Search results

Army and Joint Transformation initiatives in U.S. national defense (Shinseki, 2000) underscore the need to plan and meet mission requirements for individual soldier and small unit deployment in “close fight” scenarios (e.g. close combat, direct fire, complex terrain). This has focused interest and attention on the need for improved individual human performance research data, models, and high-fidelity simulations that can accurately represent human behavior in individual and small unit settings. New strategies are now needed to bridge the gap between performance outcome assessment and prediction (see also Pew & Mavor, 1998). The purpose of this chapter is to address epistemological and methodological issues that are fundamentally relevant to this goal.

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.

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.

Baron et al. briefly summarized the history of human performance modeling¹ (HPM) in their 1990 review. The application of control theory to aircraft simulations and the development of task network models stimulated the development of methods to represent the human contribution to system dynamics. These groundbreaking efforts first identified the manifold difficulties associated with the simulation of human performance in military settings, and many of these difficulties remain matters of contemporary concern. The technical challenges associated with the representation of human performance have endured, and military applications continue to be a major driver of interest in HBR. The expense and various difficulties associated with laboratory research, field studies, and operational tests have pushed modeling and simulation to center stage as an affordable alternative to empirical studies. Simulation is now an essential component of military force development, operational planning, engineering development and acquisition, and 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.

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.