New Methods for the Analysis of Human Performance Data

这项研究计划将发展新的统计模型，以方便分析人类表现数据。改进后的技术将减少对特别数据处理的需求，并增加可用于分析的数据量。影响人类表现的因素通常超出了研究人员有限的理论框架所确定的重要因素。例如，一个人的教育水平很可能影响到一项简单任务的表现，而感知理论只涉及光照水平和空间位置。因此，通常很难确定为什么特定的人不能按预期完成任务。研究人员依靠特别的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依据，因此有可能降低数据中可用的信息，并在从数据得出的结论中引入偏见。在这项研究中开发的模型将有助于确保从人类表现实验中得出的结论的准确性。这些模型将引起关注人类表现数据的各个学科的研究人员的兴趣，也可能应用于医学、工程和金融领域的其他类型的数据。新的软件将被开发并提供给其他研究人员。该项目将有助于培养心理学和统计学的本科生和研究生，并有助于进一步加强这些学科之间的联系。了解决定人们在不同情况下如何完成任务的过程至少需要两件事：首先，一个由模型组成的理论框架，可以预测人类认知系统如何对环境做出反应并与环境相互作用；其次，可以用来分析这些模型背景下数据的准确而稳健的统计技术。研究人员将开发包含刺激独立反应策略的分层贝叶斯模型，以尽量减少对数据预处理的需求。这些模型将区分任务适当（刺激依赖）和任务不适当（刺激独立）的反应，这样(i)不需要删除数据，（ii）任务性能随时间的变化可以在一个连贯的理论框架内进行检查。研究人员将从实验中收集新的数据，这些数据将促使人们在一段时间内从适合任务的表现策略转向不适合任务的表现策略。这将使调查人员能够评估他们的人类表现理论和他们将开发的技术来分析数据。

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.