New Methods for the Analysis of Human Performance Data

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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.