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Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiologi...

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Autores principales: Lecaignard, Françoise, Bertrand, Raphaëlle, Brunner, Peter, Caclin, Anne, Schalk, Gerwin, Mattout, Jérémie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866734/
https://www.ncbi.nlm.nih.gov/pubmed/35221952
http://dx.doi.org/10.3389/fnhum.2021.794654
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author Lecaignard, Françoise
Bertrand, Raphaëlle
Brunner, Peter
Caclin, Anne
Schalk, Gerwin
Mattout, Jérémie
author_facet Lecaignard, Françoise
Bertrand, Raphaëlle
Brunner, Peter
Caclin, Anne
Schalk, Gerwin
Mattout, Jérémie
author_sort Lecaignard, Françoise
collection PubMed
description Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.
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spelling pubmed-88667342022-02-25 Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals Lecaignard, Françoise Bertrand, Raphaëlle Brunner, Peter Caclin, Anne Schalk, Gerwin Mattout, Jérémie Front Hum Neurosci Neuroscience Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts. Frontiers Media S.A. 2022-02-10 /pmc/articles/PMC8866734/ /pubmed/35221952 http://dx.doi.org/10.3389/fnhum.2021.794654 Text en Copyright © 2022 Lecaignard, Bertrand, Brunner, Caclin, Schalk and Mattout. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lecaignard, Françoise
Bertrand, Raphaëlle
Brunner, Peter
Caclin, Anne
Schalk, Gerwin
Mattout, Jérémie
Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
title Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
title_full Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
title_fullStr Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
title_full_unstemmed Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
title_short Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
title_sort dynamics of oddball sound processing: trial-by-trial modeling of ecog signals
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866734/
https://www.ncbi.nlm.nih.gov/pubmed/35221952
http://dx.doi.org/10.3389/fnhum.2021.794654
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