McMurray Bob, Oleson Jacob, Kutlu Ethan
Dept. of Psychological and Brain Sciences, University of Iowa, United States.
Dept. of Biostatistics, University of Iowa, United States.
Brain Res. 2025 Oct 1;1864:149786. doi: 10.1016/j.brainres.2025.149786. Epub 2025 Jun 20.
In the last 30 years, the Visual World Paradigm has rapidly become a dominant experimental paradigm for understanding real-time language processing. Part of this derives from the rich visualizations of the millisecond-by-millisecond timecourse of language processing that it offers. While the field has converged on strong statistical approaches for this in experimental paradigms, a great deal of recent work has sought to apply the Visual World Paradigm in individual differences paradigms to examine factors such as development and aging, multilingualism and various types of communicative and cognitive disorders. However, these models are less useful when confronting issues that are at the core of individual differences work: of collinearity and co-morbidity among predictor variables. We review a new approach that may complement existing approaches by capitalizing on regression-based techniques that were developed to handle these issues. We argue that analysis should start by developing a quantitative "profile" of processing that can be captured in a small number of index variables that capture meaningful dimensions such as the degree of competition or the speed of settling on a target interpretation. These can then be used in more sophisticated regressions like hierarchical regression, commonality analysis and mediation analysis which can unpack shared and unique variance, and test multiple causal pathways. We illustrate this with tutorials based on our own work on hearing loss, development and language disorders that illustrate how these approaches can provide greater insight into individual differences.
在过去30年里，视觉世界范式迅速成为理解实时语言处理的主导实验范式。这部分得益于它所提供的对语言处理逐毫秒时间进程的丰富可视化呈现。虽然该领域在实验范式中已趋向于采用强大的统计方法，但最近大量工作试图将视觉世界范式应用于个体差异范式，以研究诸如发育与衰老、多语言能力以及各类交际和认知障碍等因素。然而，在面对个体差异研究核心问题，即预测变量之间的共线性和共病性时，这些模型的作用就没那么大了。我们回顾一种新方法，该方法通过利用为处理这些问题而开发的基于回归的技术，可能对现有方法起到补充作用。我们认为分析应从开发一种处理过程的定量“概况”开始，这种概况可以用少量指标变量来捕捉，这些变量能捕捉有意义的维度，比如竞争程度或确定目标解释的速度。然后，这些变量可用于更复杂的回归分析，如分层回归、共性分析和中介分析，这些分析可以剖析共享方差和独特方差，并检验多种因果路径。我们基于自己在听力损失、发育和语言障碍方面的工作用教程对此进行说明，展示这些方法如何能更深入地洞察个体差异。
