Many psychological theories assume that observable responses are determined by multiple latent processes. Multinomial processing tree (MPT) models are a class of cognitive models for discrete responses that allow researchers to disentangle and measure such processes. Before applying MPT models to specific psychological theories, it is necessary to tailor a model to specific experimental designs. In this tutorial, we explain how to develop, fit, and test MPT models using the classical pair-clustering model as a running example. The first part covers the required data structures, model equations, identifiability, model validation, maximum-likelihood estimation, hypothesis tests, and power analyses using the software multiTree. The second part introduces hierarchical MPT modeling which allows researchers to account for individual differences and to estimate the correlations of latent processes among each other and with additional covariates using the TreeBUGS package in R. All examples including data and annotated analysis scripts are provided at the Open Science Framework (https://osf.io/24pbm/). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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如何开发、测试和扩展多项处理树模型：教程。

许多心理学理论假设可观察的反应是由多个潜在过程决定的。多项处理树 (MPT) 模型是一类用于离散响应的认知模型，使研究人员能够理清并测量此类过程。在将MPT模型应用于特定的心理学理论之前，有必要针对特定​​的实验设计定制模型。在本教程中，我们将使用经典的对聚类模型作为运行示例来解释如何开发、拟合和测试 MPT 模型。第一部分涵盖所需的数据结构、模型方程、可识别性、模型验证、最大似然估计、假设检验和使用软件 multiTree 的功效分析。第二部分介绍了分层 MPT 建模，该模型允许研究人员考虑个体差异，并使用 R 中的 TreeBUGS 包来估计彼此之间以及与其他协变量之间的潜在过程的相关性。包括数据和带注释的分析脚本在内的所有示例均在 Open 中提供。科学框架（https://osf.io/24pbm/）。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。