Publications

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In this paper, we argue that information theoretic measures may provide a robust, broadly applicable, repeatable metric to assess how a system enables people to reduce high-dimensional data into topically relevant subsets of information. Explosive growth in electronic data necessitates the development of systems that balance automation with human cognitive engagement to facilitate pattern discovery, analysis and characterization, variously described as "cognitive augmentation" or "insight generation." However, operationalizing the concept of insight in any measurable way remains a difficult challenge for visualization researchers. The "golden ticket" of insight evaluation would be a precise, generalizable, repeatable, and ecologically valid metric that indicates the relative utility of a system in heightening cognitive performance or facilitating insights. Unfortunately, the golden ticket does not yet exist. In its place, we are exploring information theoretic measures derived from Shannon's ideas about information and entropy as a starting point for precise, repeatable, and generalizable approaches for evaluating analytic tools. We are specifically concerned with needle-in-haystack workflows that require interactive search, classification, and reduction of very large heterogeneous datasets into manageable, task-relevant subsets of information. We assert that systems aimed at facilitating pattern discovery, characterization and analysis - i.e., "insight" - must afford an efficient means of sorting the needles from the chaff; and simple compressibility measures provide a way of tracking changes in information content as people shape meaning from data.

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This summary of PANTHER Human Analytics work describes three of the team's major work activities: research with teams to elicit and document work practices; experimental studies of visual search performance and visual attention; and the application of spatio-temporal algorithms to the analysis of eye tracking data. Our intent is to provide basic introduction to the work area and a selected set of representative HA team publications as a starting point for readers interested our team's work.

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In this study, eye tracking metrics and visual saliency maps were used to assess analysts' interactions with synthetic aperture radar (SAR) imagery. Participants with varying levels of experience with SAR imagery completed a target detection task while their eye movements and behavioral responses were recorded. The resulting gaze maps were compared with maps of bottom-up visual saliency and with maps of automatically detected image features The results showed striking differences between professional SAR analysis and novices in terms of how their visual search patterns related to the visual saliency of features in the imagery. They also revealed patterns that reflect the utility of various features in the images for the professional analysts These findings have implications for system design andfor the design and use of automatic feature classification algorithms.

The evolution of exquisitely sensitive Synthetic Aperture Radar (SAR) systems is positioning this technology for use in time-critical environments, such as search-and-rescue missions and improvised explosive device (IED) detection. SAR systems should be playing a keystone role in the United States' Intelligence, Surveillance, and Reconnaissance activities. Yet many in the SAR community see missed opportunities for incorporating SAR into existing remote sensing data collection and analysis challenges. Drawing on several years' of field research with SAR engineering and operational teams, this paper examines the human and organizational factors that mitigate against the adoption and use of SAR for tactical ISR and operational support. We suggest that SAR has a design problem, and that context-sensitive, human and organizational design frameworks are required if the community is to realize SAR's tactical potential.

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In this study, eye tracking metrics and visual saliency maps were used to assess analysts' interactions with synthetic aperture radar (SAR) imagery. Participants with varying levels of experience with SAR imagery completed a target detection task while their eye movements and behavioral responses were recorded. The resulting gaze maps were compared with maps of bottom-up visual saliency and with maps of automatically detected image features The results showed striking differences between professional SAR analysis and novices in terms of how their visual search patterns related to the visual saliency of features in the imagery. They also revealed patterns that reflect the utility of various features in the images for the professional analysts These findings have implications for system design andfor the design and use of automatic feature classification algorithms.

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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.

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