The Role of Shared Mental Models in Team Coordination Crew Resource Management Skills of Mutual Performance Monitoring and Backup Behaviors

The purpose of Crew Resource Management (CRM) is to improve flight crew coordination in multipiloted cockpits and in turn increase aviation flight safety. One aspect of CRM team coordination is the ability for flight crews to monitor each other properly and provide the appropriate backup if necessary. The author explores the role of shared mental models among Coast Guard rotary wing cockpit flight crews and their influence on monitoring and backup behaviors during nighttime overwater flight maneuvers. Using the Coast Guard’s MH-65 Operational Flight Trainer located at the Coast Guard Aviation Training Center in Mobile, Alabama, cockpit flight crews flew automated and manual instrument takeoff (ITO) maneuvers. Coast Guard CRM subject matter experts observed the interaction of the cockpit flight crews judging the level of mutual performance monitoring and backup behaviors during the ITO maneuvers. Using a repeated measures design, the researcher investigated the relationship and interaction between ITO maneuver shared mental model, type of ITO maneuver, and pilot flight time on cockpit flight crew monitoring and backup behaviors. Findings indicate a significant relationship between cockpit automation and levels of mutual performance monitoring and backup behaviors in cockpit flight crews

Critical Team Composition Issues for Long-Distance and Long-Duration Space Exploration: A Literature Review, an Operational Assessment, and Recommendations for Practice and Research

Prevailing team effectiveness models suggest that teams are best positioned for success when certain enabling conditions are in place (Hackman, 1987; Hackman, 2012; Mathieu, Maynard, Rapp, & Gilson, 2008; Wageman, Hackman, & Lehman, 2005). Team composition, or the configuration of member attributes, is an enabling structure key to fostering competent teamwork (Hackman, 2002; Wageman et al., 2005). A vast body of research supports the importance of team composition in team design (Bell, 2007). For example, team composition is empirically linked to outcomes such as cooperation (Eby & Dobbins, 1997), social integration (Harrison, Price, Gavin, & Florey, 2002), shared cognition (Fisher, Bell, Dierdorff, & Belohlav, 2012), information sharing (Randall, Resick, & DeChurch, 2011), adaptability (LePine, 2005), and team performance (e.g., Bell, 2007). As such, NASA has identified team composition as a potentially powerful means for mitigating the risk of performance decrements due to inadequate crew cooperation, coordination, communication, and psychosocial adaptation in future space exploration missions. Much of what is known about effective team composition is drawn from research conducted in conventional workplaces (e.g., corporate offices, production plants). Quantitative reviews of the team composition literature (e.g., Bell, 2007; Bell, Villado, Lukasik, Belau, & Briggs, 2011) are based primarily on traditional teams. Less is known about how composition affects teams operating in extreme environments such as those that will be experienced by crews of future space exploration missions. For example, long-distance and long-duration space exploration (LDSE) crews are expected to live and work in isolated and confined environments (ICEs) for up to 30 months. Crews will also experience communication time delays from mission control, which will require crews to work more autonomously (see Appendix A for more detailed information regarding the LDSE context). Given the unique context within which LDSE crews will operate, NASA identified both a gap in knowledge related to the effective composition of autonomous, LDSE crews, and the need to identify psychological and psychosocial factors, measures, and combinations thereof that can be used to compose highly effective crews (Team Gap 8). As an initial step to address Team Gap 8, we conducted a focused literature review and operational assessment related to team composition issues for LDSE. The objectives of our research were to: (1) identify critical team composition issues and their effects on team functioning in LDSE-analogous environments with a focus on key composition factors that will most likely have the strongest influence on team performance and well-being, and 1 Astronaut diary entry in regards to group interaction aboard the ISS (p.22; Stuster, 2010) 2 (2) identify and evaluate methods used to compose teams with a focus on methods used in analogous environments. The remainder of the report includes the following components: (a) literature review methodology, (b) review of team composition theory and research, (c) methods for composing teams, (d) operational assessment results, and (e) recommendations

Team Learning, Development, and Adaptation

[Excerpt] Our purpose is to explore conceptually these themes centered on team learning, development, and adaptation. We note at the onset that this chapter is not a comprehensive review of the literature. Indeed, solid conceptual and empirical work on these themes are sparse relative to the vast amount of work on team effectiveness more generally, and therefore a thematic set of topics that are ripe for conceptual development and integration. We draw on an ongoing stream of theory development and research in these areas to integrate and sculpt a distinct perspective on team learning, development, and adaptation

Crew Resource Management and Shared Mental Models: A Proposal

Crew Resource Management (CRM) training focuses on situation awareness, communication skills, teamwork, task allocation, and decision making. More recently, an interest in cognitive skill is beginning to appear in relation to CRM. One aspect of cognitive skill that has been examined in a variety of team domains is the notion of overlapping or shared mental models among teammates. While a growing amount of evidence on the relationship between shared mental models and team performance exists, only limited research has focused on the role that shared mental models have-in crew resource management. The purpose of this paper is to provide CRM researchers and practitioners an understanding of the shared mental model construct and the role of shared mental models in team performance, as well as to encourage additional research on this topic within the aviation domain

EXPLORING THE POTENTIAL OF A MACHINE TEAMMATE

Artificial intelligence has been in use for decades. It is already deployed in manned formations and will continue to be fielded to military units over the next several years. Current strategies and operational concepts call for increased use of artificial-intelligence capabilities across the defense enterprise—from senior leaders to the tactical edge. Unfortunately, artificial intelligence and the warriors that they support will not be compatible "out of the box." Simply bolting an artificial intelligence into teams of humans will not ensure success. The Department of Defense must pay careful attention to how it is deploying artificial intelligences alongside humans. This is especially true in teams where the structure of the team and the behaviors of its members can make or break performance. Because humans and machines work differently, teams should be designed to leverage the strengths of each partner. Team designs should account for the inherent strengths of the machine partner and use them to shore up human weaknesses. This study contributes to the body of knowledge by submitting novel conceptual models that capture the desired team behaviors of humans and machines when operating in human-machine teaming constructs. These models may inform the design of human-machine teams in ways that improve team performance and agility.NPS_Cruser, Monterey, CA 93943Outstanding ThesisMajor, United States Marine CorpsMajor, United States Marine CorpsApproved for public release. Distribution is unlimited

A voyage to Mars: A challenge to collaboration between man and machines

A speech addressing the design of man machine systems for exploration of space beyond Earth orbit from the human factors perspective is presented. Concerns relative to the design of automated and intelligent systems for the NASA Space Exploration Initiative (SEI) missions are largely based on experiences with integrating humans and comparable systems in aviation. The history, present status, and future prospect, of human factors in machine design are discussed in relation to a manned voyage to Mars. Three different cases for design philosophy are presented. The use of simulation is discussed. Recommendations for required research are given

Toward a Theory of Practical Drift in Teams

Practical drift is defined as the unintentional adaptation of routine behaviors from written procedure. The occurrence of practical drift can result in catastrophic disaster in high-reliability organizations (e.g. the military, emergency medicine, space exploration). Given the lack of empirical research on practical drift, this research sought to develop a better understanding by investigating ways to assess and stop the process in high-reliability organizations. An introductory literature review was conducted to investigate the variables that play a role in the occurrence of practical drift in teams. Research was guided by the input-throughput-output model of team adaptation posed by Burke, Stagl, Salas, Pierce, and Kendall (2006). It demonstrates relationships supported by the results of the literature review and the Burke and colleagues (2006) model denoting potential indicators of practical drift in teams. Research centralized on the core processes and emergent states of the adaptive cycle; namely, shared mental models, team situation awareness, and coordination. The resulting model shows the relationship of procedure—practice coupling demands misfit and maladaptive violations of procedure being mediated by shared mental models, team situation awareness, and coordination. Shared mental models also lead to team situation awareness, and both depict a mutual, positive relationship with coordination. The cycle restarts when an error caused by maladaptive violations of procedure creates a greater misfit between procedural demands and practical demands. This movement toward a theory of practical drift in teams provides a conceptual framework and testable propositions for future research to build from, giving practical avenues to predict and prevent accidents resulting from drift in high-reliability organizations. Suggestions for future research are also discussed, including possible directions to explore. By examining the relationships reflected in the new model, steps can be taken to counteract organizational failures in the process of practical drift in teams

Team Learning: A Theoretical Integration and Review

With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed

Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems

Collective Failure: The Emergence, Consequences, and Management of Errors in Teams

The goal of the current chapter is to examine the emergence, consequences, and management of errors in teams. We begin by discussing the origin and emergence of errors in teams. We argue that errors in teams can originate at both the individual and collective level and suggest this distinction is important because it has implications for how errors propagate within a team. We then consider the paradoxical effects of errors on team performance and team learning. This discussion highlights the importance of error management in teams so that errors can prompt learning while at the same time mitigating their negative consequences. Thus, we focus significant attention on the challenge of error prevention and error management in teams and highlight numerous factors that can influence these processes. We conclude the chapter with a discussion of important research gaps and outline an agenda for future work in this area