Department of Psychology, Ames Hall, Johns Hopkins University, 3400 N. Charles St., 21218-2686, Baltimore, MD,
Psychon Bull Rev. 1995 Mar;2(1):20-54. doi: 10.3758/BF03214411.
Statistical mimicking issues involving reaction time measures are introduced and discussed in this article. Often, discussions of mimicking have concerned the question of the serial versus parallel processing of inputs to the cognitive system. We will demonstrate that there are several alternative structures that mimic various existing models in the literature. In particular, single-process models have been neglected in this area. When parameter variability is incorporated into single-process models, resulting in discrete or continuous mixtures of reaction time distributions, the observed reaction time distribution alone is no longer as useful in allowing inferences to be made about the architecture of the process that produced it. Many of the issues are raised explicitly in examination of four different case studies of mimicking. Rather than casting a shadow over the use of quantitative methods in testing models of cognitive processes, these examples emphasize the importance of examining reaction time data armed with the tools of quantitative analysis, the importance of collecting data from the context of specific process models, and the importance of expanding the database to include other dependent measures.
本文介绍并讨论了涉及反应时间测量的统计模拟问题。通常，模拟的讨论涉及到认知系统输入的串行与并行处理问题。我们将证明，有几种替代结构可以模拟文献中各种现有的模型。特别是，在这个领域中忽略了单过程模型。当将参数可变性纳入单过程模型中，导致反应时间分布的离散或连续混合时，仅观察到的反应时间分布不再像以前那样有用，无法根据产生它的过程的体系结构进行推断。在对四种不同模拟案例的研究中，明确提出了许多问题。这些例子并没有给使用定量方法测试认知过程模型蒙上阴影，反而强调了在使用定量分析工具检查反应时间数据的重要性，强调了从特定过程模型的上下文中收集数据的重要性，以及扩展数据库以包括其他因变量的重要性。
