Michael Farry

Visual Representations of Meta-Information

We conducted two studies that investigated display characteristics related to color (hue, saturation, brightness, and transparency) and contrast with a background for displaying information qualifiers (termed meta-information) such as uncertainty, age, and source quality. Level of detail (or granularity) of the meta-information and task demands were also manipulated. Participants were asked to rank and rate colored regions overlaid on different map backgrounds based on the level of meta-information the regions displayed. Results from Study 1 indicated that participants could appropriately rank and rate levels of meta-information across saturation, brightness, and transparency conditions, and results from Study 1 and Study 2 showed that the natural direction of ordering is complex and dependent on the relevance of different information to the task and the contrast of the overlay region with the background.

Comparing Uncertainty Visualizations for a Dynamic Decision-Making Task:

Supporting complex decision making requires conveying relevant information characteristics or qualifiers. The authors tested transparency and numeric annotation for displaying uncertainty about object identity. Participants performed a “missile defense” game in which they decided whether to destroy moving objects (which were either threatening missiles or nonthreatening birds and planes) before they reached a city. Participants were provided with uncertain information about the objects’ classifica-tions. Uncertainty was represented through the transparency of icons representing the objects and/or with numeric annotations. Three display methods were created. Icons represented the most likely object classification (with solid icons), the most likely object classification (with icons whose transparency represented the level of uncertainty), or the probability that the icon was a missile (with transparency). In a fourth condition, participants could choose among the representations. Icons either were or were not annotated with numeric probability labels. Task performance was highest when participants could toggle the displays, with little effect of numeric annotation. In conditions in which probabilities were available graphically or numerically, participants chose to engage objects when they were farther from the city and had a lower probability of being a missile. Results provided continued support for the use of graphical uncertainty representations, even when numeric representations are present.

Supporting tactical intelligence using collaborative environments and social networking

Modern military environments place an increased emphasis on the collection and analysis of intelligence at the tactical level. The deployment of analytical tools at the tactical level helps support the Warfighter’s need for rapid collection, analysis, and dissemination of intelligence. However, given the lack of experience and staffing at the tactical level, most of the available intelligence is not exploited. Tactical environments are staffed by a new generation of intelligence analysts who are well-versed in modern collaboration environments and social networking. An opportunity exists to enhance tactical intelligence analysis by exploiting these personnel strengths, but is dependent on appropriately designed information sharing technologies. Existing social information sharing technologies enable users to publish information quickly, but do not unite or organize information in a manner that effectively supports intelligence analysis. In this paper, we present an alternative approach to structuring and supporting tactical intelligence analysis that combines the benefits of existing concepts, and provide detail on a prototype system embodying that approach. Since this approach employs familiar collaboration support concepts from social media, it enables new-generation analysts to identify the decision-relevant data scattered among databases and the mental models of other personnel, increasing the timeliness of collaborative analysis. Also, the approach enables analysts to collaborate visually to associate heterogeneous and uncertain data within the intelligence analysis process, increasing the robustness of collaborative analyses. Utilizing this familiar dynamic collaboration environment, we hope to achieve a significant reduction of time and skill required to glean actionable intelligence in these challenging operational environments.

The Mutual Support Function Model A Cognitive Model for Intelligence Analysis Supporting Irregular Warfare

Modern intelligence analysis has evolved due to the focus on Irregular Warfare (IW) and the proliferation of network-centric environments. Given the ubiquity of those two themes in modern intelligence analysis, this paper seeks to provide a detailed cognitive model of intelligence analysis for IW, the Mutual Support Function Model (MSFM), based on the original Support Function Model (SFM) for intelligence analysis from (Elm et al., 2005). In addition to the three functions of Down Collect, Conflict and Corroboration, and Hypothesis Exploration from the original SFM, the MSFM considers two additional functions, Information Needs Management and Decision Selection. In addition to presenting this theoretical model, this paper also presents a discussion of how the model may be applied to operational environments through the development of tools. We also discuss strategies for introducing associated technology to the work environment in a suitable manner.

A Cross-Domain Approach to Designing an Unobtrusive System to Assess Human State and Predict Upcoming Performance Deficits

Many work environments are fraught with highly variable demands on cognitive workload, fluctuating between periods of high operational demand to the point of cognitive overload, to long periods of low workload bordering on boredom. When cognitive workload is not in an optimal range at either end of the spectrum, it can be detrimental to situational awareness and operational readiness, resulting in impaired cognitive functioning (Yerkes and Dodson, 1908). An unobtrusive system to assess the state of the human operator (e.g., stress, cognitive workload) and predict upcoming performance deficits could warn operators when steps should be taken to augment cognitive readiness. This system would also be useful during testing and evaluation (T&E) when new tools and systems are being evaluated for operational use. T&E researchers could accurately evaluate the cognitive and physical demands of these new tools and systems, and the effects they will have on task performance and accuracy. In this paper, we describe an approach to designing such a system that is applicable across environments. First, a suite of sensors is used to perform real-time synchronous data collection in a robust and unobtrusive fashion, and provide a holistic assessment of operators. Second, the best combination of indicators of operator state is extracted, fused, and interpreted. Third, performance deficits are comprehensively predicted, optimizing the likelihood of mission success. Finally, the data are displayed in such a way that supports the information requirements of any user. The approach described here is one we have successfully used in several projects, including modeling cognitive workload in the context of high-tempo, physically demanding environments, and modeling individual and team workload, stress, engagement, and performance while working together on a computerized task. We believe this approach is widely applicable and useful across domains to dramatically improve the mission readiness of human operators, and will improve the design and development of tools available to assist the operator in carrying out mission objectives. A system designed using this approach could enable crew to be aware of impending deficits to aid in augmenting mission performance, and will enable more effective T&E by measuring workload in response to new tools and systems while they are being designed and developed, rather than once they are deployed.

Combining human knowledge and operational data to promote detailed and effective reporting

There are many task-related factors that drive the complexity and diversity of submarine operations during a mission, including knowing the correct time to make periscope observations, estimating the correct sea state, and being aware of the proximity of contacts. In addition, there are unpredictable events and circumstances, including equipment failures, environmental factors, and adversary actions, that affect the operations success or failure. After operations are complete, commanders are tasked with recounting and reporting events of interest. Commanders are asked to recall details of critical incidents, when their perceptual and cognitive resources are likely to be over-tasked, resulting in less accurate recall. In most operations, there is little objective data collection to back up those recollections, especially for critical incidents that had the potential to cause catastrophes but did not. However, instances where catastrophes are narrowly avoided offer valuable teaching moments for crewmembers. Collecting and visualizing objective performance data within a mission reconstruction tool can help commanders account for actual actions and decisions for the purpose of reporting, and also enables resilient planning and optimal execution of future tasks, because commanders are able to analyze alternative courses of action (COAs) and their trade-offs. Most importantly, having a more comprehensive analysis tool can enable more accurate and thorough training, thus improving the mission performance and operational safety of future submarine operations and performance.

Trust metrics and results for social media analysis

Social media has changed the information landscape for a variety of events including natural disasters, demonstrations, and violent crises. During these events, people use a variety of social media, such as Twitter, to share information with the world. Given the massive amount of data generated, it is difficult to identify the valuable information in a sea of noise. In this study, we focus on a universal contributing factor to information value---trust---which is analyzed in two steps. Leveraging the theory of trust in information, a set of metrics is developed that focus on trusted relationships and behavioral indicators of trustworthiness within social media. Second, these trust metrics are tested on an anonymized data set and their results presented.

Incrementally Formalizing Graphical Models for Collaborative Operations Research

To address issues in enterprise systems, operations research (OR) analysts need to be able to understand, codify, and communicate various aspects of the issues, such as the environment’s conditions and their relationships. Models are a natural way to capture that information, but they must be understandable to a variety of stakeholders involved in improving the enterprise. At the same time, the large amount of available information, such as event history and weather data, can easily overload analysts. To help analysts cope with data overload, it is useful for models to be accessible to computational tools that can provide data processing and visualization capabilities. To support both of those goals simultaneously, we describe an approach that supports the elicitation of qualitative insight from operations researchers and other relevant stakeholders and also provides avenues for computer software to perform semantic labeling and quantitative data processing. This approach directly supports an iterative OR process that satisfies the needs of multiple stakeholder communities, enabling initial qualitative relationships and hypotheses to be further investigated and justified with data-driven conclusions. Building on our previous experiences in knowledge acquisition and quantitative analysis, this paper outlines a new integrated workflow and a collection of graphical representation concepts for operations research and similar domains.

Visualizations and interaction methods for resilient submarine decision support

Submarine commanders must make decisions rapidly to carry out increasingly complex missions. However, the rate of information delivery has outpaced the capacity of the command and control systems that prioritize and filter it. Technology could help commanders filter through data to make decisions, but this decision support must be carefully engineered to support the development of resilient courses of action (COAs). This paper details our experiences applying resilience engineering to submarine decision support. It focuses on designing two features that are essential for a resilient decision support system: (1) user interaction with a decision support system, which blends the user’s operational insights with the technical aspects of the decision support system; and (2) visual representations of trade-offs. The paper ends with a discussion of the lessons learned from this work and a set of recommendations for designing decision support systems.

Perception of Meta-Information Representation: A Psychophysical Approach

Previous research has identified many effective methods to visualize different types of meta-information, or information qualifiers; however, these methods are often incorporated without understanding how the graphical codes are perceived and how the encoded information is interpreted by display users. This results in display designers selecting graphical codes to represent meta-information without empirical evidence to determine the appropriateness of these selections. To help address this lack of guidance, this paper presents a systematic study of how people perceive two graphical codes (saturation and opacity) and relate those codes to different types of meta-information. Results were generated using psychophysical scaling methods, and provide visualization designers with a means to more appropriately design meta-information representations.