Anderson Andrew J, Mannion Damien J, Quiroga Maria Del Mar, Tescari Edoardo
Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, Victoria, Australia.
Melbourne Data Analytics Platform, The University of Melbourne, Parkville, Victoria, Australia.
J Neurophysiol. 2025 Feb 1;133(2):440-446. doi: 10.1152/jn.00396.2024. Epub 2024 Dec 24.
Analyzing reaction time distributions can provide insights into decision-making processes in the brain. The Linear Approach to Threshold with Ergodic Rate (LATER) model is arguably the simplest model for predicting reaction time distributions and can summarize distributions with as few as two free parameters. However, the coordinates for visualizing and fitting distributions with LATER ("reciprobit" space) are irregular, making the application of this simple model inaccessible to those without a programming background. Here we describe an open-source R package, LATERmodel, that allows for easy visualization of reaction time distributions, along with fitting of these with the LATER model. Using canonical data from the literature, we show that our tool replicates key features from previous LATER analysis tools, while also providing more robust fitting procedures and a more flexible method for fitting subpopulations of very rapid, early responses. Although all features of LATERmodel can be used directly in the statistical programming language R, key features are also available through a graphical user interface to allow researchers without programming background to apply the LATER model to their reaction time data. Analyzing reaction time distributions provides a powerful tool for investigating decision-making processes. Here we describe an open-source toolbox to allow the Linear Approach to Threshold with Ergodic Rate (LATER) model, the simplest principled model linking reaction times and decisions, to be applied to empirical reaction time data, including by clinicians and scientists without any programming experience.
分析反应时间分布可以深入了解大脑中的决策过程。线性遍历率阈值方法（LATER）模型可以说是预测反应时间分布的最简单模型，并且可以用最少两个自由参数来总结分布。然而，用于可视化和拟合LATER分布（“倒数概率”空间）的坐标是不规则的，这使得没有编程背景的人无法应用这个简单模型。在这里，我们描述了一个开源R包LATERmodel，它可以轻松可视化反应时间分布，并使用LATER模型进行拟合。利用文献中的典型数据，我们表明我们的工具复制了以前LATER分析工具的关键特征，同时还提供了更稳健的拟合程序和一种更灵活的方法来拟合非常快速的早期反应亚群。虽然LATERmodel的所有功能都可以直接在统计编程语言R中使用，但关键功能也可以通过图形用户界面获得，以使没有编程背景的研究人员能够将LATER模型应用于他们的反应时间数据。分析反应时间分布为研究决策过程提供了一个强大的工具。在这里，我们描述了一个开源工具箱，以使线性遍历率阈值方法（LATER）模型（将反应时间和决策联系起来的最简单的原理模型）能够应用于实证反应时间数据，包括供没有任何编程经验的临床医生和科学家使用。
