Team Cognition: Coordination across Individuals and Machines

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Team Cognition: Coordination across Individuals and Machines  

Patricia Bockelman Morrow and Stephen M. Fiore

The Oxford Handbook of Cognitive Engineering

Edited by John D. Lee and Alex Kirlik

Print Publication Date: Feb 2013Subject: Psychology, Cognitive PsychologyOnline Publication Date: May 2013DOI: 10.1093/oxfordhb/9780199757183.013.0012

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Abstract and Keywords

Team cognition emerges as both the process and product of effective collaboration and coordination. This chapter examines the essential vocabulary for framing team cognition as an interdisciplinary endeavor. A historical review of major contributors to contemporary team cognition theories is provided to establish the field’s place in the larger stories of psychology, computer intelligence, and learning. Mental model representation methodologies provide bridges between theory and practice.

Keywords: team cognitionmental modelscollaborationinterdisciplinaritycognitive engineering

Introduction to Team Cognition

Cognitive science has had an important influence on a number of fields, including engineering and systems design, thus making an impact on our understanding of how to improve organizational and human performance. Importantly, this application has produced a bidirectional influence in the theories that cognitive scientists have produced to understand complex cognition and human performance in team contexts. This influence has helped to move the field such that a melding of human and machine cognition is emerging—an important theoretical blend that is producing a more holistic understanding of cognition in context. In this chapter, we review some of the foundational elements of team cognition theory and discuss the historical basis from which they developed. We discuss these in the context of a theory of team cognition as both process and product of interaction. We first establish a working vocabulary with which the topics may be discussed. The interdisciplinary roots of cognitive science make it especially important to clarify terms that varied fields may use with other intended meanings. We then review some of the theoretical shifts during the 20th century that set in motion the current trends in team cognition. We follow this with a discussion of a team cognitive tool that we suggest provides an example of how to bridge theory and practice. Our overarching goal is to discuss a sampling of the historical antecedents to team cognition as well as discuss important developments in the fields addressing complex collaborative cognition. Through all of this is the theme of the importance of understanding the relation between coordinated cognition and context, whether that context consists of additional humans or machines.

For the cognitive engineer, this means a challenge to design systems for nested intelligences. If teams of people create an emergent cognitive entity, and teams can be found within the context of larger teams, then a cognitive engineer will design for the human and for the layers of cognitive entities found at each team level. In this chapter, we will describe the historical context of team cognition, with one goal being the articulation of team cognition as a distinct form of cognition, one that demands its (p. 201) own tools and techniques from system designers and managers. It is a type of cognition that is inseparable from the humans that compose a team, but distinct in its own cognitive demands. To illustrate the challenges of cognitive engineering for teams, we describe the processes and products related to shared mental models (SMM). While SMM are by no means the only factor of team cognition that is not a critical component of individual cognition, SMM provide distinct theoretical and design challenges for cognitive engineers. We will close with a look at the future directions and research questions that directly relate to cognitive engineering.

Foundations of Team Cognition

The interdisciplinary nature of cognitive science requires caution as we use terms that some of the adjacent and complementary disciplines may apply with different implications. Therefore, we first clarify some of the nomenclature framing team cognition theory. We then review key historical contributions in basic and applied research that have brought us to the contemporary models for understanding the factors that contribute to collaborative cognitive processes and outcomes.

Coordinating the Nomenclature

As team cognition emerges as an increasingly distinct area of inquiry unto itself, it is imperative that the nomenclature becomes more established in its application. One important distinction that has risen from the academic refinery is that between group and team. Though there are a multitude of team types, and studies will often focus on specific aspects of teams, there are well-established components of teams that distinguish them from groups. A “team” is made from interdependent individuals who are viewed collectively, share responsibility for performance and achievement, and are embedded in organizational contexts (Cohen & Bailey, 1997; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000; Hackman et al., 2000; Katzenbach & Smith, 1993; Salas & Fiore, 2004). Conversely, the term “group” may be used more generically, implying a commonality but not necessarily identifying shared constructs—which becomes vitally consequential when the term “cognition” is added to “team.” Cognitive processes arise during communications and interactions, becoming both meaningful and measurable in the forms of process and product (Fiore & Salas, 2004). Although there are numerous cognitive constructs at play in groups, the individuals are still largely independent. Teams, however, depend upon one another and, by definition, are interdependent (see Saavedra, Earley, & Van Dyne, 1993).

These distinctions led to the succinct definition of teams as “interdependent collections of individuals who share responsibility for specific outcomes for their organizations” (Sundstrom, DeMeuse, & Futrell, 1990, p. 120), or as “two or more people who interact dynamically, interdependently and adaptively toward” a shared goal (Salas, Dickinson, Converse, & Tannenbaum, 1992, p. 4). We embrace these definitions, noting the particular importance of interdependenceshared goals, and collective adaptivity, as it is from these factors that we can see how the construct of team cognition can help us better explore distinct theoretical principles.

The cognitive and social sciences have also identified an important quandary unique to the problems of team cognition. Decades ago, attempting to address coordination, researchers identified a phenomenon that Steiner (1972) referred to as process loss—coordination decrements that led to performance below team potential. If failure to support coordination results in team failure, it serves to reason that coordination lies at the heart of team success. The factors that enhance coordination—such as communication, shared knowledge, and team member awareness—are tied together in team cognition in ways in which Fiore and Salas (2004) equated to the binding problem. In neuroscience, the binding problem speaks to the conceptualization of the myriad of coordinated neurological impulses that must coalesce to generate synchronized processes. Fiore and Salas (2004) suggested that we can similarly conceptualize team cognition as the mechanism that “fuses the multiple inputs of a team into its own functional entity” (p. 237). Just as neural firings synchronize, successful team performance requires analogously coordinated actions from team members. Thus, cognitive and behavioral components must bind to produce desired outcomes in ways that can be recognized and assessed via coordination terms. Consequently, the binding problem becomes representative of both the neurocognitive and the team cognitive. Further expanding on the critical importance of coordination, Fiore and Salas (2006) argued for a more foundational understanding of team coordination, asserting that it is unique from “collaboration” or “cooperation,” terms sometimes used interchangeably in theories of team cognition. They note that collaboration and cooperation simply mean to “work together,” but the concept of coordination most cogently captures (p. 202) what we mean by effective teamwork. Considering this in light of the literature on teamwork, perhaps the most faithful definition of coordination can be found in theorizing by Marks and colleagues. Specifically, they view team coordination as “orchestrating the sequence and timing of interdependent actions” (Marks, Mathieu, & Zaccaro, 2001, p. 363). As the etymological origins of coordination show that it was derived from three distinct concepts (i.e., “arrange,” “order,” and “together”), we can see how the Marks et al. definition encompasses these origins and succinctly relates them to teams.

In addition to this foundational connection between team coordination and team cognition, Fiore and Salas (2004) illustrated how conceptualizations of team cognition fit under a general theme of awareness or communication. Specifically, team cognition research focused either on a type of awareness used to bind a team’s actions or on implicit/explicit communication as the means through which team cognition is developed or scaffolded in support of coordination. First, awareness in general, and shared awareness in particular, emerges as a key concept in team cognition research whereby researchers speak of the general need for shared awareness within teams. For example, team metacognition (Hinsz, 2004), mutual understanding of team knowledge and capabilities (e.g., Rentsch & Woehr, 2004), and computer-supported collaborative work (e.g., Gutwin & Greenberg, 2004) all emphasize awareness in the context of team cognition. Second, communication has been described as a “window to team cognition” (Cooke, Salas, Kiekel, & Bell, 2004) and may be the method whereby individual cognitive components actually become integrated within the team. It is through team processes such as communication that teams come to share or make common their awareness (cf. Fussell & Krauss, 1989).

More recently, Elias and Fiore (2012) engaged in a deeper analysis of the distinction between collaboration and coordination. They note that team cognition not only enables and facilitates team social cognition but also provides the context through which we can understand the constraints that scaffold a team’s interaction—a set of constraints that “endows behavior with meaning and purpose, with directedness and aim, and allows for anticipation precisely by narrowing the range of action” (p. 585). Their conceptual analysis suggests a system of frames inside frames in which collaboration occurs within, and because of, the constraints of coordination. Elias and Fiore (2012) argue that autonomy is inherent to collaboration, but that there is still subordination of the “part” to the larger team “whole.” Collaboration, therefore, consists of interaction and interdependence among autonomous individuals. But while coordination provides the constraints for team interaction, collaboration allows for the adaptive response to a team’s interdependencies. Implicit and explicit team processes, then, create the shared awareness necessary for collaboration and coordination. As such, in the context of team cognition, these are neither synonymous nor entirely separable constructs; rather, they are a necessary complement that creates effective teamwork.

This analysis of terminology was meant to clarify the meaning and relation of a set of foundational concepts. With these concepts as our stepping off point, we next discuss some of the historical antecedents that set the stage for the varied ways in which researchers have studied team cognition through the lenses of coordination, awareness, and communication.

Past as Prelude: Historical Underpinnings of Team Cognition

Team cognition as a distinct area of inquiry has risen as both melding and offshoot. Its history pulls from multidisciplinary considerations and collaborations that speak to this merger of complementary concepts. However, it also is distinct unto itself, contributing methods and philosophies that have afforded researchers the opportunities to consider problems of biological and artificial cognition. To better understand the basis for the approaches to team cognition, we provide a brief history of developments in applied psychology and related fields. Although only tangentially related, we suggest that this early work set the stage for the field of team cognition by demonstrating the value to theory and practice of connecting context to studies of cognition.

Though applied psychology may trace its roots to the early 20th century, it would take decades to move the field to a point where it truly examined contextually situated cognition. From observations made in the courts of law, Hugo Münsterberg called for the application of psychology to be used in real-world contexts (Benjamin, 2000). In other words, he encouraged his field to move beyond the theoretical findings in controlled laboratory settings to observe the psychological phenomena in the natural settings of human interaction. Throughout the subsequent decades, the opinions toward basic versus applied psychology ebbed and flowed. However, globally significant events, primarily World Wars I and II, would usher in an era where applied psychology not (p. 203) only was respected but also contributed to theory and practice in human performance.

The large-scale wars were contextualized in an industrial time, when the interactions of team members and machinery required strategic considerations. From this, researchers transcended the artificial barriers between basic and applied science, and effectively melded theory and practice in human performance (for a discussion, see Fiore, Salas, & Pavlas, 2009). In particular, World War I ushered in a complex industrialization of combat and, consequentially, new approaches to thinking about human performance. A new type of war fighter emerged, the pilot, inseparable in task from the machinery he operated, and coordinated in complex task goals with other pilots and ground troops. The aviators, with unique task and perception stressors, presented subjects for study in the military as researchers sought to understand and improve their performance (e.g., Hoffman & Deffenbacher, 1992; Meister, 1999).

Psychological measures were also being developed and applied for selection and assignment during World War I, and this continued into World War II (Katzell & Austin, 1992)—a practice that continues today. Although not described in the modern terminology of human cognition, this early work enhanced our understanding of cognition by setting the stage for how to train “skills” and their relation to “aptitudes.” Further, this work began to illustrate the importance of conceptualizing the relation between the job or task context and cognitive processes.

The interaction of humans with each other and with machines continued to gain attention from researchers following World War II as computers became important tools in not only military strategy but also domestic industry. Understanding how teams would work in context became a central concern, and the trends in the West would begin to converge with task theories developing in other countries. For example, researchers in the Soviet Union were developing complementary approaches to understanding humans in complex situations. Activity theory informed research on humans in their work environments by providing tools for looking at a person’s activity at both micro- and macro- levels (see Nardi, 1996). To activity theorists, context plays a central role, applying to internal goals and objects and simultaneously to external factors, thereby requiring multidisciplinary input. Artifacts, connections between humans and their experiences, are anthropological, historical, and sociological. These views set the stage for influential theories in the cognitive sciences, where ideas about “situated” and “distributed” cognition argued for the importance of context and collaboration to both human cognition as well as human-machine cognition (e.g., Clancey, 1997; Hutchins, 1995).

The final decades of the 20th century brought conspicuous shifts in cognitive research as the integration of technologies became the norm in everyday life. Researchers reassessed theories and frameworks, observing human interaction with sophisticated new equipment in complex environments, the likes of which had simply not existed in earlier times (e.g., aircraft cockpits, power plants, control systems). User-inspired engineering followed the newer foci, adjusting work theories into more holistic and useful cognitive task paradigms. From this, some theorists and practitioners began to realize the futility of trying to parse human from machine, recognizing that cognition had to be viewed across humans and their machines.

The evolution of the “task analysis” concept illustrates these developments. Earlier versions of task analysis, easily transposed to flowcharts where chains of events and consequences could be shown and anticipated, failed to fully capture the real-world mental and physical activities involved in decision making, especially in technology-rich and high-risk collaborative contexts. Cognitive task analysis (CTA) and team cognitive task analysis were developed as methods and techniques to inform decision making in these dynamic environments (see Rasmussen, 1985; Crandall, Klein, & Hoffman, 2006). CTA methods depended heavily on in-depth observations and interviews with experts (Crandall et al., 2006; Klein & Militello, 2001; Militello & Hutton, 1998). Expertise holds an important place in CTA, the assumption being that experts can provide copious insights to their essential knowledge, skills, and processes foundational to optimal performance. Though much of the theoretical literature arose simultaneously, it came from across the globe as more people in more places needed to work in harmony with advanced technologies and each other.

In sum, this brief review illustrates how a blend of basic and applied science supported the development of theories foundational to examination of human performance. These arose from a careful analysis of the work of not only individuals but also teams, carrying out complex responsibilities with and through sophisticated technologies. Understanding how humans interacted with each other and with their systems helped set the stage for theories of (p. 204) cognition in context. Important methods and theories for human performance testing (e.g., aptitude tests) and for system design (e.g., activity theory and CTA methods) arose from this work. But we turn next to the theoretical view that had the largest impact on research in cognition—the information processing approach to human cognition.

Human Information Processing Model of Cognition

The information processing model of cognition reigned solidly as the dominating theoretical approach to cognitive psychology in the post–WWII era. This model facilitated research and engineering because the computer metaphor assigned segments to cognitive processing—input, process, output (see Simon, 1978)—and was productively applied to theories about groups (Hinsz, Tindale, & Vollrath, 1997). In turn, these classifications of activity informed computer engineering by opening a potential “likeness” to the human mind, inspiring programmers to increase human-like interaction by modeling human linguistic and emotional responses as the product of input-based processes on elementary and aggregate levels. A complete review of the information processing approach is beyond the scope of this chapter. We therefore focus on its impact to theories of team cognition and the view of groups as information processers.

As in individuals, the information processing model is meant to capture a significant amount of the cognitive activity observed in groups and teams (e.g., Lord & Maher, 1990; Larson & Christensen, 1993; Levine, Resnick, & Higgins, 1993). Examining team cognition through the information processing model allowed researchers to expand the focus of study to include cognitive processes at the individual level and the group level, treating the group as a unit of cognitive study. This distinction is critical, as it moves beyond traditional examinations of social, contextual, or ecological cues as impacting individual minds and acknowledges the emergent and dynamic processes that occur in the collective (von Cranach, Ochsenbein, & Valach, 1986; Ickes & Gonzalez, 1994; Stasser & Dietz-Uhler, 2001).

In their influential paper that helped to cement the view of cognitive processing at the level of group, Hinsz, Tindale, and Vollrath (1997) applied a model for information processing asserting that “group-level information processing includes information, ideas, and cognitive processes that are shared, in that not only are they common among group members but also that the information, ideas, and cognitive processes are being shared (i.e., exchanged and transferred)” (p. 44). Their work applied that model to the group process by identifying the following components: processing objective, attention, encoding, storage, retrieval, processing workspace, output or response, and feedback. Those components have been explored in numerous studies involving groups and teams and, because of their influence on team cognition, we briefly review a subset of the ideas from Hinsz et al. In addition, for each of these we illustrate the practical relevance of these theoretical contributions.

First, the processing objective is the information embedded in a given context. The roles of members, diversity in perspective, procedures and governance, nature of the task, and even the instructions, influence the efficiency and efficacy of processing (Sherif, 1935; Hinsz, Tindale, & Vollrath, 1997; De Dreu, Nijstad, & van Knippenberg, 2008). The complexity of some modern team situations, such as aviation combat and nuclear facility management, demonstrates multiple processing objectives. From a practical standpoint, the consequential cognitive processing load of teams has become so great in those multiple-process situations (e.g., Johnston, Fiore, Paris, & Smith, in press) that research must examine how team cognition can be engineered with awareness of simultaneous or sequential objectives so that, for example, intelligent agents are organically and anticipatorily embedded in teams.

Following the thought of processing objectives, it is natural that attention is the immediate concatenation. Research has shown the human tendency for distraction within the numerous group dynamic, and attention problems manifest in numerous ways. For example, group members may distract one another, and individuals may be self-conscious or insecure and consequently self- (rather than task-) focused (Mullen, Chapman, & Peaugh, 1989). Aspects of information distribution among group members and components of interaction also have been shown to influence attention (Stewart & Stasser, 1998). At a practical level, such notions reconnect team cognition to its interdisciplinary roots and illustrate how cognitive engineering needs to be informed by the social sciences.

The group as information processor must also encode, or structure and interpret concepts, schema, and individual representations in shared models. Teams rely on encoding at two distinct yet inseparable levels of cognition. Obviously, team members must be able to represent all of the stages of task accomplishment, but they also need to collectively (p. 205) represent the task overall as well as the individual roles for meeting the goal (e.g., Cannon-Bowers, Salas, & Converse, 1993; Salas, Sims, & Burke, 2005). Encoding is intimately connected to the other stages of information processing, as it both influences, and is influenced by, the processing and attention arising during collaboration.

The notion of storage is well accepted as a computer-based concept, and, within collaborative contexts, it serves the same role. But, rather than manifesting in neatly organized files, humans tap into a variety of memory systems. When measuring simple storage capacity, groups have an advantage over individuals simply because capacity increases as the group size grows. Ideally, groups access this advantage via interpersonal communication to enhance performance and judgment. But a long line of research suggests that, despite a broader memory storage capability, the advantage does not always meet actualization (e.g., Hinsz, 1990; Stasser & Stewart, 1992; Stasser & Titus, 19851987). As an important illustration of the social interacting with the cognitive, when designing or organizing teams, it may be useful to consider the impact of minority voice on storage, as it can contribute to a broader base of options in decision making and avoid the “groupthink” behavior of team members possessing or expressing only common knowledge (Nemeth, 1986; Janis, 1982). Further, in a recent meta-analysis of information sharing within teams, Mesmer-Magnus and DeChurch (2009) found that factors such as task demonstrability fostered sharing, whereas others, like information distribution, inhibited it. As such, technologies that can attenuate the factors impacting information sharing are an important target for cognitive engineering.

Finally, critical to utilization of distributedly stored information is access. In an early discussion of this, Hinsz (1990) asserted an advantageous position for groups in the retrieval phase because of their multiple access points or triggers for memory retrieval. Further, members may recognize errors in another person’s recollection and may collectively be able to construct a more accurate aggregate account. Conversely, groups can create retrieval interference (e.g., Stroebe & Diehl, 1994; Basden et al., 1998). Thus, it is critical, in terms of engineering team cognitive systems, to leverage the benefits while addressing the challenges that develop in group retrieval settings.

This brief review of the groups as information processors model was meant to illustrate that, as engineers and computer scientists design for teams, they must consider the interactive nature of the group as it impacts processing, encoding, and retrieval. By taking into account individual and group level strategies and the positional dynamics emergent in the team setting, technology-mediated collaborative activity can be designed to mitigate process loss (cf. Steiner, 1972) and to produce the kind of coordination that fully leverages the promise of team cognition. We next turn to a discussion of theories of teamwork that have moved beyond the information processing view of team cognition.

From Cognition as Information Processing to Macrocognition

The information processing model would remain the primary means to understand human cognition until the mid-1970s. Despite its strengths as an approach for conceptualizing cognition, with this interpretation came numerous limitations (Hollnagel, 2002). This view considered cognition outside of context and, as such, complex cognitive activity was thought of as complex only because variables within the environment were so, as opposed to acknowledging that the processes themselves were very elaborate. As the information processing model is reductionist in nature, researchers conducted microcognitive research in controlled settings, seeking to reduce the cognitive phenomena to the smallest contributing components. Microcognition studied the mind as functions and processes of the individual so that group or team interaction was interpreted solely as the result of independent brain activity. The research concentrated on inquiries like serial or parallel attention, puzzle solving, and interpretation errors (Crandall et al., 2006). However, for the purpose of interpreting and understanding natural cognitive activity and team functions, microcognitive approaches are sorely limited.

An important development at this time was theory that distinguished between levels of process control, that is, analysis at the micro- and macro-operational levels (see Schraagen, Klein, & Hoffman, 2008; Woods & Roth, 1986). Out of this, macrocognition was offered as a theoretical lens through which to interpret complex cognition. Researchers in the area of naturalistic decision making adopted this view and expanded upon it to more fully describe complex cognition in natural settings (Hutton, Miller, & Thorsden, 2003; Klein, Klein, & Klein, 2000; Klein et al., 2003; Schraagen, Militello, Ormerod, & Lipshitz, 2008). Macrocognition suggested a way of developing “a framework for studying and understanding cognitive processes as they (p. 206) directly affect performance of natural tasks” [with representative macrocognitive functions described as] “decision-making, situation awareness, planning, problem detection, option generation, mental simulation, attention management, uncertainty management, expertise, and so forth” (Klein et al., 2000, p. 173).

Holding to this broader schema for examining cognition, Hollnagel (2002) regarded five aspects that support the macrocognitive approach. First, across natural and artificial cognitive systems, the process and product of cognition will be distributed. Second, cognition is not self-contained and finite, but a continuance of activity. Third, cognition is contextually embedded within a social environment. Fourth, cognitive activity is not stagnant, but dynamic. Last, artifacts aid in nearly every cognitive action. Importantly, these latter notions fit within emerging theories from cognitive science that similarly have argued for the importance of understanding externalized and embedded cognition (e.g., Clark, 2001; Clark & Chalmers, 1998).

Contexts like medical decision making illustrate the value of macrocognitive concepts such as distributed and embedded cognition and the use of cognitive artifacts as a part of the team’s cognition. For example, Nemeth and colleagues (20042006) analyzed the value of externalized cognitive artifacts such as schedules, lists, and display boards in medical decision making. They argued that such artifacts “mediate collective work … as a way to maintain an overview of the total activity … [and] are products of various work activities that are distributed in time and location” (2006, p. 728). Essentially, these forms of externalized cognition support assessment and planning, as well as coordination for contingencies and negotiation of resources. In the broader context of collaborative medical decision making, with its inherent uncertainties, these externalizations serve as “cognitive-aid structures” (e.g., Rao & Turoff, 2000) to reify the decision processes among collaborating experts.

Research in collaborative engineering domains is also illustrative of cognition of the more macrocognitive form. For example, software design and development and system administration all require complex collaborative problem solving. Further, teams ranging from somewhat homogeneous teams, such as in software development, to often heterogeneous teams, such as in systems administration, are all created to develop, manage, and maintain complex technological products or systems. These collaborative tasks consist of dynamic cognitive processes requiring diagnostic interrogation of some system and diagnostic questioning from an oftentimes ad hoc team. Haber (2005) referred to this as “group sense making” when he described problem definition and solution processes in systems administration. In an example illustrative of these macrocognitive processes, he states that a “problem existed due to interactions between the components of a very complicated system, and the experts on the different components needed to work together to understand the cause and find a solution. The overall strategy was a cycle of shared observations of the system in question, developing hypotheses as individuals, small groups, or the group as a whole, and implementing changes to attempt a fix” (p. 3). Maglio et al. (2003) similarly discuss computer systems administration from a perspective that fits within a macrocognitive frame and articulates the complex nature of the collaboration. They describe a requirement for developing common ground and the coordination of attention across a number of team members, ranging from engineers engaged in troubleshooting to technical support personnel to software application developers.

But social-cognitive factors also come into play in macrocognitive contexts. In a study of expert software teams, Sonnentag (2000) showed that experienced problem solvers place a high value on cooperation and engage in more work-related communication. Thus, a crucial factor is an emphasis on cooperation strategies because the work places high cognitive and social demands on system administrators. Specifically, these engineers have to “troubleshoot systems, making sense of millions of log entries by controlling thousands of configuration settings, and performing tasks that take hundreds of steps. The work also places high social demands on practitioners as systems administrators need organizational and interpersonal skills to coordinate tasks and collaborate effectively with others” (Barrett et al., 2004). Related to this, Sonnentag and Lange (2002) found that, among engineering and software development teams, a general knowledge of cooperation strategies, that is, what to do in situations requiring cooperative behavior, is related to better performance. Further, this research showed that cooperation is more valued by the experts than by the moderate-level performers. Because of this, experts engaged in higher amounts of work-related communication, helped their coworkers, and sought out feedback from coworkers (Sonnentag, 2000).

More recently, this notion of collaborative macrocognition has been elaborated upon to specifically (p. 207) address macrocognition in teams as a form of complex and coordinative cognition (Letsky, Warner, Fiore, & Smith, 2008; Warner, Letsky, & Cowen, 2005). Macrocognition in teams is defined as the internalized and externalized high-level mental processes employed by teams to create new knowledge during complex, collaborative problem solving (Letsky, Warner, Fiore, Rosen, & Salas, 2007). High-level, in this setting, encompasses the processes of combining, visualizing, and/or integrating information to resolve ambiguity and in support of the discovery of new knowledge.

In this context, macrocognition in teams is a particular instance of the more general area of team cognition research in that team cognition theory tends to emphasize coordinating actions among individuals. For example, research in team cognition might examine how team members sequence actions in service of meeting a team’s objectives. Macrocognition in teams focuses more on the knowledge work done by a team and how externalized knowledge and the creation of cognitive artifacts support this work (Fiore, Rosen, et al., 2010; Fiore, Smith-Jentsch, Salas, Warner, & Letsky, 2010). In this sense, knowledge work is defined as the transformation of data and informational inputs to build knowledge that enables the team to develop problem representations and candidate solutions for the problem at hand (Fiore, Elias, Salas, Warner, & Letsky, 2010). Although team cognition research does address “knowledge” in teams when discussing shared mental models and related forms of overlapping knowledge structures (e.g., Cannon-Bowers, Salas, & Converse, 1993; Marks, Zaccaro, & Mathieu, 2000; Mathieu, Heffner, Goodwin, Cannon-Bowers, & Salas, 2005; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000; Rentsch & Davenport, 2006; Salas & Fiore, 2004), as noted, its emphasis is more upon coordination processes and executing previously learned task procedures in familiar environments. Macrocognition in teams is distinguished from the broader area of team cognition research in that it does not involve selecting and executing procedures or rules. Rather, the focus is on understanding processes engaged by teams to generate new knowledge to solve particular problems in context (Fiore, Rosen, et al., 2010; Fiore, Smith-Jentsch, et al., 2010). More specifically, it is “the process of transforming internalized knowledge into externalized team knowledge through individual and team knowledge-building processes” (Fiore, Rosen, et al., 2010, pp. 204–205).

In sum, applying macrocognition theory to the study of teams helps researchers and engineers account for the interwoven influences on human performance beyond the individual members’ thoughts, perceptions, or activity. Macrocognitive studies have shifted psychology’s narrower focus from simple tasks with control groups to naturalistic studies that are more qualitative in nature than quantitative (Crandall et al., 2006). This has opened the door for interdisciplinary approaches to thinking about the diverse problems of team cognition, all in the service of improving our understanding of organizational productivity. We next delve deeper into a core element of team cognition, the shared mental model construct. We seek to address the question, “How do we do team cognition?” and we describe “process mapping” to illustrate how team cognition emerges as both process and product during process re-engineering. We describe its components and use it to connect shared mental model theory to theorizing on macrocognition in teams.

Mental Models and Team Cognition

From research on team process and performance, we have come to understand the key success factors for expert teams and the relationship between team cognition and team processes. Based upon a review of the teamwork literature, Salas and colleagues identified what it is that expert teams do best (for elaboration on these, see Salas, Rosen, Burke, Goodwin, & Fiore, 2006), and we next summarize a subset of the elements of expert teamwork most relevant to team cognition. As will be seen, team cognition benefits teams by helping them comprehend and deal with complex phenomena, predict performance, and aid in the production of a course of action (Cannon-Bowers & Salas, 2001).

First and foremost, research suggests that expert teams hold shared or compatible knowledge structures referred to as either shared mental models or transactive memory systems (see DeChurch & Mesmer-Magnus, 2010, for a review). Second, expert teams demonstrate collaborative learning and use that to adapt to changing situations (e.g., Edmonson et al., 2001). Related to learning, expert teams will often engage in preparatory and reflective activities to improve performance. Here they will anticipate performance needs as well as reflect upon their performance episodes (Smith-Jentsch, Zeisig, Acton, & McPherson, 1998). In line with the notion of shared mental models, expert teams manage expectations of their teammates by clearly understanding the roles and responsibilities necessary to (p. 208) meet team goals. For example, air traffic controllers adapt responsibilities during shifts to meet evolving workload conditions (La Porte & Consolini, 1991; Beauchamp, Bray, Eys, & Carron, 2002; Brun et al., 2005; Bliese & Castro, 2000). In further elaboration of shared knowledge, expert teams consist of members who have a clear understanding of their mission, vision, and goals (e.g., Castka, Bamber, Sharp, & Belohoubek, 2001; Pearce & Ensley, 2004). This shared knowledge also supports superior decision making and reduces errors. Communication becomes more efficient when members hold compatible knowledge; specifically, team members give and receive timely information (e.g., Orasanu, 1990; Patel & Arocha, 2001). Along these lines, they use this knowledge to identify relevant teamwork and task-work requirements. In this sense, expert teams balance task characteristics and workload with individual expertise, and work to alter their operating environment to optimize communication and coordination (Schaafstal, Johnston, & Oser, 2001). As can be seen, shared mental models are an important component of expert teamwork, essentially acting as the foundation from which effective team processes can be executed. Given this, we turn next to a more thorough explication of the shared mental model construct.

Shared Mental Models as an Organizing Construct

Shared mental models are clearly one of the more promising advancements in team cognition. We next connect theory and practice through a more thorough review of shared mental model theory and examination of a process re-engineering tool that may contribute to better team performance by both establishing and actually becoming a shared mental model. Further, we illustrate how this supports some of the core ideas within a theory of macrocognition in teams.

The team cognition literature identifies a set of factors that must be present to be considered a shared mental model (e.g., Cannon-Bowers, Salas, & Converse, 1993; Klimoski & Mohammed, 1994; Mathieu et al., 2000), and these facilitate explanation, description, and prediction to aid team performance. In this vein, Fiore and Schooler (2004) used this theoretical approach to argue for the following essential factors in collaborative problem solving: awareness of problem structure, understanding of the roles and skills that teammates contribute as they relate to the task, and awareness that all team members possess that problem structure knowledge. In the following section, we build on their assertion that the development of these components leads to more productive problem conceptualization processes and subsequent solution generation.

To the first component, a shared problem structure, Orasanu and Fischer (1992) propose that, “the degree to which a team establishes a shared mental model for a problem and the degree to which it is made explicit in communication, will determine the team’s effectiveness in coping with the problem” (p. 189). This shared problem structure provides team members with the benefits of overlapping and organized knowledge (Resnick, 1991). Whether declarative or procedural, the knowledge that concerns both the problem and the rules of decision making are included in this body of correlative knowledge (Cannon-Bowers et al., 1993).

The second component, understanding each team member’s skills and roles, helps team members use fully the potential contributions of each other (Fiore & Schooler, 2004). The main assumption of this component is that understanding the endowments and obligations of others decreases erroneous assumptions and directs specific task segments to the members most likely to succeed at them.

These explanatory aspects of shared mental models shape the “predictive” potential for team performance. The degree to which team members share an accurate and clear mental model, the more likely they are to perform successfully (Cannon-Bowers & Salas, 2001). Furthermore, the shared mental model gives members insight that can be used to avoid or fix potential problems. In this respect, the shared model can be self-diagnostic, predicting the outcome and indicating the pieces of task flow that would result in such an end.

In a recent meta-analysis of the team cognition literature, DeChurch and Mesmer-Magnus (2010) addressed the question of cognition’s value to team performance. They hypothesized that team cognition would be positively related to behavioral team process, team motivational states, and team performance. Via an analysis of over 60 experiments and nearly 4,000 teams, this analysis of team cognition research provides a clearer sense of what has been validated empirically and what yet needs to be explored further. They found that the effectiveness of a team is due primarily to “interaction processes and emergent states” that connect input and outcome. “Team cognition is an emergent state that refers to the manner in which knowledge important to team functioning is mentally organized, represented and distributed within the team and allows (p. 209) team members to anticipate and execute actions” (p. 2). They conclude that this emergence in teams can manifest itself in either shared mental models (i.e., knowledge held in common) or as transactive memory systems (i.e., knowledge distributed across members).

In application of the empirical evidence supporting the critical contributions of mental models and distributed knowledge, cognitive engineers and others who work to support complex collaborative processes must address the challenge of facilitating these emergent constructs to support team success. We turn next to a discussion of an example of an approach from process re-engineering that illustrates processes and products of interaction, along with cognitive emergence.

Process Mapping as Reification of the Shared Mental Model Concept

Process mapping is not a new tool in organizational knowledge management. It, along with various other techniques including flowcharts and diagrams, has been used by managers and engineers alike to visually represent team structure and task components. In application, process mapping helps a team gain a “big picture” for what is happening and what ought to happen within a complex organizational process. But its potential for team cognition is much broader. In the following section, we expand on some of the key ideas originally set forth by Fiore and Schooler (2004), who asserted that process mapping serves as an example of capturing a shared mental model, and, of significance to those who research and design for cognition, the creation of the process map actually facilitates the construction of shared models.

Most simply stated, a process map captures a visual representation of work flow within a given organizational process. Team members contribute from their individual sets of experiences, insights into workflow, and knowledge of task processes as they know them. Collaboratively, the team produces a representation of their process knowledge. Because the map is constructed from unique perspectives, even the best-informed team members will realize that they had gaps in their individual knowledge. This shared understanding comes directly from the act of constructing the process map and allows groups to focus appropriately on problem conceptualization rather than moving straight to solution generation (Fiore & Schooler, 2004).

Roles and responsibilities are also highlighted in the structuring of the process map, and team members become more aware of the group contributors. This validates the process, as groups often show difficulty identifying accurately who has the knowledge most relevant to the problem at hand (e.g., Serfaty, Entin, & Johnston, 1998). “Process mapping is additionally beneficial because it facilitates information sharing by guiding the transfer of information that takes place during group discussion” (Fiore & Schooler, 2004, p. 143). The team can contribute idiosyncratically and synergistically, thus improving the value of the information brought to the map. In execution, problem-solving teams have found that process-mapping sessions elicit better understanding of member roles, and the natural outgrowth of this understanding is respect (Loew & Hurley, 1995).

Further, in line with our earlier description of macrocognition in teams and the role of cognitive artifacts, Fiore and Schooler (2004) elaborated upon the value of external representations with process mapping. They suggested that “the degree the team-task requires the construction of a shared understanding, external representational tools can act as a scaffolding to facilitate the building of that shared representation” (Fiore & Schooler, 2004, p. 134). The externalizations, that is, the process maps, are tangible artifacts that embody the team’s conceptualization of the problem. They suggest that these artifacts provide the means through which collaborators can visually articulate abstract concepts. In support of team cognition, the members are able to “manipulate these task artifacts as the problem solving process proceeds [and they] act as a scaffolding with which the team can construct a truly shared, and concrete, depiction of the process problem” (p. 144).

Thus, more important than the mutual understanding of skills and roles is the collaborative conceptualization of problems. The expression of knowledge helps the team experience greater clarity, and it promotes and supports a shared problem model. Fiore and Schooler (2004) essentially argue that it is the externalization that helps to mediate the team’s cognitive and collaborative process. But the shared understanding does not just appear during construction of the map, or it would be as simple as fitting pieces of a puzzle together. Instead, metaphorically, many of the puzzle pieces actually need reshaping or, if an assumption is entirely inaccurate, the piece is thrown away. The team members must negotiate and experience a certain level of flexibility as they work to express, reshape, and incorporate ideas (Levine et al., 1993). This approach (p. 210) suggests that a forced negotiation for construction of the map itself develops into the shared mental model that said map represents. “By diagramming the entire flow such that interconnections are clear and all repercussions are noted, process mapping provides a means with which to accurately articulate complicated processes and can overcome limitations normally experienced when teams deal with complex problems” (Fiore & Schooler, 2004, p. 145). Furthermore, later problem-solving stages benefit from the process map because the problem-solving team disposes of inaccurate conceptions and workflow redundancies. As the individual mental models move toward a shared understanding, the initial problem representation (referred to as the “as is” map) reflects the whole group, but from there an idealized map, a picture of what should be, can then be generated (Mason, 1997). From the standpoint of team cognition, process mapping illustrate how teams can find solutions by collaborating to identify, analyze, and accurately conceptualize the problem.

In sum, Fiore and Schooler (2004) approached process mapping as an effective tool for developing shared problem models, and as a tool that provides a number of predictive and evaluative benefits to team cognition. They presented it as a representation of mental models themselves, noting that process maps capture the mental constructions of team members and the visionary constructs of a task as it should be. Beyond that, it shapes the way in which team members think. Specifically, the very creation of the map forces a more clearly shared understanding of both team and task. In this construction, the teams develop a shared problem model for the task by facilitating communication among contributors, and examination of factors within the problem environment produces a shared awareness of the process problem. We further suggest that it provides an important illustration of macrocognition in teams, connecting traditional team cognition theory with the value of externalized cognition in the service of building knowledge to solve problems (cf. Fiore, Elias, et al., 2010).

Future Directions in Cognitive Engineering for Teams

In this final section, we provide a set of questions distilled from our prior discussion. Our goal is to guide cognitive engineering in the context of team cognition. This list is not meant to be exhaustive. Rather, it is meant to be representative of the types of issues that arise when cognition and technology merge. Similarly, they illustrate well the necessity for interdisciplinary collaborations cutting across cognitive science, psychology, engineering, and computer science. Further, they are meant to ensure that the human is central in the design of team cognitive technologies.

  •  How can collaborative systems be engineered to support distributed teams? A key problem that cognitive engineers should continue to examine is the challenge of distributed teams. While programs and tools have been developed to address the challenges of distributed interaction (visual and audio interfaces for globally distributed teams, real-time text-based conversational tools), there are gaps in our ability to support the more socioemotional aspects of teams. This includes essential team needs, like trust, joint decision, and empathy. The characteristics of effective teams should be brought to the forefront of the engineering tasks, just as the individual needs for attention and prior knowledge are part of the design considerations for one human in a system. It becomes a matter of shifting from the notion that teams are only multiple humans to a decisive recognition that teams are distinct socio-cognitive entities that contain multiple humans.
  •  What technologies are necessary to scaffold the more embodied and enactive components of team cognition? In recent theorizing, we have argued for the notion of embodied cognitive fidelity (ECF) as a construct that captures the emergence of socio-cognitive factors at both individual and team levels (Bockelman et al., 2011). ECF is a form of fidelity “which captures the dynamic, embodied, enactive, and distributed nature of collaborative cognition that is situated within physical and social environments” (p. 1507). As a complement to our above point, we suggest that cognitive engineering must work to develop interaction systems that simulate interaction and prioritize these aspects of cognition (cf. Walmsley, 2008). In particular, on micro- and macrocognitive levels, the confluence of these factors produces the type of social intelligence that enables team effectiveness. Collaboration technologies for distributed cognition must enable the type of social cuing and implicit communication processes that foster collaboration and the development of shared awareness and knowledge within teams.
  •  How can cognitive engineering collaborations produce visualization technologies that scaffold team cognition dealing with more abstract problems? As we have shown in this chapter, research into external (p. 211) problem representations illustrates an important interplay between person and visualizations in problem solving. But we have primarily discussed concrete tasks more readily lending themselves to visualization. Further research is needed in how externalization of reasoning processes can be developed. For example, early work in this area documented the efficacy of diagrammatic presentation to facilitate argument construction (Stenning & Oberlander, 1995; Suthers & Hundhausen, 2001). Others showed how imagery in collaborative problem solving facilitated the generation of alternative interpretations (Grabowski, Litynski, & Wallace, 1997). More recently, cognitive science has explored decision support systems in the service of clinical reasoning and problem solving (e.g., Lu & Lajoie, 2008). But there are many such collaborative tasks rife with abstractions, uncertainties, and complexities (e.g., Balakrishnon, Kiesler, & Fussell, 2008). We suggest that cognitive engineering more fully explore how to develop technologies for visualization. Such technologies need to support collaboration through the development of artifacts that mediate collaborative cognition dealing with both concrete and abstract issues.
  •  How can team cognition support the development of human-robotic agents? One area where engineers can practically apply their skills and methods to team cognition is in the development of productive human-robot teams (Hoffman & Breazeal, 2004). Here we can find the interconnected tiers of cognitive engineering from the intelligent and autonomous robot agent, to interactive systems, to team cognition and beyond. In short, a significant challenge with technologically based “teammates” is understanding the subtle forms of interaction that emerge when humans collaborate with agents or even with robots (Goodrich & Schultz, 2007). This includes scaffolding how agents manage social engagement (Argall et al., 2009; Asada et al., 2009) and social cognitive factors like shared attention (Elias et al., 2011; Streater et al., 2011). From the standpoint of training humans how to engage in this new form of collaboration, the use of advanced capabilities in simulation may support this (Bockelman et al., 2011). This line of inquiry and development is particularly valuable, as it allows for advancement of the artificial agents in conjunction with deeper explorations into the nuances of social cognition.
  •  How might cognitive engineers use their experiences in teams to develop better team tools and products? Finally, cognitive engineering projects rarely, if ever, are taken on by individuals. The practitioners of cognitive engineering are starting with an experiential framework that could provide insight into the field of team cognition and could advance engineering as a whole. We suggest that an important starting point for addressing team cognition challenges is for cognitive engineers to begin looking at how their teams are already dealing with difficulties of an interdisciplinary field. How do neuroscientists, computer scientists, and psychologists develop shared mental models for the engineering tasks they address? From this meta-level approach of self-examination, cognitive engineers may be able to consider engineering and ergonomic solutions to help generate more intelligent team solutions.

Conclusion

In this chapter we have advocated for framing the discussion of team cognition as a distinct and interdisciplinary field developed to study complex collaborative processes. We first noted that, in the design and development of human-technology systems, it is imperative that we distinguish groups from teams. Although groups and teams involve multiple participants, designing for independent thought processes is drastically different from designing to support the natural and complex interdependent processes emerging in team contexts. Furthermore, we argued that coordination is at the core of team cognition, and human-centered technologies should keep this in the forefront of design concepts and frameworks.

We described how team cognition has evolved from early research in social and organizational psychology, which focused on the dynamics of interaction in context, to a broadly applied interdisciplinary science that explores complex cognitive dynamics where human and machine are intimately integrated into team performance. A review of the history of team cognition does not encourage the contemporary scientist to dismiss early theories and approaches, but in this field the insights into human cognition have matured while others have been replaced by new concepts. Nonetheless, as in the early days of psychology, the individual mind matters, but we now consider the contributions of the individual in conjunction with the contextual influences of the task, the team, and the technology and how these interrelate to create collective team (p. 212) models. This history has unfolded from the intimate connection between intelligent technologies and the study of intelligence itself.

Further, the development of team cognition has shifted focus from microcognition to macrocognition, informing understanding of coordination with externalized and distributed representations. To this point, we described the value of shared mental models and how these can be externalized and co-constructed in the service of collaborative problem solving. We discussed an example from process re-engineering that provided both means and metaphor for conceptualizing shared mental models through process mapping. Teams gain efficacy as they realize and eliminate inaccuracies associated with process problems. The emphasis here is not so much on the tool itself, but rather that a systematic approach to communicating and visualizing problem-solving tasks helps to move knowledge from individual minds. Externalizing this knowledge helps team members to employ it to its fullest extent. For the engineer, this invites exploration in the ways that engineering teams approach design and exploration into designs that support teams.

Across all of this was the connection between team cognition theory and human-technology integration. The increasing interaction and interdependence of humans and machines is reshaping cognitive science, psychology, engineering, and computer science. As the lines between human and machine become less clear, it becomes increasingly important to keep the human central in the design of technologies supporting teamwork and team cognition.

Acknowledgments

The writing of this chapter was partially supported by Grant SES-0915602 from the National Science Foundation and ONR MURI Grant #N000140610446 from the Office of Naval Research Collaboration and Knowledge Interoperability (CKI) Program. The views, opinions, and findings contained in this article are the authors’ and should not be construed as official or as reflecting the views of the University of Central Florida, the National Science Foundation, or the Office of Naval Research.

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Patricia Bockelman Morrow, Cognitive Sciences Laboratory, University of Central Florida, Orlando, FLStephen M. Fiore

Stephen M. Fiore holds a joint appointment with the University of Central Florida’s Cognitive Sciences Program in the Department of Philosophy and UCF’s Institute for Simulation and Training and Team Performance Laboratory. He earned his PhD (2000) in cognitive psychology from the University of Pittsburgh, Learning Research and Development Center. He maintains a multidisciplinary research interest that incorporates aspects of the cognitive, organizational, and computational sciences in the investigation of learning and performance in individuals and teams. He is Co‐editor of a recent volume on Distributed Learning as well as a volume on Team Cognition and he has published in the area of learning, memory, and problem solving at the individual and the group level. He has helped to secure and manage over US$6 million in research funding from organizations such as the National Science Foundation, the European Science Foundation, the Office of Naval Research, and the Air Force Office of Scientific Research.

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