McMaster University, Hamilton, Canada.
Stanford University, Stanford, United States.
Elife. 2023 Nov 29;12:e85012. doi: 10.7554/eLife.85012.
Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using ) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.
尽管人类的经验在时间中不断展开，但它并不是严格线性的；相反，它涉及到级联过程，构建分层认知结构。例如，在语音感知过程中，人类将连续变化的声信号转化为音素、单词和意义，而这些层次都具有独特但相互依存的时间结构。使用时间滞后回归（time-lagged regression）的方法最近成为一种有前途的工具，可以分离与这种复杂感知模型相关的电生理脑反应。在这里，我们引入了 Eelbrain Python 工具包，它使得这种分析变得简单易用。我们以连续语音为例，使用免费提供的有声读物听书的 EEG 数据集演示了其用法。一个配套的 GitHub 存储库提供了从原始数据到组级统计的分析的完整源代码。更一般地，我们提倡一种假设驱动的方法，其中实验者指定一个连续时间表示的层次结构，假设这些表示有助于脑反应，并将这些表示作为电生理信号的预测变量。这类似于多元回归问题，但增加了时间维度。TRF 分析通过估计多变量 TRF（mTRF），将脑信号分解为与不同预测变量相关的不同响应，从而量化每个预测变量对脑反应的影响随时间（滞后）的变化。这允许针对预测变量提出两个问题：（1）是否存在与该预测变量对应的显著神经表示？如果有，（2）与之相关的神经反应的时间特征是什么？因此，可以系统地组合和评估不同的预测变量，以联合模型化多个层次的神经处理。我们讨论了这种方法的应用，包括通过具有适当链接假设的计算模型将不同认知水平的算法/表示理论与脑反应联系起来的潜力。
