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，也会受到该人随着时间的推移精神或身体状态变化的影响。关于一个人的表现为什么会随着时间的推移而变化的准确结论，以及关于如何执行任务的准确假设，都需要非高斯时间序列模型，该模型可以将由于任务条件的变化而导致的表现变化与人的心理或身体状态的变化分开。目前，可用于分析人类表现数据的技术有限。特别是，重复响应时间测量的模型通常没有考虑到测量是相关的，总体速度可能会随着时间的推移自然波动，即使所有其他任务条件保持不变。这个项目将通过开发响应时间序列的现实模型来解决这一问题，这些模型为同时解释生成机制以及描述由于人的状态随着时间的变化而产生的趋势提供了基础。这个项目的一个重要组成部分将是开发新的计算方法，以有效地拟合这些统计模型，并评估模型的适合性。响应时间测量很重要，因为人们执行任务的好坏通常是由他们完成任务的速度来衡量的，至少部分是这样。在许多情况下，任务是重复的，需要在固定的时间段内多次重复做出决定和采取行动。这类任务虽然通常在心理实验室进行，但也用于许多现实世界的环境中，如流水线工作、标准化测试期间和体育运动中。该项目不仅将为这些类型的测量带来更好的分析技术，而且还将开发出人们如何执行重复任务的更准确和更现实的模型。不同领域的学生(心理学和统计学)将以既能沟通又能强化这两门学科的方法进行指导。根据该奖项收集的所有数据和开发的通用软件将(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.