Department of Psychology, University of Massachusetts Amherst.
Psychol Bull. 2013 Nov;139(6):1173-203. doi: 10.1037/a0033044. Epub 2013 Jun 3.
Multinomial processing tree (MPT) models such as the single high-threshold, double high-threshold, and low-threshold models are discrete-state decision models that map internal cognitive events onto overt responses. The apparent benefit of these models is that they provide independent measures of accuracy and response bias, a claim that has motivated their frequent application in many areas of psychological science including perception, item and source memory, social cognition, reasoning, educational testing, eyewitness testimony, and psychopathology. Before appropriate conclusions about a given analysis can be drawn, however, one must first confirm that the model's assumptions about the underlying structure of the data are valid. The current review outlines the assumptions of several popular MPT models and assesses their validity using multiple sources of evidence, including receiver operating characteristics, direct model fits, and experimental tests of qualitative predictions. We argue that the majority of the evidence is inconsistent with these models and that, instead, the evidence supports continuous models such as those based on signal detection theory (SDT). Hybrid models that incorporate both SDT and MPT processes are also explored, and we conclude that these models retain the limitations associated with their threshold model predecessors. The potentially severe consequences associated with using an invalid model to interpret data are discussed, and a simple tutorial and model-fitting tool is provided to allow implementation of the empirically supported SDT model.
多项处理树 (MPT) 模型，如单高阈值、双高阈值和低阈值模型，是一种离散状态决策模型，将内部认知事件映射到外显反应上。这些模型的明显优势在于，它们提供了准确性和反应偏差的独立测量，这一说法促使它们在包括感知、项目和来源记忆、社会认知、推理、教育测试、目击证词和精神病理学在内的许多心理学领域得到了频繁的应用。然而，在对特定分析得出适当结论之前，必须首先确认模型关于数据潜在结构的假设是有效的。本综述概述了几种流行的 MPT 模型的假设，并使用多种证据来源评估了它们的有效性，包括接收者操作特征、直接模型拟合和定性预测的实验测试。我们认为，大多数证据与这些模型不一致，相反，证据支持基于信号检测理论 (SDT) 的连续模型。还探讨了结合 SDT 和 MPT 过程的混合模型，我们得出的结论是，这些模型保留了与其阈值模型前辈相关的局限性。讨论了使用无效模型来解释数据所带来的潜在严重后果，并提供了一个简单的教程和模型拟合工具，以允许实施经验支持的 SDT 模型。
