.org/10.1371/journal.pbio.1001251
[6]	Kubanek J, Brunner P, Gunduz A, et al. (2013) The Tracking of Speech Envelope in the Human Cortex. PLoS ONE 8. https://doi.org/10.1371/journal.pone.0053398
[7]	Doelling KB, Poeppel D (2015) Cortical entrainment to music and its modulation by expertise. P Natl Acad Sci USA 112: E6233-E6242. https://doi.org/10.1073/pnas.1508431112
[8]	Baltzell LS, Srinivasan R, Richards VM (2017) The effect of prior knowledge and intelligibility on the cortical entrainment response to speech. J Neurophysiol 118: 3144-3151. https://doi.org/10.1152/jn.00023.2017
[9]	Di Liberto GM, Pelofi C, Shamma S, et al. (2020) Musical expertise enhances the cortical tracking of the acoustic envelope during naturalistic music listening. Acoust Sci Technol 41: 361-364. https://doi.org/10.1250/ast.41.361
[10]	Harding EE, Sammler D, Henry MJ, et al. (2019) Cortical tracking of rhythm in music and speech. NeuroImage 185: 96-101. https://doi.org/10.1016/j.neuroimage.2018.10.037
[11]	Belo J, Clerc M, Schön D (2022) Attentional inhibition ability predicts neural representation during challenging auditory streaming. BioRxiv Preprint .
[12]	O'Sullivan JA, Power AJ, Mesgarani N, et al. (2015) Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cereb Cortex 25: 1697-1706. https://doi.org/10.1093/cercor/bht355
[13]	Zion Golumbic EM, Ding N, Bickel S, et al. (2013) Mechanisms underlying selective neuronal tracking of attended speech at a “cocktail party.”. Neuron 77: 980-991. https://doi.org/10.1016/j.neuron.2012.12.037
[14]	Connine CM, Mullennix J, Shernoff E, et al. (1990) Word Familiarity and Frequency in Visual and Auditory Word Recognition. J Exp Psychol Learn 16: 1084-1096. https://doi.org/10.1037/0278-7393.16.6.1084
[15]	Skuk VG, Schweinberger SR (2013) Gender differences in familiar voice identification. Hearing Res 296: 131-140. https://doi.org/10.1016/j.heares.2012.11.004
[16]	Blank H, Wieland N, von Kriegstein K (2014) Person recognition and the brain: Merging evidence from patients and healthy individuals. Neurosci Biobehav Rev 47: 717-734. https://doi.org/10.1016/j.neubiorev.2014.10.022
[17]	Vanden Bosch der Nederlanden CM, Joanisse MF, Grahn JA, et al. (2022) Familiarity modulates neural tracking of sung and spoken utterances. NeuroImage 252: 119049. https://doi.org/10.1016/j.neuroimage.2022.119049
[18]	Madsen J, Margulis EH, Simchy-Gross R, et al. (2019) Music synchronizes brainwaves across listeners with strong effects of repetition, familiarity and training. Sci Rep 9: 1-8. https://doi.org/10.1038/s41598-019-40254-w
[19]	Weineck K, Wen OX, Henry MJ (2022) Neural entrainment is strongest to the spectral flux of slow music and depends on familiarity and beat salience. Elife 11: e75515. https://doi.org/10.7554/eLife.75515
[20]	Kumagai Y, Matsui R, Tanaka T (2018) Music familiarity affects EEG entrainment when little attention is paid. Front Hum Neurosci 12: 1-11. https://doi.org/10.3389/fnhum.2018.00444
[21]	Seli P, Kane MJ, Smallwood J, et al. (2018) Mind-Wandering as a Natural Kind: A Family-Resemblances View. Trends Cogn Sci 22: 479-490. https://doi.org/10.1016/j.tics.2018.03.010
[22]	Barron E, Riby LM, Greer J, et al. (2011) Absorbed in thought: The effect of mind wandering on the processing of relevant and irrelevant events. Psychol Sci 22: 596-601. https://doi.org/10.1177/0956797611404083
[23]	Kam JWY, Xu J, Handy TC (2014) I don't feel your pain (as much): The desensitizing effect of mind wandering on the perception of others' discomfort. Cogn Affect Behav Ne 14: 286-296. https://doi.org/10.3758/s13415-013-0197-z
[24]	Patou F, Py H, Backus B, et al. Oticon Medical's ecosystem of lab- and field investigational platforms for behavioral and cognitive research in CI Peripheral neural interfacing Behavior (2019).
[25]	Gramfort A, Luessi M, Larson E, et al. (2013) MEG and EEG data analysis with MNE-Python. Front Neurosci 7: 1-13. https://doi.org/10.3389/fnins.2013.00267
[26]	Crosse MJ, Zuk NJ, Di Liberto GM, et al. (2021) Linear Modeling of Neurophysiological Responses to Naturalistic Stimuli: Methodological Considerations for Applied Research. PsyArXiv . https://doi.org/10.31234/osf.io/jbz2w
[27]	Greenlaw KM, Puschmann S, Coffey EBJ (2020) Decoding of Envelope vs. Fundamental Frequency During Complex Auditory Stream Segregation. Neurobiol Language 1: 268-287. https://doi.org/10.1162/nol_a_00013
[28]	Biesmans W, Vanthornhout J, Wouters J, et al. Comparison of speech envelope extraction methods for EEG-based auditory attention detection in a cocktail party scenario (2015): 5155-5158. https://doi.org/10.1109/EMBC.2015.7319552
[29]	. Available from: https://github.com/mcdermottLab/pycochleagram
[30]	O'Sullivan JA, Power AJ, Mesgarani N, et al. (2015) Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cereb Cortex 25: 1697-1706. https://doi.org/10.1093/cercor/bht355
[31]	Crosse MJ, Di Liberto GM, Bednar A, et al. (2016) The multivariate temporal response function (mTRF) toolbox: A MATLAB toolbox for relating neural signals to continuous stimuli. Front Hum Neurosci 10: 1-14. https://doi.org/10.3389/fnhum.2016.00604
[32]	Lalor EC, Power AJ, Reilly RB, et al. (2009) Resolving precise temporal processing properties of the auditory system using continuous stimuli. J Neurophysiol 102: 349-359. https://doi.org/10.1152/jn.90896.2008
[33]	R Core Team.R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2022) . Available from: https://www.R-project.org/
[34]	Bates D, Mächler M, Bolker B, et al. (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67: 1-48. https://doi.org/10.18637/jss.v067.i01
[35]	Kuznetsova A, Brockhoff PB, Christensen RHB (2014) Package ‘lmerTest’. Test for random and fixed effects for linear mixed effect models (lmer objects oflme4 package). R package Version 2 . Available from: https://github.com/runehaubo/lmerTestR
[36]	Millard SP, EnvStats-An R (2013) An R package for environmental statistics. New York: Springer-Verlag.
[37]	Huron D (2006) Sweet anticipation: Music and the psychology of expectation (pp. xii, 462).The MIT Press.
[38]	Di Liberto GM, Pelofi C, Bianco R, et al. (2020) Cortical encoding of melodic expectations in human temporal cortex. ELife 9: 1-26. https://doi.org/10.7554/eLife.51784
[39]	Kern P, Heilbron M, De Lange FP, et al. (2022) Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience. BioRxiv Preprint .
[40]	Bendixen A (2014) Predictability effects in auditory scene analysis: A review. Front Neurosci 8: 1-16. https://doi.org/10.3389/fnins.2014.00060
[41]	Verschueren E, Vanthornhout J, Francart T (2020) The effect of stimulus choice on an EEG-Based objective measure of speech intelligibility. Ear Hearing 41: 1586-1597. https://doi.org/10.1097/AUD.0000000000000875
[42]	Soni S, Tata MS (2021) Brain electrical dynamics in speech segmentation depends upon prior experience with the language. Brain Lang 219: 104967. https://doi.org/10.1016/j.bandl.2021.104967
[43]	Smallwood J (2013) Distinguishing how from why the mind wanders: A process-occurrence framework for self-generated mental activity. Psychol Bull 139: 519-535. https://doi.org/10.1037/a0030010
[44]	Kane MJ, Smeekens BA, Meier ME, et al. (2021) Testing the construct validity of competing measurement approaches to probed mind-wandering reports. Behav Res Methods 53: 2743. https://doi.org/10.3758/s13428-021-01685-4
[45]	Hausfeld L, Riecke L, Valente G, et al. (2018) Cortical tracking of multiple streams outside the focus of attention in naturalistic auditory scenes. NeuroImage 181: 617-626. https://doi.org/10.1016/j.neuroimage.2018.07.052

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)