Lecaignard Françoise, Bertrand Raphaëlle, Brunner Peter, Caclin Anne, Schalk Gerwin, Mattout Jérémie
Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France.
University Lyon 1, Lyon, France.
Front Hum Neurosci. 2022 Feb 10;15:794654. doi: 10.3389/fnhum.2021.794654. eCollection 2021.
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.
最近的知觉计算模型将听觉失匹配负波反应概念化为（贝叶斯）学习过程的特征，这与将失匹配负波（MMN）视为预测误差信号的有影响力的观点一致。新颖的MMN实验范式强调了操纵声音序列的规律性和可预测性所产生的神经生理效应。这就提出了学习过程本身的情境适应性问题，从计算角度来看，这涉及到增益调制（或精度加权）预测误差的机制。在这项使用皮质电图（ECoG）信号的研究中，我们操纵了失匹配声音序列的可预测性，目的有两个：（i）揭示皮层反应逐次试验变化背后的计算过程。基于平均诱发反应的方法通常忽略试验之间的波动，而这些波动应反映所涉及的学习。我们使用一般线性模型（GLM）和贝叶斯模型简化（BMR）来评估概率假设下实验操纵和学习机制的各自贡献。（ii）使用同步脑电图 - 脑磁图记录来验证和扩展先前关于可预测性变化影响的研究结果。我们的逐次试验分析仅揭示了少数刺激响应传感器，但所测量的效应在时间和空间上在受试者之间似乎是一致的。在时间上，它们出现在MMN的典型潜伏期（刺激后100至250毫秒之间）。在空间上，我们发现在更靠前的颞叶位置的与时间无关的效应和更靠后的位置的与时间相关（学习）的效应之间存在分离。然而，我们没有观察到我们对可预测性调制的操纵对上述学习过程有任何清晰可靠的影响。总体而言，这些发现清楚地证明了逐次试验建模在揭示知觉学习过程及其神经生理对应物方面的潜力。
