Department of Music, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt am Main, Germany.
Department of Psychology, Neuroscience & Behavior, McMaster University, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada.
Behav Res Methods. 2024 Mar;56(3):1376-1412. doi: 10.3758/s13428-023-02098-1. Epub 2023 Jun 22.
The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.
眼睛的瞳孔为认知科学家提供了丰富的信息来源，因为它可以反映出各种身体状态（例如，觉醒、疲劳）和认知过程（例如，注意力、决策）。随着瞳孔测量技术变得更加容易获取和流行，研究人员已经提出了各种分析瞳孔数据的技术。在这里，我们重点关注基于时间序列的信号到信号的方法，这些方法可以将瞳孔大小随时间的动态变化与刺激时间序列、连续行为结果测量或其他参与者的瞳孔轨迹的动态变化联系起来。我们首先介绍瞳孔测量技术、其神经基础，以及瞳孔测量值与其他眼球运动行为（例如，眨眼、扫视）之间的关系，以强调理解正在测量的内容以及可以从瞳孔活动变化中推断出什么的重要性。接下来，我们讨论可能的预处理步骤以及在哪些情况下需要这些步骤。最后，我们转向信号到信号的分析技术，包括基于回归的方法、动态时间扭曲、相位聚类、去趋势波动分析和递归量化分析。概述了这些技术的假设以及每个技术可以解决的科学问题的示例，并引用了关键论文和软件包。此外，我们还提供了一个详细的代码教程，该教程逐步介绍了本文中的关键示例和图。最终，我们认为从瞳孔测量中获得的见解受到所使用的分析技术的限制，并且信号到信号的方法通过考虑瞳孔信号与其他感兴趣信号之间未被充分研究的光谱时间关系，提供了一种生成新科学见解的手段。
