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.

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' classifications. 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.

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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.

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