Publications

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Inferring the cognitive state of an individual in real time during task performance allows for implementation of corrective measures prior to the occurrence of an error. Current technology allows for real time cognitive state assessment based on objective physiological data though techniques such as neuroimaging and eye tracking. Although early results indicate effective construction of classifiers that distinguish between cognitive states in real time is a possibility in some settings, implementation of these classifiers into real world settings poses a number of challenges. Cognitive states of interest must be sufficiently distinct to allow for continuous discrimination in the operational environment using technology that is currently available as well as practical to implement.

A critical challenge in data science is conveying the meaning of data to human decision makers. While working with visualizations, decision makers are engaged in a visual search for information to support their reasoning process. As sensors proliferate and high performance computing becomes increasingly accessible, the volume of data decision makers must contend with is growing continuously and driving the need for more efficient and effective data visualizations. Consequently, researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles to assess the effectiveness of data visualizations. In this paper, we compare the performance of three different saliency models across a common set of data visualizations. This comparison establishes a performance baseline for assessment of new data visualization saliency models.

‘Big data’ is a phrase that has gained much traction recently. It has been defined as ‘a broad term for data sets so large or complex that traditional data processing applications are inadequate and there are challenges with analysis, searching and visualization’ [1]. Many domains struggle with providing experts accurate visualizations of massive data sets so that the experts can understand and make decisions about the data e.g., [2, 3, 4, 5]. Abductive reasoning is the process of forming a conclusion that best explains observed facts and this type of reasoning plays an important role in process and product engineering. Throughout a production lifecycle, engineers will test subsystems for critical functions and use the test results to diagnose and improve production processes. This paper describes a value-driven evaluation study [7] for expert analyst interactions with big data for a complex visual abductive reasoning task. Participants were asked to perform different tasks using a new tool, while eye tracking data of their interactions with the tool was collected. The participants were also asked to give their feedback and assessments regarding the usability of the tool. The results showed that the interactive nature of the new tool allowed the participants to gain new insights into their data sets, and all participants indicated that they would begin using the tool in its current state.

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Visual search data describe people’s performance on the common perceptual problem of identifying target objects in a complex scene. Technological advances in areas such as eye tracking now provide researchers with a wealth of data not previously available. The goal of this work is to support researchers in analyzing this complex and multimodal data and in developing new insights into visual search techniques. We discuss several methods drawn from the statistics and machine learning literature for integrating visual search data derived from multiple sources and performing exploratory data analysis. We ground our discussion in a specific task performed by officers at the Transportation Security Administration and consider the applicability, likely issues, and possible adaptations of several candidate analysis methods.

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.

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.

Numerous domains, ranging from medical diagnostics to intelligence analysis, involve visual search tasks in which people must find and identify specific items within large sets of imagery. These tasks rely heavily on human judgment, making fully automated systems infeasible in many cases. Researchers have investigated methods for combining human judgment with computational processing to increase the speed at which humans can triage large image sets. One such method is rapid serial visual presentation (RSVP), in which images are presented in rapid succession to a human viewer. While viewing the images and looking for targets of interest, the participant’s brain activity is recorded using electroencephalography (EEG). The EEG signals can be time-locked to the presentation of each image, producing event-related potentials (ERPs) that provide information about the brain’s response to those stimuli. The participants’ judgments about whether or not each set of images contained a target and the ERPs elicited by target and non-target images are used to identify subsets of images that merit close expert scrutiny [1]. Although the RSVP/EEG paradigm holds promise for helping professional visual searchers to triage imagery rapidly, it may be limited by the nature of the target items. Targets that do not vary a great deal in appearance are likely to elicit useable ERPs, but more variable targets may not. In the present study, we sought to extend the RSVP/EEG paradigm to the domain of aviation security screening, and in doing so to explore the limitations of the technique for different types of targets. Professional Transportation Security Officers (TSOs) viewed bag X-rays that were presented using an RSVP paradigm. The TSOs viewed bursts of images containing 50 segments of bag X-rays that were presented for 100 ms each. Following each burst of images, the TSOs indicated whether or not they thought there was a threat item in any of the images in that set. EEG was recorded during each burst of images and ERPs were calculated by time-locking the EEG signal to the presentation of images containing threats and matched images that were identical except for the presence of the threat item. Half of the threat items had a prototypical appearance and half did not. We found that the bag images containing threat items with a prototypical appearance reliably elicited a P300 ERP component, while those without a prototypical appearance did not. These findings have implications for the application of the RSVP/EEG technique to real-world visual search domains.

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.

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.