New Methods for the Analysis of Human Performance Data

分析人类绩效数据的新方法

• 批准号: 1424481
• 负责人: Trisha Van Zandt
• 金额: $ 35万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1424481&HistoricalAwards=false
• 关键词: New; Methods; Analysis; Human; Performance

This research project will develop new statistical models to facilitate the analysis of human performance data. The improved techniques will reduce the need for ad hoc data processing and increase the amount of data available for analysis. The factors that influence human performance usually extend beyond those identified as important by a researcher's restricted theoretical framework. A person's level of education, for example, may well influence performance of a simple task for which a theory of perception concerns only levels of illumination and spatial location. As a result, it often is difficult to determine why particular people fail to perform tasks as expected. Researchers rely on ad hoc strategies to identify and remove from a data set people who perform poorly or who seem unmotivated. Such strategies generally have little theoretical justification and thus have the potential to degrade the information available in the data and to introduce bias in the conclusions drawn from the data. The models developed in this research will help ensure the accuracy of conclusions drawn from experiments on human performance. These models will be of interest to researchers across a range of disciplines that care about human performance data and also may be applied to other types of data in medicine, engineering, and finance. New software will be developed and made available to other researchers. The project will contribute to the training of both undergraduate and graduate students in Psychology and Statistics and help further connections between those disciplines.Learning about the processes that determine how well people perform tasks in different circumstances requires at least two things: first, a theoretical framework consisting of models that can predict how the human cognitive system responds to and interacts with the environment, and, second, accurate and robust statistical techniques that can be used to analyze data within the context of these models. The investigators will develop hierarchical Bayesian models that incorporate stimulus-independent response strategies to minimize the need for data pre-processing. The models will separate task appropriate (stimulus-dependent) from task inappropriate ( stimulus-independent) responding in such a way that (i) no data need to be removed, and (ii) task performance changes over time can be examined within a coherent theoretical framework. The researchers will collect new data from experiments that will provoke people to move from task-appropriate to task-inappropriate performance strategies over time. This will enable the investigators to evaluate their theories of human performance and the techniques they will develop to analyze the data.

这一研究项目将开发新的统计模型，以促进对人类表现数据的分析。改进后的技术将减少对特别数据处理的需要，并增加可供分析的数据量。影响人类表现的因素通常超出了研究人员受限的理论框架所认定的重要因素。例如，一个人的教育水平很可能会影响一项简单任务的表现，对于这项任务，知觉理论只涉及照明水平和空间位置。因此，通常很难确定特定人员未能按预期完成任务的原因。研究人员依靠特别策略来识别并从数据集中删除表现不佳或似乎没有动力的人。这种策略通常没有什么理论上的合理性，因此有可能降低数据中的信息，并在从数据中得出的结论中引入偏见。这项研究中开发的模型将有助于确保从关于人类行为的实验得出的结论的准确性。这些模型将引起一系列学科的研究人员的兴趣，这些学科关心人类表现数据，也可能应用于医学、工程和金融领域的其他类型的数据。将开发新的软件，并向其他研究人员提供。该项目将有助于对本科生和研究生进行心理学和统计学方面的培训，并有助于这些学科之间的进一步联系。要了解决定人们在不同环境下如何出色完成任务的过程，至少需要两件事：第一，由模型组成的理论框架，该模型可以预测人类认知系统如何响应环境并与环境相互作用；第二，可以用于在这些模型的背景下分析数据的准确而稳健的统计技术。研究人员将开发层次化的贝叶斯模型，该模型包含不依赖刺激的反应策略，以最大限度地减少数据预处理的需要。这些模型将任务适当(刺激相关)和任务不适当(刺激无关)的反应分开，使得(I)不需要删除数据，(Ii)任务绩效随时间的变化可以在一个连贯的理论框架内进行考察。研究人员将从实验中收集新的数据，这些实验将促使人们随着时间的推移从适合任务的表现策略转变为不适合任务的表现策略。这将使研究人员能够评估他们的人类表现理论，以及他们将开发的分析数据的技术。

项目成果

Anchored Bayesian Gaussian mixture models

• DOI: 10.1214/20-ejs1756
• 发表时间: 2020-01-01
• 期刊: ELECTRONIC JOURNAL OF STATISTICS
• 影响因子: 1.1
• 作者: Kunkel, Deborah;Peruggia, Mario
• 通讯作者: Peruggia, Mario