Applying the LATER model to reaction time data: an open-source toolkit.

IF 2.1 3区 医学 Q3 NEUROSCIENCES
Journal of neurophysiology Pub Date : 2025-02-01 Epub Date: 2024-12-24 DOI:10.1152/jn.00396.2024
Andrew J Anderson, Damien J Mannion, Maria Del Mar Quiroga, Edoardo Tescari
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引用次数: 0

Abstract

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 RShiny graphical user interface to allow researchers without programming background to apply the LATER model to their reaction time data.NEW & NOTEWORTHY 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.

将LATER模型应用于反应时间数据:一个开源工具包。
分析反应时间分布可以让我们深入了解大脑的决策过程。具有遍历率的阈值线性方法(LATER)模型可以说是预测反应时间分布的最简单模型,并且可以总结只有两个自由参数的分布。然而,使用LATER(“互易”空间)可视化和拟合分布的坐标是不规则的,使得没有编程背景的人无法应用这个简单的模型。在这里,我们描述了一个开源的R包——LATERmodel——它可以很容易地可视化反应时间分布,以及使用LATER模型对这些分布进行拟合。使用文献中的规范数据,我们展示了我们的工具复制了以前的LATER分析工具的关键特征,同时也提供了更强大的拟合程序和更灵活的方法来拟合非常快速,早期反应的子种群。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RShiny图形用户界面使用,从而允许没有编程背景的研究人员将LATER模型应用于他们的反应时间数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of neurophysiology
Journal of neurophysiology 医学-神经科学
CiteScore
4.80
自引率
8.00%
发文量
255
审稿时长
2-3 weeks
期刊介绍: The Journal of Neurophysiology publishes original articles on the function of the nervous system. All levels of function are included, from the membrane and cell to systems and behavior. Experimental approaches include molecular neurobiology, cell culture and slice preparations, membrane physiology, developmental neurobiology, functional neuroanatomy, neurochemistry, neuropharmacology, systems electrophysiology, imaging and mapping techniques, and behavioral analysis. Experimental preparations may be invertebrate or vertebrate species, including humans. Theoretical studies are acceptable if they are tied closely to the interpretation of experimental data and elucidate principles of broad interest.
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