Bayesian Analysis of Chronometric Data

计时数据的贝叶斯分析

• 批准号: 0214574
• 负责人: Trisha Van Zandt
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2006-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0214574&HistoricalAwards=false
• 关键词: Bayesian; Analysis; Chronometric; Data

This research will develop accurate and powerful Bayesian modeling and computational methods for the problem of response time (RT) analysis. Although Bayesian techniques are well established in other fields, social scientists very rarely use them because they require a considerable investment in computational resources as well as additional statistical training. The project will develop a number of strategies that will improve the analysis of RT data, including analyses that consider theories about how RTs are produced and new procedures that can help untrained practitioners use Bayesian methods without too much inconvenience. The study also undertakes a program of education and dissemination to improve the overall quality of statistical analyses of RT data. Thus, this research will result in new and better statistical procedures specific for RT (and similar chronometric) data.The importance of this project is considerable. How well a person performs a task is often evaluated by way of how quickly he or she can respond during the task. Measurements of RTs are important for both theoretical and pragmatic reasons. Theoretically, RTs are used to test hypotheses about cognitive structure, the ways in which people use and process information, and how changes in the environment influence human behavior. Pragmatically, RTs are important for evaluating human performance in many areas. They assist machine interface design decisions, such as the optimal way to present information to a pilot or the best place where to put a turn signal lever. They are also used in medicine; diagnoses of some organic brain disorders such as Alzheimer's disease or Attention Deficit Hyperactivity Disorder can be informed by a patient's RTs on certain kinds of tests. Many of the statistical procedures used to test hypotheses based on RTs are suboptimal. They depend on oversimplifying assumptions about RT data that are usually incorrect, and consequently the inferences that are made about RTs collected in different environments can be faulty. This project will result in more accurate characterization of RT data and therefore improved decision making about human capabilities and disease.

这项研究将为响应时间（RT）分析问题开发准确而强大的贝叶斯建模和计算方法。 尽管贝叶斯技术在其他领域已经很成熟，但社会科学家很少使用它们，因为它们需要在计算资源以及额外的统计培训方面进行大量投资。 该项目将制定一系列策略来改进 RT 数据的分析，包括考虑有关 RT 如何产生的理论的分析，以及可以帮助未经培训的从业者使用贝叶斯方法而不会带来太多不便的新程序。 该研究还开展了一项教育和传播计划，以提高 RT 数据统计分析的整体质量。 因此，这项研究将产生专门针对 RT（和类似计时）数据的新的、更好的统计程序。这个项目的重要性是相当大的。 一个人执行任务的好坏通常是通过他或她在任务期间的反应速度来评估的。 出于理论和实用原因，RT 的测量都很重要。 从理论上讲，RT 用于检验有关认知结构、人们使用和处理信息的方式以及环境变化如何影响人类行为的假设。 实际上，RT 对于评估人类在许多领域的表现非常重要。 它们协助机器界面设计决策，例如向飞行员呈现信息的最佳方式或放置转向信号杆的最佳位置。 它们也用于医学；一些器质性脑部疾病（例如阿尔茨海默氏病或​​注意力缺陷多动障碍）的诊断可以通过患者在某些类型测试中的 RT 来了解。 许多用于检验基于 RT 的假设的统计程序都不是最优的。 它们依赖于对 RT 数据的过于简单化的假设，而这些假设通常是不正确的，因此对在不同环境中收集的 RT 做出的推论可能是错误的。 该项目将更准确地表征 RT 数据，从而改进有关人类能力和疾病的决策。