Modeling Trends, Dependence, and Tail Structure in Sequential Response Time Data

对同一个人进行的重复测量通常是高度相关的，也会受到个人精神或身体状态随时间变化的影响。关于为什么一个人的表现会随着时间的推移而变化的准确结论，以及关于任务如何执行的准确假设，都需要非高斯时间序列模型，这种模型可以将任务条件的变化导致的表现变化与人的精神或身体状态的变化区分开来。目前，用于分析人类表现数据的技术是有限的。特别是，用于重复响应时间测量的模型通常没有考虑到测量是相关的，并且即使在所有其他任务条件保持不变的情况下，总体速度也可能随时间自然波动。该项目将通过开发响应时间序列的现实模型来解决这个问题，该模型为同时解释产生机制以及描述由于人的状态随时间变化而导致的趋势提供了基础。该项目的一个重要组成部分将是开发新的计算方法来有效地拟合这些统计模型以及评估模型拟合。响应时间测量很重要，因为人们执行任务的好坏通常是通过他们完成任务的速度来衡量的，至少在一定程度上是这样。在许多情况下，任务是重复的，需要在固定时间内重复多次的决策和行动。这些任务通常在心理实验室中进行，但也会在许多现实环境中使用，比如流水线作业、标准化测试和体育运动。这个项目不仅会产生更好的分析这些测量的技术，而且还会发展出更准确、更现实的关于人们如何执行重复性任务的模型。学生的不同截面（心理学和统计学）将以桥梁和加强他们的两个学科的方法进行指导。所有收集到的数据和根据该奖项开发的通用软件将（通过万维网）提供给研究界。

Repeated measurements taken on the same person are usually highly correlated and also influenced by changes in that individual's mental or physical state over time. Accurate conclusions about why a person's performance changes over time, as well as accurate hypotheses about how tasks are performed, require non-Gaussian time series models that can separate changes in performance due to changes in task conditions from changes in a person's mental or physical state. At present, techniques available for the analysis of human performance data are limited. In particular, models for repeated response time measurements usually fail to consider that the measurements are correlated and that overall speed may naturally fluctuate over time, even when all other task conditions remain the same. This project will address this problem by developing realistic models for response time series that provide a basis for simultaneously explaining the generating mechanism as well as describing trends due to changes in a person's state over time. An important component of this project will be the development of new computational methods for effectively fitting these statistical models as well as evaluating the model fits.Response time measurements are important because how well people perform tasks is often measured, at least in part, by how quickly they can accomplish those tasks. In many situations, tasks are repetitive, requiring decisions and actions that recur many times within a fixed period of time. Such tasks, while commonly performed in psychological laboratories, are also used in a number of real-world settings such as assembly line work, during standardized testing, and in athletics. This project will result not only in better techniques of analysis for these kinds of measurements, but also in the development of more accurate and realistic models of how people perform repetitive tasks. A diverse cross-section of students (psychological and statistical) will be mentored in methods that both bridge and strengthen their two disciplines. All data collected and general-purpose software developed under this award will be made available (via the World Wide Web) to the research community.