Data_Sheet_1_Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals.pdf

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.

近期，感知的计算模型将听觉奇数球反应视为（贝叶斯）学习过程的标志，这与将失配负波（MMN）视为预测误差信号的具有影响力的观点相一致。新颖的MMN实验范式强调了在声音序列中操纵规律性和可预测性的神经生理效应。这引发了关于学习过程本身情境适应性的问题，从计算的角度来看，这涉及增益调制（或精度加权）预测误差的机制。在本研究中，我们利用电皮质电图（ECoG）信号，通过两个目标操纵奇数球声音序列的可预测性：（一）揭示皮层反应逐次变化的计算过程。通常被基于平均诱发电位的方法忽视的试验间波动应反映所涉及的学习过程。我们使用广义线性模型（GLM）和贝叶斯模型降维（BMR）在概率假设下评估实验操作和学习机制各自的贡献。（二）通过同时进行脑电图（EEG）-脑磁图（MEG）记录，验证并扩展关于可预测性变化影响的先前发现。我们的逐次试验分析仅发现少数刺激响应传感器，但测量的效果似乎在时间和空间上对受试者是一致的。在时间上，它们发生在MMN的典型潜伏期（刺激后100至250毫秒）。在空间上，我们发现在更前额叶的时间不变效应和更后叶的时间依赖（学习）效应之间存在分离。然而，我们未能观察到我们对可预测性调节操作的明显且可靠的影响。总体而言，这些发现清楚地证明了逐次建模的潜力，以揭示感知学习过程及其神经生理学对应物。