Behavioral representation in modeling and simulation: Introduction to CMOT special issue—BRiMS 2009

Abstract

1 June 2011 The BRiMS Society and Conference (Behavioral Representation in Modeling and Simulation (BRiMS, brimsconference.org) promote cross-disciplinary communication for basic and applied scientific research in the realm of modeling and simulation of human behavior, with a particular emphasis on defense government-related tasks and behavior. Thus, the BRiMS conference brings together scientists, engineers, practitioners, and application users to discuss modeling behavior ranging from that of individuals to the behavior of whole societies, their interactions, and their implications. For a few days each year, we get to meet to share ideas and experiences, identify gaps in current capabilities, discuss new directions, highlight promising technologies, and showcase applications. This special issue is similar to our previous special issue (Kennedy, Ritter, & Best, 2010) in that it includes four papers based on the award winning conference papers of the 2010 annual conference, reviewed and extended to journal article length. The papers include a new model integrating top-down and bottom-up factors affecting visual target acquisition (Jungkunz & Darken, 2011), the application of a statistical methodology to modeling psychological and cognitive impacts of protective clothing (Mueller et al., 2011), the use of fuzzy cognitive mapping techniques to model situation awareness (Jones et al., 2011), and the challenge of exploration and optimization of cognitive models (Moore, 2011). Overall, they represent how the 2010 conference addressed modeling from small-scale models, for example, predicting eye movements, to large-scale parameter exploration using high-performance computing facilities. At the small-scale end of the range of these papers, Patrick Jungkunz and Christian Darken present models of eye movements during target acquisition in military simulations. They found that a relevance map performed better than a salience map and that scene locations that are semantically relevant predict human eye fixations better than just visual salience. However, the combined approach was not statistically better than the relevance map alone. Their work identified semantically relevant scene locations as the most significant factor in predicting eye fixations and they developed a novel method that supports direct extraction of that information directly from the simulation environment. This work is important for simulations because they too often assume that models can see everything (Ritter, Baxter, Jones, & Young, 2000). Not seeing everything or seeing things that are trying not to be seen is important in adversarial simulations (Best & Gerhart, 2011). The second paper concerns the predicting the impact of the psychological and cognitive stressors on performance. Shane Mueller, Benjamin Simpkins, George Anno, Corey Fallon, Gene McCellan, and Owen Price of Applied Research Associates, report on the