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

被引:3
|
作者
Lecaignard, Francoise [1 ,2 ]
Bertrand, Raphaelle [1 ,2 ]
Brunner, Peter [3 ,4 ,5 ]
Caclin, Anne [1 ,2 ]
Schalk, Gerwin [5 ]
Mattout, Jeremie [1 ,2 ]
机构
[1] CNRS, Lyon Neurosci Res Ctr, INSERM, CRNL,U1028,UMR 5292, Lyon, France
[2] Univ Lyon 1, Lyon, France
[3] Washington Univ, Sch Med, Dept Neurosurg, St Louis, MO USA
[4] Albany Med Coll, Dept Neurol, Albany, NY 12208 USA
[5] Natl Ctr Adapt Neurotechnol, Albany, NY USA
来源
关键词
single-trial analysis; predictive coding; mismatch negativity; Bayesian learning; general linear model; Bayesian model reduction; MISMATCH NEGATIVITY; PREDICTION ERRORS; BRAIN; REPETITION; ATTENTION; RESPONSES; CORTEX; MMN;
D O I
10.3389/fnhum.2021.794654
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页数:23
相关论文
共 36 条
  • [1] Trial-by-trial modeling of electrophysiological signals during inverse Bayesian inference
    Antonio Kolossa
    Bruno Kopp
    Tim Fingscheidt
    [J]. BMC Neuroscience, 15 (Suppl 1)
  • [2] Trial-by-trial dynamics: a window in time
    Bengson, Jesse J.
    Mazaheri, Ali
    [J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2010, 4
  • [3] Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks
    Modirshanechi, Alireza
    Kiani, Mohammad Mahdi
    Aghajan, Hamid
    [J]. NEUROIMAGE, 2019, 196 : 302 - 317
  • [4] Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal
    Packheiser, Julian
    Donoso, Jose R.
    Cheng, Sen
    Guentuerkuen, Onur
    Pusch, Roland
    [J]. PROGRESS IN NEUROBIOLOGY, 2021, 197
  • [5] The effect of powered prosthesis control signals on trial-by-trial adaptation to visual perturbations
    Johnson, Reva E.
    Kording, Konrad P.
    Hargrove, Levi J.
    Sensinger, Jonathon W.
    [J]. 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 3512 - 3515
  • [6] Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics
    Fine, Michael S.
    Thoroughman, Kurt A.
    [J]. JOURNAL OF NEUROPHYSIOLOGY, 2007, 98 (03) : 1392 - 1404
  • [7] Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling
    Zhao, Yukai
    Liu, Jiajuan
    Dosher, Barbara Anne
    Lu, Zhong-Lin
    [J]. JOURNAL OF COGNITIVE ENHANCEMENT, 2024,
  • [8] Models of human visual attention should consider trial-by-trial variability in preparatory neural signals
    Sylvester, Chad M.
    d'Avossa, Giovanni
    Corbetta, Maurizio
    [J]. NEURAL NETWORKS, 2006, 19 (09) : 1447 - 1449
  • [9] Trial-by-trial identification of categorization strategy using iterative decision-bound modeling
    Helie, Sebastien
    Turner, Benjamin O.
    Crossley, Matthew J.
    Ell, Shawn W.
    Ashby, F. Gregory
    [J]. BEHAVIOR RESEARCH METHODS, 2017, 49 (03) : 1146 - 1162
  • [10] Trial-by-trial identification of categorization strategy using iterative decision-bound modeling
    Sébastien Hélie
    Benjamin O. Turner
    Matthew J. Crossley
    Shawn W. Ell
    F. Gregory Ashby
    [J]. Behavior Research Methods, 2017, 49 : 1146 - 1162