Publications

Haass, Michael J.; Burnham, Laurie B.; Cole, Kerstan S.; Jeffers, Robert F.; Adams, Susan S.; Warrender, Christina E.; Forsythe, James C.

Abstract not provided.

Adams, Susan S.; Cole, Kerstan S.; Haass, Michael J.; Warrender, Christina E.; Jeffers, Robert F.; Burnham, Laurie B.; Forsythe, James C.

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Roach, R.A.; Adams, Susan S.

Abstract not provided.

Harms, Gary A.; Knief, Ronald A.; Adams, Susan S.; Cole, Kerstan S.; Ford, John T.; Campbell, Rafe C.

Abstract not provided.

Harms, Gary A.; Monahan, Shean P.; Knief, Ronald A.; Adams, Susan S.; Cole, Kerstan S.; Ford, John T.; Campbell, Rafe C.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

McNamara, Laura A.; Cole, Kerstan S.; Haass, Michael J.; Matzen, Laura E.; Morrow, James D.; Adams, Susan S.; McMichael, Stephanie N.

Researchers at Sandia National Laboratories are integrating qualitative and quantitative methods from anthropology, human factors and cognitive psychology in the study of military and civilian intelligence analyst workflows in the United States’ national security community. Researchers who study human work processes often use qualitative theory and methods, including grounded theory, cognitive work analysis, and ethnography, to generate rich descriptive models of human behavior in context. In contrast, experimental psychologists typically do not receive training in qualitative induction, nor are they likely to practice ethnographic methods in their work, since experimental psychology tends to emphasize generalizability and quantitative hypothesis testing over qualitative description. However, qualitative frameworks and methods from anthropology, sociology, and human factors can play an important role in enhancing the ecological validity of experimental research designs.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Matzen, Laura E.; Haass, Michael J.; McNamara, Laura A.; Adams, Susan S.; McMichael, Stephanie N.

Vision is one of the dominant human senses and most human-computer interfaces rely heavily on the capabilities of the human visual system. An enormous amount of effort is devoted to finding ways to visualize information so that humans can understand and make sense of it. By studying how professionals engage in these visual search tasks, we can develop insights into their cognitive processes and the influence of experience on those processes. This can advance our understanding of visual cognition in addition to providing information that can be applied to designing improved data visualizations or training new analysts. In this study, we investigated the role of expertise on performance in a Synthetic Aperture Radar (SAR) target detection task. SAR imagery differs substantially from optical imagery, making it a useful domain for investigating expert-novice differences. The participants in this study included professional SAR imagery analysts, radar engineers with experience working with SAR imagery, and novices who had little or no prior exposure to SAR imagery. Participants from all three groups completed a domain-specific visual search task in which they searched for targets within pairs of SAR images. They also completed a battery of domain-general visual search and cognitive tasks that measured factors such as mental rotation ability, spatial working memory, and useful field of view. The results revealed marked differences between the professional imagery analysts and the other groups, both for the domain-specific task and for some domain-general tasks. These results indicate that experience with visual search in non-optical imagery can influence performance on other domains.

Procedia Manufacturing

Adams, Susan S.; Cole, Kerstan S.; Haass, Michael J.; Warrender, Christina E.; Jeffers, Robert F.; Burnham, Laurie B.; Forsythe, James C.

Electric distribution utilities, the companies that feed electricity to end users, are overseeing a technological transformation of their networks, installing sensors and other automated equipment, that are fundamentally changing the way the grid operates. These grid modernization efforts will allow utilities to incorporate some of the newer technology available to the home user – such as solar panels and electric cars – which will result in a bi-directional flow of energy and information. How will this new flow of information affect control room operations? How will the increased automation associated with smart grid technologies influence control room operators’ decisions? And how will changes in control room operations and operator decision making impact grid resilience? These questions have not been thoroughly studied, despite the enormous changes that are taking place. In this study, which involved collaborating with utility companies in the state of Vermont, the authors proposed to advance the science of control-room decision making by understanding the impact of distribution grid modernization on operator performance. Distribution control room operators were interviewed to understand daily tasks and decisions and to gain an understanding of how these impending changes will impact control room operations. Situation awareness was found to be a major contributor to successful control room operations. However, the impact of growing levels of automation due to smart grid technology on operators’ situation awareness is not well understood. Future work includes performing a naturalistic field study in which operator situation awareness will be measured in real-time during normal operations and correlated with the technological changes that are underway. The results of this future study will inform tools and strategies that will help system operators adapt to a changing grid, respond to critical incidents and maintain critical performance skills.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Haass, Michael J.; Matzen, Laura E.; Adams, Susan S.; Roach, R.A.

The potential for bias to affect the results of knowledge elicitation studies is well recognized. Researchers and knowledge engineers attempt to control for bias through careful selection of elicitation and analysis methods. Recently, the development of a wide range of physiological sensors, coupled with fast, portable and inexpensive computing platforms, has added an additional dimension of objective measurement that can reduce bias effects. In the case of an abductive reasoning task, bias can be introduced through design of the stimuli, cues from researchers, or omissions by the experts. We describe a knowledge elicitation methodology robust to various sources of bias, incorporating objective and cross-referenced measurements. The methodology was applied in a study of engineers who use multivariate time series data to diagnose mance of devices throughout the production lifecycle. For visual reasoning tasks, eye tracking is particularly effective at controlling for biases of omission by providing a record of the subject’s attention allocation.

Procedia Manufacturing

Haass, Michael J.; Warrender, Christina E.; Burnham, Laurie B.; Jeffers, Robert F.; Adams, Susan S.; Cole, Kerstan S.; Forsythe, James C.

The impact of automation on human performance has been studied by human factors researchers for over 35 years. One unresolved facet of this research is measurement of the level of automation across and within engineered systems. Repeatable methods of observing, measuring and documenting the level of automation are critical to the creation and validation of generalized theories of automation's impact on the reliability and resilience of human-in-the-loop systems. Numerous qualitative scales for measuring automation have been proposed. However these methods require subjective assessments based on the researcher's knowledge and experience, or through expert knowledge elicitation involving highly experienced individuals from each work domain. More recently, quantitative scales have been proposed, but have yet to be widely adopted, likely due to the difficulty associated with obtaining a sufficient number of empirical measurements from each system component. Our research suggests the need for a quantitative method that enables rapid measurement of a system's level of automation, is applicable across domains, and can be used by human factors practitioners in field studies or by system engineers as part of their technical planning processes. In this paper we present our research methodology and early research results from studies of electricity grid distribution control rooms. Using a system analysis approach based on quantitative measures of level of automation, we provide an illustrative analysis of select grid modernization efforts. This measure of the level of automation can be displayed as either a static, historical view of the system's automation dynamics (the dynamic interplay between human and automation required to maintain system performance) or it can be incorporated into real-time visualization systems already present in control rooms.

Matzen, Laura E.; Haass, Michael J.; Silva, Austin R.; Trumbo, Michael C.; Adams, Susan S.; McNamara, Laura A.; Speed, Ann S.

Abstract not provided.

McNamara, Laura A.; Cole, Kerstan S.; Adams, Susan S.

Abstract not provided.

McNamara, Laura A.; Cole, Kerstan S.; Adams, Susan S.

Abstract not provided.

Adams, Susan S.; Cole, Kerstan S.; McNamara, Laura A.

Abstract not provided.

Burnham, Laurie B.; Warrender, Christina E.; Cole, Kerstan S.; Forsythe, James C.; Haass, Michael J.; Adams, Susan S.

Abstract not provided.

Reliability Engineering and System Safety

Groth, Katrina G.; Swiler, Laura P.; Adams, Susan S.

In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.

Warrender, Christina E.; Cole, Kerstan S.; Forsythe, James C.; Haass, Michael J.; Adams, Susan S.

Abstract not provided.

Burnham, Laurie B.; Warrender, Christina E.; Cole, Kerstan S.; Forsythe, James C.; Haass, Michael J.; Adams, Susan S.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Cole, Kerstan S.; Adams, Susan S.; McNamara, Laura A.; Ganter, John H.

The purpose of the current study was to analyze the work of imagery analysts associated with Sagebrush, a Synthetic Aperture Radar (SAR) imaging system, using an adapted version of cognitive work analysis (CWA). This was achieved by conducting a work domain analysis (WDA) for the system under consideration. Another purpose of this study was to describe how we adapted the WDA framework to include a sequential component and a means to explicitly represent relationships between components. Lastly, we present a simplified work domain representation that we have found effective in communicating the importance of analysts' adaptive strategies to inform the research strategies of computational science researchers who want to develop useful algorithms, but who have little or no familiarity with sensor data analysis work. © 2014 Springer International Publishing.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Adams, Susan S.; Cole, Kerstan S.; McNamara, Laura A.

Imagery analysts are given the difficult task of determining, post-hoc, if particular events of importance had occurred, employing Synthetic Aperture Radar (SAR) images, written reports and PowerPoint presentations to make their decision. We were asked to evaluate the current system analysis process and make recommendations for a future temporal geospatial analysis prototype that is envisioned to allow analysts to quickly search for temporal and spatial relationships between image-derived features. As such, we conducted a Hierarchical task analysis (HTA; [3], [6]) to understand the analysts' tasks and subtasks. We also implemented a timeline analysis and workload assessment [4] to better understand which tasks were the most time-consuming and perceived as the most effortful. Our results gave the team clear recommendations and requirements for a prototype. © 2014 Springer International Publishing.

Reliability Engineering and System Safety

Liao, Huafei L.; Groth, Katrina G.; Adams, Susan S.

Abstract not provided.

Groth, Katrina G.; Liao, Huafei L.; Adams, Susan S.

Abstract not provided.

Liao, Huafei L.; Groth, Katrina G.; Adams, Susan S.

Abstract not provided.

Forsythe, James C.; Silva, Austin R.; Adams, Susan S.

Abstract not provided.

Monahan, Shean P.; Adams, Susan S.

Abstract not provided.