The effects of data quantity on performance of temporal response function analyses of natural speech processing.
The effects of data quantity on performance of temporal response function analyses of natural speech processing.
复制标题
数据数量对自然语音处理的时间响应功能分析的性能的影响。
DOI：
10.3389/fnins.2022.963629
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发表时间：
2022
影响因子：
4.3
通讯作者：
Wojtczak, Magdalena
中科院分区：
文献类型：
--
作者：
Mesik, Juraj;Wojtczak, Magdalena
关键词：
近年来，时间响应函数（TRF）分析的神经活动记录引起的连续自然刺激已成为越来越受欢迎的表征响应特性内的听觉层次。然而，尽管扶轮基金会的使用率增加，但这些工具的教育资源却相对较少。在这里，我们使用一个双说话者连续语音范例来演示实验设计的一个关键参数，获得的数据量，影响TRF分析适合于个人数据（特定主题的分析），或组数据（通用分析）。我们表明，虽然模型预测精度随着数据量单调增加，但实现显着预测精度所需的数据量可能会根据拟合模型是否包含密集（例如，声学包络）或稀疏地（例如，词汇（词法）间隔特征，特别是当分析的目标是捕捉由特定特征唯一解释的神经反应的方面时。此外，我们证明了通用模型可以在少量测试数据（2-8分钟）上表现出高性能，如果它们在足够大的数据集上训练的话。因此，它们对于记录时间有限的临床和多任务研究设计可能特别有用。最后，我们表明，用于拟合TRF模型的正则化过程可以与用于拟合模型的数据量相互作用，较大的训练量会导致系统性较大的TRF振幅。总之，在这项工作中的演示应帮助TRF分析的新用户，并结合其他工具，如试点和功率分析，可以作为一个详细的参考选择收购时间在未来的研究。
In recent years, temporal response function (TRF) analyses of neural activity recordings evoked by continuous naturalistic stimuli have become increasingly popular for characterizing response properties within the auditory hierarchy. However, despite this rise in TRF usage, relatively few educational resources for these tools exist. Here we use a dual-talker continuous speech paradigm to demonstrate how a key parameter of experimental design, the quantity of acquired data, influences TRF analyses fit to either individual data (subject-specific analyses), or group data (generic analyses). We show that although model prediction accuracy increases monotonically with data quantity, the amount of data required to achieve significant prediction accuracies can vary substantially based on whether the fitted model contains densely (e.g., acoustic envelope) or sparsely (e.g., lexical surprisal) spaced features, especially when the goal of the analyses is to capture the aspect of neural responses uniquely explained by specific features. Moreover, we demonstrate that generic models can exhibit high performance on small amounts of test data (2–8 min), if they are trained on a sufficiently large data set. As such, they may be particularly useful for clinical and multi-task study designs with limited recording time. Finally, we show that the regularization procedure used in fitting TRF models can interact with the quantity of data used to fit the models, with larger training quantities resulting in systematically larger TRF amplitudes. Together, demonstrations in this work should aid new users of TRF analyses, and in combination with other tools, such as piloting and power analyses, may serve as a detailed reference for choosing acquisition duration in future studies.
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