Alcalá Emmanuel, Saavedra Pablo, García-Rangel Frida, Sosa Rodrigo, Reyes Víctor, Buriticá Jonathan
Centro de Estudios e Investigaciones en Comportamiento, Universidad de Guadalajara, Calle Francisco de Quevedo #180, 44130, Guadalajara, México.
Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Behav Res Methods. 2025 Jun 16;57(7):199. doi: 10.3758/s13428-025-02720-4.
In recent years, with the advent of open-source programming languages such as R and Python, there has been a resurgence of the DIY spirit in the experimental analysis of behavior and, more broadly, in experimental psychology. This paper introduces YEAB, an R package that streamlines the processing and analysis of data from numerous common behavioral paradigms. Key features include automated extraction of raw data from MED-PC output files, flexible modeling of interresponse times (including mixture-exponential approaches), single-trial analysis for fixed-interval and peak procedures, and model fitting of delay discounting data in intertemporal choice tasks. By consolidating core functions into one accessible package, YEAB empowers researchers to efficiently handle multi-level data structures, promote reproducible workflows, and expand the scope of behavioral experimentation. Collectively, these functionalities facilitate robust, transparent analyses, fostering open-science practices and supporting innovative exploration in EAB and related fields.
近年来，随着R和Python等开源编程语言的出现，在行为实验分析以及更广泛的实验心理学领域，DIY精神再度兴起。本文介绍了YEAB，这是一个R包，它简化了来自众多常见行为范式的数据处理和分析。关键特性包括从MED-PC输出文件自动提取原始数据、灵活的反应间隔建模（包括混合指数方法）、固定间隔和峰值程序的单次试验分析，以及跨期选择任务中延迟折扣数据的模型拟合。通过将核心功能整合到一个易于访问的包中，YEAB使研究人员能够高效处理多层次数据结构，促进可重复的工作流程，并扩大行为实验的范围。总体而言，这些功能有助于进行稳健、透明的分析，促进开放科学实践，并支持行为分析及相关领域的创新探索。
