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DDM-UI:R语言中用于行为研究中差异扩散模型的用户界面。

DDM-UI: A user interface in R for the discrepancy diffuse model in behavioral research.

作者信息

Aguayo-Mendoza Miguel, Dos Santos Cristiano Valerio

机构信息

Centro de Estudios e Investigación en Comportamiento, University of Guadalajara, Guadalajara, Mexico.

出版信息

Behav Res Methods. 2025 Mar 28;57(5):128. doi: 10.3758/s13428-025-02648-9.

Abstract

The diffuse discrepancy model (DiffDiscM) has proven to be a valuable tool for simulating both Pavlovian and operant conditioning phenomena. However, its original implementation in Pascal (SelNet1© interface) has limitations regarding accessibility and ease of use. This paper presents DDM-UI, a new user interface developed in R for the DiffDiscM. DDM-UI offers an intuitive, open-source platform that enables researchers to configure, run, and analyze DiffDiscM simulations more efficiently. The main features of DDM-UI are described, including network architecture setup, trial and contingency definition, and result visualization. Three use cases demonstrate the practical application of DDM-UI in simulating various conditioning experiments, including superstition, Pavlovian/autoshaped impulsivity, and complex phenomena such as blocking, compound conditioning, and successive conditioning. The validation process highlights DDM-UI's ability to replicate previous findings while offering enhanced data visualization and analysis capabilities. DDM-UI represents a significant advancement in the accessibility of the DiffDiscM, facilitating its use in behavioral research and promoting reproducibility in the field. The paper also discusses the limitations of the current implementation and suggests future developments to further enhance the tool's capabilities in exploring complex learning and behavioral phenomena.

摘要

扩散差异模型(DiffDiscM)已被证明是模拟巴甫洛夫条件反射和操作性条件反射现象的宝贵工具。然而,其最初用Pascal语言实现(SelNet1©接口)在可访问性和易用性方面存在局限性。本文介绍了DDM-UI,这是一个用R语言为DiffDiscM开发的新用户界面。DDM-UI提供了一个直观的开源平台,使研究人员能够更高效地配置、运行和分析DiffDiscM模拟。文中描述了DDM-UI的主要特性,包括网络架构设置、试验和偶然性定义以及结果可视化。三个用例展示了DDM-UI在模拟各种条件反射实验中的实际应用,包括迷信、巴甫洛夫式/自动形成的冲动性,以及诸如阻断、复合条件反射和连续条件反射等复杂现象。验证过程突出了DDM-UI在复制先前研究结果的同时,还具备增强的数据可视化和分析能力。DDM-UI代表了DiffDiscM在可访问性方面的重大进步,便于其在行为研究中的应用,并促进该领域的可重复性。本文还讨论了当前实现方式的局限性,并提出了未来的发展方向,以进一步增强该工具在探索复杂学习和行为现象方面的能力。

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