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.

本研究将为响应时间（RT）分析问题开发准确和强大的贝叶斯建模和计算方法。 虽然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，因为它们需要大量的计算资源投资以及额外的统计培训。 该项目将开发一些策略，以改善RT数据的分析，包括考虑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方案，以提高RT数据统计分析的整体质量。 因此，这项研究将为RT（和类似的计时）数据带来新的、更好的统计程序。该项目的重要性是相当大的。 一个人完成一项任务的能力通常是通过他或她在任务中的反应速度来评估的。 测量的RT是重要的理论和实用的原因。 从理论上讲，RT被用来测试有关认知结构的假设，人们使用和处理信息的方式，以及环境的变化如何影响人类行为。 实际上，RT对于评估人类在许多领域的表现非常重要。 它们有助于机器界面设计决策，例如向飞行员提供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;阿尔茨海默病或注意力缺陷多动障碍等一些器质性脑部疾病的诊断可以通过患者的RT进行某些类型的测试来得知。 许多用于检验基于RT的假设的统计程序是次优的。 它们依赖于对RT数据的过度简化的假设，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进行的推断可能是错误的。 该项目将导致RT数据的更准确表征，从而改善有关人类能力和疾病的决策。