Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals
Year: 2022
Type: article
Abstract: 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.
Source: Frontiers in Human Neuroscience
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,950
Funders: Agence Nationale de la Recherche, Université de Lyon, Centre Lyonnais d'Acoustique, Université de Lyon, National Institutes of Health, U.S. Army, Institut National de Physique Nucléaire et de Physique des Particules, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, NIH Blueprint for Neuroscience Research, Army Research Office
Awards: G1291134351, G169068394, G2356978587, G2455160852, G261106309, G2722754911, PROJET AVENIR LYON SAINT-ETIENNE, G3117456484, G3219833070, G3259502590, G3424441068, G3498750383, G3656529027, G3739333157, G459367103, G4866930200, G5245564242, G5549936139, G5707930127, G6163133600, Construction, Fonction Cognitive et Réhabilitation du Cerveau, G7240651115, G7317382981, Dynamics and Causal Functions of Large-Scale Cortical and Subcortical Networks, G7376787254, BCI2000+: A Software Platform for Adaptive Neurotechnologies, G7452299184, G7835500200, BCI2000: Software Resource for Adaptive Neurotechnology Research, Lyon Acoustics Centre, Center for Adaptive Neurotechnologies, G9000066060, G9000096072, Neurobiology and Cognitive Role of Slow Brain Network Fluctuations, G9575639
OpenAlex ID: W4211240320