Publications

Journal of Human Performance in Extreme Environments

Divis, Kristin; Abbott, Robert G.; Branda, Catherine B.; Avina, Glory E.; Femling, Jon F.; Huerta, Jose G.; Jelinkova, Lucie J.; Jennings, Jeremy K.; Pearce, Emily P.; Ries, Daniel R.; Sanchez, Danielle; Silva, Austin R.

Abstract not provided.

Proceedings of the Annual Hawaii International Conference on System Sciences

Ferguson-Walter, Kimberly J.; Shade, Temmie B.; Rogers, Andrew V.; Niedbala, Elizabeth M.; Trumbo, Michael C.; Nauer, Kevin S.; Divis, Kristin; Jones, Aaron P.; Combs, Angela C.; Abbott, Robert G.

The Tularosa study was designed to understand how defensive deception-including both cyber and psychological-affects cyber attackers. Over 130 red teamers participated in a network penetration task over two days in which we controlled both the presence of and explicit mention of deceptive defensive techniques. To our knowledge, this represents the largest study of its kind ever conducted on a professional red team population. The design was conducted with a battery of questionnaires (e.g., experience, personality, etc.) and cognitive tasks (e.g., fluid intelligence, working memory, etc.), allowing for the characterization of a “typical” red teamer, as well as physiological measures (e.g., galvanic skin response, heart rate, etc.) to be correlated with the cyber events. This paper focuses on the design, implementation, data, population characteristics, and begins to examine preliminary results.

Ferguson-Walter, Kimberly F.; Shade, Temmie S.; Rogers, Andrew R.; Trumbo, Michael C.; Nauer, Kevin S.; Divis, Kristin; Jones, Aaron P.; Combs, Angela C.; Abbott, Robert G.; Niedbala, Elizabeth N.

Abstract not provided.

Ferguson-Walter, Kimberly F.; Shade, Temmie S.; Rogers, Andrew R.; Combs, Angela C.; Divis, Kristin; Nauer, Kevin S.; Jones, Aaron P.; Trumbo, Michael C.; Abbott, Robert G.

Abstract not provided.

IEEE Transactions on Visualization and Computer Graphics

Matzen, Laura E.; Haass, Michael J.; Divis, Kristin; Wang, Zhiyuan; Wilson, Andrew T.

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.

Avina, Glory E.; Divis, Kristin; Abbott, Robert G.

Abstract not provided.

Matzen, Laura E.; Haass, Michael J.; Divis, Kristin; Wang, Zhiyuan W.; Wilson, Andrew T.

Abstract not provided.

Wilson, Andrew T.; Divis, Kristin; Matzen, Laura E.; Haass, Michael J.

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Divis, Kristin; Wilson, Andrew T.

Abstract not provided.

Divis, Kristin; Avina, Glory E.; Divis, Kristin; Abbott, Robert G.; Anderson-Bergman, Clifford I.; Branda, Catherine B.; Divis, Kristin; Jelinkova, Lucie J.; Newton, Victoria E.; Pearce, Emily P.; Femling, Jon F.

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin

Abstract not provided.

Avina, Glory E.; Abbott, Robert G.; Anderson-Bergman, Clifford I.; Divis, Kristin

Abstract not provided.

Avina, Glory E.; Abbott, Robert G.; Anderson-Bergman, Clifford I.; Divis, Kristin

Abstract not provided.

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

Avina, Glory E.; Abbott, Robert G.; Anderson-Bergman, Clifford I.; Branda, Catherine B.; Divis, Kristin; Newton, Victoria E.; Pearce, Emily; Femling, Jon

The Rim-to-Rim Wearables At The Canyon for Health (R2R WATCH) study examines metrics recordable on commercial off the shelf (COTS) devices that are most relevant and reliable for the earliest possible indication of a health or performance decline. This is accomplished through collaboration between Sandia National Laboratories (SNL) and The University of New Mexico (UNM) where the two organizations team up to collect physiological, cognitive, and biological markers from volunteer hikers who attempt the Rim-to-Rim (R2R) hike at the Grand Canyon. Three forms of data are collected as hikers travel from rim to rim: physiological data through wearable devices, cognitive data through a cognitive task taken every 3 hours, and blood samples obtained before and after completing the hike. Data is collected from both civilian and warfighter hikers. Once the data is obtained, it is analyzed to understand the effectiveness of each COTS device and the validity of the data collected. We also aim to identify which physiological and cognitive phenomena collected by wearable devices are the most relatable to overall health and task performance in extreme environments, and of these ascertain which markers provide the earliest yet reliable indication of health decline. Finally, we analyze the data for significant differences between civilians’ and warfighters’ markers and the relationship to performance. This is a study funded by the Defense Threat Reduction Agency (DTRA, Project CB10359) and the University of New Mexico (The main portion of the R2R WATCH study is funded by DTRA. UNM is currently funding all activities related to bloodwork. DTRA, Project CB10359; SAND2017-1872 C). This paper describes the experimental design and methodology for the first year of the R2R WATCH project.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin

Abstract not provided.

Divis, Kristin; Abbott, Robert G.; Avina, Glory E.; Matzen, Laura E.

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin; Armenta, Mika

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin

Abstract not provided.

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

Haass, Michael J.; Wilson, Andrew T.; Matzen, Laura E.; Divis, Kristin

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.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin; Armenta, Mika; McNamara, Laura A.

Abstract not provided.

Speed, Ann S.; Silva, Austin R.; Trumbo, Derek T.; Trumbo, Michael C.; Stracuzzi, David J.; Warrender, Christina E.; Divis, Kristin

Abstract not provided.

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

Speed, Ann S.; Silva, Austin R.; Trumbo, Derek T.; Stracuzzi, David J.; Warrender, Christina E.; Trumbo, Michael; Divis, Kristin

The Transportation Security Administration has a large workforce of Transportation Security Officers, most of whom perform interrogation of x-ray images at the passenger checkpoint. To date, TSOs on the x-ray have been limited to a 30-min session at a time, however, it is unclear where this limit originated. The current paper outlines methods for empirically determining if that 30-min duty cycle is optimal and if there are differences between individual TSOs. This work can inform scheduling TSOs at the checkpoint and can also inform whether TSOs should continue to be cross-trained (i.e., performing all 6 checkpoint duties) or whether specialization makes more sense.

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

Trumbo, Michael C.; Matzen, Laura E.; Silva, Austin R.; Haass, Michael J.; Divis, Kristin; Speed, Ann S.

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.