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

TitleDynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals.
Publication TypeJournal Article
Year of Publication2022
AuthorsLecaignard, F, Bertrand, R, Brunner, P, Caclin, A, Schalk, G, Mattout, J
JournalFront Hum Neurosci
Date Published02/2022

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.

Alternate JournalFront Hum Neurosci
PubMed ID35221952
PubMed Central IDPMC8866734
Grant ListU24 NS109103 / NS / NINDS NIH HHS / United States
U01 NS108916 / NS / NINDS NIH HHS / United States
R01 EB026439 / EB / NIBIB NIH HHS / United States
P50 MH109429 / MH / NIMH NIH HHS / United States
P41 EB018783 / EB / NIBIB NIH HHS / United States

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