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This volume was initiated by the U.S. Army Medical Research and Materiel Command (USAMRMC) Military Operational Medicine Research Directorate in response to a specific need to organize research on behavioral modeling in the Department of Defense. In particular, it was necessary to address relevant issues concerning the metrics, methods, and presuppositions of scientific inquiry in all aspects of human performance research and modeling, with specific focus on individual and small unit performance. The objective of this volume is to bridge the gap between outcome assessment and prediction in military performance literature, and thus to advance the utility and development of individual human performance research, modeling, and simulation.

The history of simulated warfare is nearly as old as warfare itself, dating back at least 5000 years to the Chinese war game known as Wei-Hai. Also the game we now know as chess evolved from a war game originally played in India as early as 500AD (see also Smith, 1998). Throughout military history, the art of warfare has been trained and practiced through the use of artificial tabletop landscapes, miniaturized soldiers, and tactical and strategic gaming rules designed to challenge the minds of military leaders.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Each of the four objectives can be applied within the military training environment. Military training often requires that soldiers achieve specific levels of performance or proficiency in each phase of training. For example, training courses impose entrance and graduation criteria, and awards are given for excellence in military performance. Frequently, training devices, training media, and training evaluators or observers also directly support the need to diagnose performance strengths and weaknesses. Training measures may be used as indices of performance, and to indicate the need for additional or remedial training.

Pew and Mavor (1998) called for an integrative representation of human behavior for use in models of individual combatants and organizations. Models with integrated representation of behavior have only been achieved at rudimentary levels according to those performing the studies (e.g. Pew & Mavor, 1998; Tulving, 2002) and those building the models (e.g. Warwick et al., 2002). This chapter will address aspects of cognitive performance that are important to incorporate into models of combat based on acceptance of theory, strength of empirical data, or for other reasons such as to bridge gaps where incomplete knowledge exists about cognitive behavior and performance. As a starting point, this chapter will assess which of Pew and Mavor’s recommendations are still appropriate as determined by a review of selected literature on cognition and its representation. We will also provide some review and extensions of key literature on cognition and modeling and suggest a way ahead to close the remaining gaps. Different aspects of cognition are described with recent findings, and most are followed by an example of how they have been represented in computer models or a discussion of challenges to their representation in modeling.

This chapter provides an overview of the Department of Defense (DoD) laboratory structure to help equipment designers, modelers, and manufacturers determine where research, testing programs, or relevant findings can be found. The chapter includes a discussion of the performance measures and metrics typically used in DoD laboratories and concludes by considering the current state-of-the-art as well as the state-of-the-possible for human performance measurement.

Human performance, particularly that of the warfighter, has been the subject of a large amount of research during the past few decades. For example, in the Medline database of medical and psychological research, 1,061 papers had been published on the topic of “military performance” as of October 2003. Because warfighters are often pushed to physiological and mental extremes, a study of their performance provides a unique glimpse of the interplay of a wide variety of intrinsic and extrinsic factors on the functioning of the human brain and body. Unfortunately, it has proven very difficult to build performance models that can adequately incorporate the myriad of physiological, medical, social, and cognitive factors that influence behavior in extreme conditions. The chief purpose of this chapter is to provide a neurobiological (neurochemical) framework for building and integrating warfighter performance models in the physiological, medical, social, and cognitive areas. This framework should be relevant to all other professionals who routinely operate in extreme environments. The secondary purpose of this chapter is to recommend various performance metrics that can be linked to specific neurochemical states and can accordingly strengthen and extend the scope of the neurochemical model.

Understanding how health status and physiological factors affect performance is a daunting task. This chapter will discuss physiological, behavioral, and psychological factors that influence or determine the capacity to fight, and will consider metrics that can be used to measure their status. The premise of this discussion is that there is a set of physiological and psychological factors that intimately affect performance and that the relative contribution of these variables is individually unique. These factors can be identified and assessed, and are amenable to modification. A fuller understanding of these variables can lead the effort to maintain and improve performance in the adverse and challenging environments of military operations.