Trisha Van Zandt - US grants

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.

Psychologists and marketing experts have different theories about why people make the choices they do. While psychological theories more accurately describe individual human choice behavior in simple circumstances, they do not typically consider the range of variables, such as product features, consumer factors, vendor strategies, that marketing theories must be able to handle. Nor do they consider the fact that individuals' behaviors interact to form and influence marketplace demand. In this project, psychologists, marketing experts, and statisticians will collaborate to merge these theories. This collaboration will result in a deeper understanding of consumer behavior through better marketing theories and more complete psychological theories of human choice. Statistics is at the heart of the research, providing the unifying conceptual instrument for combining the varied theories and data sets through models such as the hierarchical Bayesian model. The hierarchical Bayesian structure allows one to describe simultaneously and coherently individual and aggregate behavior and to combine the disparate psychological and marketing theories and data. The Bayesian nature of these models allow one to adjust for known effects, even in experiments of modest size. The models also allow one to move from experimental settings to forecasting behavior in the marketplace.

Human behavior reflects a complex, multi-stage process that begins with the allocation of an individual's resources to affect his or her environment and to improve his or her state of being. This research will improve understanding of human behavior both in the marketplace and in general. The research will take place at the nexus of the three disciplines, statistics, marketing and psychology, with benefits to all three fields. The project has a strong educational emphasis and will provide the many students involved with a unique opportunity for cross-disciplinary training. This will aid them in their careers in academics, industry, or government. This award was supported as part of the Fiscal Year 2004 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).

This award will provide partial support for the 41st Annual Meeting of the Society for Mathematical Psychology to be held in Washington, D.C. on July 26-29, 2008. The Society for Mathematical Psychology conference encourages the presentation of research in which mathematical, statistical, or simulation methods play a significant role in the development of hypotheses or the interpretation of experimental results in the behavioral, neural, and cognitive sciences. In addition, accepted research papers focus on theoretical developments clearly relating to substantive issues or methodologies of obvious use in psychology, cognitive science, cognitive neuroscience, and related areas and/or experimental results which bear directly on particular mathematical or simulation models of aspects of human behavior. This year's conference themes include: (1) cognitive decision theory; (2) causal modeling; (3) computational linguistics; and (4) psychometric assessment.

Mathematical psychology brings together social scientists, statisticians, mathematicians and computer scientists to work on problems critical to behavioral scientists. Much transformational research has come from the mathematical psychology community, including mathematical models of brain function, memory, and decision-making, as well as the introduction of new methods for data analysis. While mathematical models improve our understanding of human behavior and provide formal structures for future scientific exploration, new and better methods for data analysis allow us to derive more accurate and nuanced conclusions from behavioral data. The annual meeting of the Society advances discovery and understanding. It promotes training and learning of new models and methods for analyzing behavioral data and it makes possible the broad dissemination of new findings important in all areas of psychology, as well as economics, political science, and sociology.

It is important to understand how people in our society, including our leaders and people in mission-critical positions, make decisions in various stressful contexts. A major area of research in cognitive psychology describes how people make decisions as a process of information accumulation over time. These theories state that people make better (more accurate) choices if they have enough time to accumulate relevant evidence. A strength of the information accumulation theories is that they explain why speedy responding, perhaps under stress, is often erroneous responding. Much experimental evidence supports the information accumulation theories. However, this evidence comes from experimental tasks embedded within a larger experimental context, a context that includes temporal constraints such as when the different components of the task are presented and when decisions are required. That is, psychology experiments (and many real-world decision problems) have a rhythm within which decision problems are framed and solved, and this rhythm also exerts an influence on decision performance. The importance of task rhythm is underscored by another theoretical perspective which emphasizes the dynamic aspects of the choice environment. In this perspective, attention is viewed as a dynamic process attuned to the underlying task rhythm, and understanding how the pace of the environment interacts with how effectively people focus attention is critical for understanding the decision-making process.

This proposal offers an integrated theory that formally combines the information accumulation and dynamic attending approaches. The proposed experiments test predictions of the hybrid model with regard to effects of task difficulty, pacing and rhythm, and the role of instructions and feedback. This research is significant because it links choice behaviors to contextual factors that shape people's attention, thus allowing them to "tune in" to relevant information more or less effectively. It will improve our understanding of choice performance of healthy individuals who must render sometimes critical decisions under time stress, and also, eventually, choice performance of individuals who suffer from a variety of attentional dysfunctions (e.g., attention deficit disorders, autism, etc.).

Repeated measurements taken on the same person are usually highly correlated and also influenced by changes in that individual's mental or physical state over time. Accurate conclusions about why a person's performance changes over time, as well as accurate hypotheses about how tasks are performed, require non-Gaussian time series models that can separate changes in performance due to changes in task conditions from changes in a person's mental or physical state. At present, techniques available for the analysis of human performance data are limited. In particular, models for repeated response time measurements usually fail to consider that the measurements are correlated and that overall speed may naturally fluctuate over time, even when all other task conditions remain the same. This project will address this problem by developing realistic models for response time series that provide a basis for simultaneously explaining the generating mechanism as well as describing trends due to changes in a person's state over time. An important component of this project will be the development of new computational methods for effectively fitting these statistical models as well as evaluating the model fits.

Response time measurements are important because how well people perform tasks is often measured, at least in part, by how quickly they can accomplish those tasks. In many situations, tasks are repetitive, requiring decisions and actions that recur many times within a fixed period of time. Such tasks, while commonly performed in psychological laboratories, are also used in a number of real-world settings such as assembly line work, during standardized testing, and in athletics. This project will result not only in better techniques of analysis for these kinds of measurements, but also in the development of more accurate and realistic models of how people perform repetitive tasks. A diverse cross-section of students (psychological and statistical) will be mentored in methods that both bridge and strengthen their two disciplines. All data collected and general-purpose software developed under this award will be made available (via the World Wide Web) to the research community.

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.