Publications

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Exposure to extreme environments is both mentally and physically taxing, leading to suboptimal performance and even life-threatening emergencies. Physiological and cognitive monitoring could provide the earliest indicator of performance decline and inform appropriate therapeutic intervention, yet little research has explored the relationship between these markers in strenuous settings. The Rim-to-Rim Wearables at the Canyon for Health (R2RWATCH) study is a research project at Sandia National Laboratories funded by the Defense Threat Reduction Agency to identify which physiological and cognitive phenomena collected by non-invasive wearable devices are the most related to performance in extreme environments. In a pilot study, data were collected from civilians and military warfighters hiking the Rim-to-Rim trail at the Grand Canyon. Each participant wore a set of devices collecting physiological, cognitive, and environmental data such as heart rate, memory, ambient temperature, etc. Promising preliminary results found correlates between physiological markers recorded by the wearable devices and decline in cognitive abilities, although further work is required to refine those measurements. Planned follow-up studies will validate these findings and further explore outstanding questions.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

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Many companies rely on user experience metrics, such as Net Promoter scores, to monitor changes in customer attitudes toward their products. This paper suggests that similar metrics can be used to assess the user experience of the pilots and sensor operators who are tasked with using our radar, EO/IR, and other remote sensing technologies. As we have previously discussed, the problem of making our national security remote sensing systems useful, usable and adoptable is a human-system integration problem that does not get the sustained attention it deserves, particularly given the high-throughput, information-dense task environments common to military operations. In previous papers, we have demonstrated how engineering teams can adopt well-established human-computer interaction principles to fix significant usability problems in radar operational interfaces. In this paper, we describe how we are using a combination of Situation Awareness design methods, along with techniques from the consumer sector, to identify opportunities for improving human-system interactions. We explain why we believe that all stakeholders in remote sensing-including program managers, engineers, or operational users-can benefit from systematically incorporating some of these measures into the evaluation of our national security sensor systems. We will also provide examples of our own experience adapting consumer user experience metrics in operator-focused evaluation of currently deployed radar interfaces.

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This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need for cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. We then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. In conclusion, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

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