Department of Developmental and Social Psychology, University of Padua, Padua, Italy.
Department of Environmental Sciences Informatics and Statistics, Ca' Foscari University, Venice, Italy.
Behav Res Methods. 2024 Apr;56(4):3346-3365. doi: 10.3758/s13428-023-02172-8. Epub 2023 Jul 13.
Pupillometry has been widely implemented to investigate cognitive functioning since infancy. Like most psychophysiological and behavioral measures, it implies hierarchical levels of arbitrariness in preprocessing before statistical data analysis. By means of an illustrative example, we checked the robustness of the results of a familiarization procedure that compared the impact of audiovisual and visual stimuli in 12-month-olds. We adopted a multiverse approach to pupillometry data analysis to explore the role of (1) the preprocessing phase, that is, handling of extreme values, selection of the areas of interest, management of blinks, baseline correction, participant inclusion/exclusion and (2) the modeling structure, that is, the incorporation of smoothers, fixed and random effects structure, in guiding the parameter estimation. The multiverse of analyses shows how the preprocessing steps influenced the regression results, and when visual stimuli plausibly predicted an increase of resource allocation compared with audiovisual stimuli. Importantly, smoothing time in statistical models increased the plausibility of the results compared to those nested models that do not weigh the impact of time. Finally, we share theoretical and methodological tools to move the first steps into (rather than being afraid of) the inherent uncertainty of infant pupillometry.
瞳孔测量技术自婴儿期以来就被广泛应用于研究认知功能。像大多数生理心理和行为测量方法一样，它在进行统计数据分析之前的预处理中具有一定程度的任意性。通过一个说明性的例子，我们检查了一个熟悉过程结果的稳健性，该过程比较了 12 个月大的婴儿对视听和视觉刺激的影响。我们采用多元宇宙方法对瞳孔测量数据进行分析，以探讨（1）预处理阶段的作用，即处理极值、选择感兴趣区域、处理眨眼、基线校正、参与者纳入/排除，以及（2）建模结构的作用，即纳入平滑器、固定和随机效应结构，以指导参数估计。多元宇宙分析表明，预处理步骤如何影响回归结果，以及当视觉刺激与视听刺激相比，明显预示着资源分配增加时。重要的是，与不考虑时间影响的嵌套模型相比，在统计模型中平滑时间增加了结果的可信度。最后，我们分享了理论和方法工具，以推动婴儿瞳孔测量技术的第一步（而不是害怕）进入固有不确定性。
