Cooke, Gorman, & Winner 1
TEAM COGNITION
Chapter for Handbook of Applied Cognition, Second Edition
Nancy J. Cooke, 1, 3 Jamie C. Gorman, 2, 3 and Jennifer L. Winner 1, 3
Arizona State University East1
New Mexico State University2
Cognitive Engineering Research Institute3
Contact:
Nancy J. Cooke, Ph.D.
Applied Psychology Unit
Arizona State University East
7001 E. Williams Field Rd., Bldg. 140
Mesa, AZ 85212
480-988-2173 (office)
480-988-3162 (fax)
ncooke@asu.edu
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 2
Research and theory on cognitive structure and process occurring inside an
individual’s head have dominated the last fifty years of scientific psychology. In this
regard, cognition has primarily been attributed to the individual and not to a team or
group. For the most part, applications of cognitive theories and findings have, as a result,
tended to be individual-centric (e.g., cognitive processes involved in using a computer;
individual decision making; situation awareness of a single pilot). However, the world
has not kept pace. The growing complexity of technological systems and the associated
cognitive burden of operating, maintaining, diagnosing, and overseeing such systems has
necessitated teams or groups of humans. Problems posed to cognitive engineers in
settings ranging from military systems to aviation and nuclear power plant systems
depend increasingly on multiple humans interacting with each other and interfacing with
complex technology. What does cognitive psychology have to say about these important
problems? Do individually oriented cognitive theories and methods apply to teams? Are
teams somehow different from a collection of individuals? How can we measure, assess,
and design for team cognition? How can team cognition be applied to increase team
effectiveness? These are some of the questions to be explored in this chapter.
First we will define team cognition in order to position this topic within the area of
applied cognition and to provide some focus for the remainder of the chapter. Team
researchers have distinguished between teams and groups, and in turn, between team
research and applications and group research and applications. In this chapter we follow
convention and define a team as a special type of group. Specifically, Salas, Dickinson,
Converse, and Tannenbaum (1992) define a team as "a distinguishable set of two or more
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 3
people who interact dynamically, interdependently, and adaptively toward a common and
valued goal/object/mission, who have each been assigned specific roles or functions to
perform, and who have a limited life span of membership" (p. 4). Thus teams are special
types of groups that are interdependent and that have specific roles for different team
members. A surgical unit is a team, but a jury is a group, as is a typing pool. A pilot, and
co-pilot, and flight attendant comprise a team by this definition, but the faculty of a
psychology department functions more as a group, as do religious and political groups.
Command-and-control tasks and military planning tasks involve teams.
One dimension that tends to distinguish groups from teams is degree of
homogeneity in regard to cognitive requirements of individuals. Teams tend to be more
heterogeneous than groups by virtue of a division of labor that is tied to the definition of
teams and necessitated by the requirements of increasingly complex systems. Related to
the notion of a division of labor, heterogeneous team members complement each other,
though they may not be very similar (like vinegar and oil). Nonetheless, they can work
well together in the service of some higher-level functionality, such as a common or
valued goal.
Much research has been done in the organizational management, computer
supported cooperative work, and social psychology arenas on small group behavior and
decision making (e.g., Kerr & Tindale, 2004). The research in this area tends to be on
groups, not teams, and so some theories and findings may not apply. On the other hand,
there are probably as many theories and findings that do apply to both groups and teams.
Thus, in this chapter, we will consider the work on groups that may inform the theories,
measures, and applications of team cognition.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 4
Given the Salas, et al. (1992) definition of a team, what is team cognition? In this
chapter we define team cognition as cognitive activity that occurs at a team level. For
instance, planning is a cognitive activity and team members can carry out planning
independently. But this does not by itself constitute team cognition, in the sense that
planning has not been carried out via collaborative planning by two or more team
members with a common goal. Thus, the team-level stipulation necessitates that there is
interaction among the individuals. For example, by our definition, team situation
awareness is not the respective situation awareness of each individual team member
(Endsley & Jones 1997), but necessarily something based on interaction and probably
something emergent.
The difference between a linear aggregate of individuals and interacting individuals
is a very important, and somewhat controversial, distinction in team cognitive research
(Cooke & Gorman, in press). Team cognition is different than the sum of the cognition
of individual team members. It is the result that emerges during functional interactions of
team members in pursuit of a common and valued goal. So how do we measure team
cognition? How do we design technology to support this team-level cognition? How do
we train teams to support team-level cognition? These are questions addressed in the
remainder of this chapter. Answers to these questions are increasingly important as
teams continue to supplant individuals as the human element in highly complex
technological systems (e.g., socio-technical systems; Eason, 1988).
The sections in this chapter are organized in a nontraditional sequence that to the
authors, characterizes the historical roots of this new field. Though several precursors to
the study of team cognition such as team performance in the industrial/organizational
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 5
tradition, management in the business tradition, and small group decision making in the
social psychology tradition are not at all new, the study of team cognition as defined in
the preceding paragraphs is no more than fifteen years old. Unlike other applied research
topics for which application of well-developed theory is the end goal, for team cognition,
application has been the main driver. The field was sparked by a need to improve the
cognitive activity of teams. Measurement, empirical work, and theoretical development
followed. Of course, existing theory from the precursor disciplines was also influential in
the early days of the field and in reality, the interplay between application and theory is
really more cyclical than linear, in this case the push stemming from application is
noteworthy. Therefore, we have decided to structure this chapter accordingly. We start
with the applied need, working our way to the developments in measurement and
empirical work, and finally culminating in the continually evolving theory of team
cognition.
THE NEED FOR A SCIENCE OF TEAM COGNITION
We often think of applications in cognitive psychology as the natural product of
good cognitive theories. As the story goes, a theory is born, grows, is tested, is further
developed, and eventually matures all in the pristine circumlocution of the lab. When
ready the theory is (sometimes) pushed out into the world of application, often looking
for a job and sometimes finding that it is poorly qualified to fit the existing need. This
story does not characterize team cognition. While there were theories and sciences of
teams and of group decision making they did not completely address the problem of team
cognition. And there was a problem to be addressed.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 6
Indeed much of the research on team cognition was fueled by a handful of disasters
that occurred in a ten-year period and that pointed out problems with team performance
in cognitively demanding arenas. The Three Mile Island and Chernobyl nuclear power
plant accidents of 1979 and 1986, respectively both involved problems with the response
of the operating crews in the control rooms (Gaddy & Wachtel, 1992). On January 28,
1986 faulty decision making at the organizational level (i.e., teams of teams) resulted in
the mistaken and tragic launch of space shuttle Challenger (Vaughan, 1996). Then in
July of 1988 the USS Vincennes, a US Naval warship, mistakenly shot down an Iranian
airbus full of passengers (Collyer & Malecki, 1998). This incident, like the others, was
tied to a complex web of causes and preexisting system weaknesses (Reason, 1997), but
also like the others, was partially attributed to coordination problems in the commandand-control decision making.
These events of the 1980s highlighted the complexity of our human-technological
systems, and not only in terms of the physical system, but also in terms of the sociocognitive system. It became clear that human decision making and other cognitive
activities were occurring in the context of a complex socio-technical system and that this
type of cognition, so foreign from the isolated and artificial lab studies that dominate
mainstream cognitive psychology, demanded attention. The naturalistic decision making
movement fully appeared on the scene in the 1990s to address cognitive processing in
complex dynamic environments (Zsambok & Klein, 1997).
The cognitive engineering
and decision making technical group of the Human Factors and Ergonomics Society also
had its start in the 1990’s. The TADMUS (Tactical Decision Making Under Stress)
research program of the Navy was initiated in 1990 as a direct result of the Vincennes
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 7
incident with specific Naval applications as the target of this research (Cannon Bowers &
Salas, 1998). This was not the case of a well-developed theory of team cognition looking
for a problem, but a problem, or set of problems, in need of a theory. The TADMUS
program fueled some of the first research on team cognition. It is significant that these
research efforts were tied heavily to application—to enhancing team effectiveness in
complex cognitive environments. Because an understanding of team cognition is a
prerequisite to improving team effectiveness, the theoretical growth has occurred in
parallel, but the research has been driven primarily by applied needs. Thus, applications
are seen here not as the end result of various research programs, but as drivers of those
programs.
Fifteen years have passed since the disasters and ensuing programs that sparked
work on team cognition. Theoretical perspectives, methodologies, and research findings
have progressed significantly and in parallel with specific applied solutions. As the field
progresses, so do the applications. With disasters such as the Vincennes looming in the
recent past, there was no time to wait for the science of team cognition. Applications to
improve team decision making, for instance, were needed “yesterday.” Thus, the need
spawned some initial applied solutions that were based on preliminary conceptions of
team cognition primarily drawn from precursor and related disciplines (i.e., social
psychology, management, information processing) and applied by those in
industrial/organizational psychology, military psychology, and human factors. Efforts
toward these early applications incited many research questions regarding measurement
and concepts of team cognition. At the same time the applications evolve with the
science of team cognition. Thus, in the following sections we describe some of the early
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 8
applications of team cognition, saving the more recently applied products of this
evolution for the end of the chapter.
Team Training
In applied psychology and specifically in industrial/organizational psychology,
there has been a long tradition of applying psychology to training and specifically,
applying the study of teamwork to improving team training effectiveness (Ford,
Kozlowski, Kraiger, Salas, & Teachout (1997). To address the need for training teams in
more cognitively laden activities, team training programs began to focus more heavily on
the cognitive training of teams.
For instance, Crew Resource Management (CRM) training programs (Helmreich,
Merritt, & Wilhelm, 1999; Salas, Burke, Bowers, & Wilson, 2001) incorporate important
cognitive skills such as team coordination, communication, and resource allocation.
CRM training has been predominant in the aviation community and CRM training
programs have been recently implemented in other team domains such as transportation
and medicine (Salas, Wilson, Burke, & Wightman, in press). Evaluations of CRM
training programs have been generally positive.
Cross training involves the training of individuals in the job skills associated with
other team member positions. In jobs that are heavily cognitive, many of the skills are
apt to be cognitive in nature. Cross training is generally effective, but the concept of
team cognition and in particular, shared mental models and interpositional knowledge
were drawn upon as explanations for the success of this approach (Cannon-Bowers,
Salas, Blickensderfer, & Bowers, 1998; Cooke, Kiekel, Salas, Stout, Bowers, & CannonBowers, 2003; Volpe, Cannon-Bowers, Salas, & Spector, 1996).
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 9
Other training strategies were readily adapted to cognitive tasks and include team
leadership training and team self-correction which teaches team members to identify and
correct problems in the team without help from an outside instructor (Salas & CannonBowers, 2001)
Simulation
In order to study team cognition in a laboratory, it became clear that rich synthetic
environments were required that provided empirical testbeds for teams. Although there
were team testbeds in place, there was a need for cognitive simulators for teams. Some
initial testbeds grew out of the TADMUS program and were based on tactical decision
making by crews on ships (e.g., TANDEM, Johnston, Poirier, & Smith-Jentsch, 1998).
In the mid to late 1990s there was a flurry of work devoted to the development of
synthetic task environments for teams (Schiflett, Elliott, Salas, & Coovert, 2004). The
Dynamic Distributed Decision Making Task (DDD, Kleinman, Young, & Higgins, 1996)
was developed as a synthetic version of an Airborn Warning and Control System
(AWACS) platform. This synthetic environment has since morphed to simulate a wide
range of team tasks (e.g., a snowmobile-based search and rescue mission). Cooke and
Shope (2002, 2004) developed a synthetic task environment to simulate Unmanned
Aerial Vehicle (UAV) ground control by a team. This testbed was unique in its
experimenter-friendly design (i.e., affording measurement and manipulation) and its
applicability to heterogeneous team tasks in which team members all have very different
jobs to do (i.e., navigator, pilot, photographer).
The act of developing these simulations required and generated extensive
knowledge about the cognitive team tasks in the field. Questions were also raised about
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 10
the concept of simulator fidelity (Cooke & Shope, 2004; Salas, Bowers, & Rhodenizer,
1998). As military doctrine changed and concepts such as network-centric warfare
became common place, the applicability of these testbeds as potential training and
interface testing environments also became clear.
Software Tools
Much of the work on decision aids and collaborative tools for teams and groupware
had its start in business management and human-computer interaction. This work has
been labeled CSCW (Computer Supported Cooperative Work), GDSS (Group Decision
Support Systems), and groupware (Olson, Malone, & Smith, 2001). These applications
were designed to facilitate business meetings, collaborative writing, and organizational
decision making. More specific applications of groupware focused on facilitating the
integration of multiple perspectives across a broad decision making domain (e.g., product
development teams, Monplaisir, 2002) by improving the ability for collaboration from a
distance.
Groupware researchers have experimented with various communication modes for
distributed groups such as video, audio, and text (Daft & Lengel, 1986). However, while
groupware can often lead to increased decision quality, it may have a negative impact in
terms of time needed to make decisions, as well as overall group member consensus and
sense of satisfaction (cf. cohesiveness; McLeod, 1992). Indeed the advantages of various
CSCW applications seem dependent on the type of team and task (DeSanctis & Poole,
1991).
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 11
Some Unanswered Questions
These initial applications for improving team cognition were valuable in identifying
research questions and guiding future research and development efforts in team
cognition. As before, team cognition is a good example of the problem-driven approach
of cognitive engineering.
Interests in team cognition, combined with successful tests of cross training, gave
rise to the concept of shared mental model and the idea of interpositional knowledge. But
there were also questions about scalability of cross training to large heterogeneous teams
in highly complex settings such as shipboard command-and-control. Would cross
training across all positions be practical? Is there another more streamlined approach
(Cooke, et al., 2003)? Is the “shared mental models for all” an optimal goal state?
Groupware applications provided valuable insight into how distributed decision
making may be facilitated by technology, but it also spawned questions about the costs
and benefits of co-located versus distributed environments. With groupware technology
also came an interest in ways to facilitate team situation assessment and group interaction
processes associated with group decision making. Indeed, these were some of the same
issues important those interested in team cognition. However, other questions were
raised about differences between small groups and teams and between the business
meeting and more heterogeneous, interdependent tasks.
One very important question kept surfacing; the question of measurement. How
should we measure team cognition? The answer to this question was central for
designing and evaluating the success of training programs, supportive tools and
technologies, and even for understanding individual and team differences that may lead
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 12
to applications in team composition and staffing (Morgan & Lassiter, 1992). Further, the
measurement problem is not merely about eliciting team cognition from experts to build
systems, but it is also about assessment and diagnosis. How can we measure team
cognition so that deficiencies and strengths of a team can be assessed and diagnostic
information can be provided to target specific team-level cognitive skills? Accordingly,
one of the biggest measurement challenges is to consider how assessment and diagnosis
could occur in an embedded, automated, and real-time fashion in an operational setting.
We could then monitor team cognition, assess it on the fly, and provide diagnostically
appropriate interventions, all in real time.
MEASURING TEAM COGNITION
Leveraging cognitive work at the individual level researchers have applied and
adapted various individual measurement methodologies to teams. The exact nature of the
measurement problem dictated specific types of methods over others. Initial forays into a
team domain are usually accompanied by a need for techniques that elicit and analyze
team cognition. These methods have been used to circumscribe the task or domain in
terms of the knowledge, skills, and abilities required. Information gleaned from these
activities is used to construct tests to assess a team with respect to a desired cognitive
state. The general idea is that through assessment we can determine if a team’s cognition
is improving with a particular intervention and rank order teams with respect to team
cognition. Finally, with a deeper understanding of team cognition in a domain, the
researcher can begin to identify diagnostic information for targeted interventions. This
stage is analogous to understanding whether observed memory deficits in an individual
are due to a biological cause or loss of motivation.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 13
In this section we describe some of the measurement methods that have been
applied to team cognition, some limitations of those measures, and some approaches to
measuring team cognition that address those limits.
Elicitation and Analysis
Cooke (1994; 1999) reviews knowledge elicitation techniques and categorizes them
by type. The types include observations, interviews, process tracing, and conceptual
methods. There are countless variations within each category. Knowledge elicitation
methods evolved from a need to understand individual expertise, often for applications
such as expert systems and intelligent tutoring systems. Although knowledge elicitation
focuses primarily on knowledge, there are other methods for analyzing the cognition
underlying a task more generally. These include cognitive task analysis (Seamster,
Redding, & Kaempf, 1997), and cognitive work analysis (Vicente, 1999). For team
cognition, these kinds of measurement activities have been conducted for a variety of
purposes including design of a synthetic task that captures some of the cognitive fidelity
of the real task (Cooke & Shope, 2004), identification of training requirements (Mitchell,
Yadrick, & Bennett, 1993), improvement of team coordination (Klinger & Klein, 1999),
and development of assessment metrics (Cooke, Stout, & Salas, 2001).
A popular team knowledge elicitation method involves what Cooke (1999) referred
to as a “conceptual method”, in which judgments of the proximity of domain-relevant
concepts to one another are elicited from team members. The set of pairwise judgments
can be submitted to multivariate statistical routines, like Pathfinder (Schvaneveldt, 1990)
in order to better visualize “knowledge structure.”
This approach has been used
extensively to examine individual expert-novice differences and has been applied to team
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 14
cognition in the elicitation of team knowledge and team member mental models (Cooke,
et al., 2003) as well as the identification of interaction patterns in teams (Shope, DeJoode,
Cooke, & Pedersen, 2004).
Cognitive Work Analysis (Vicente, 1999) is also especially well-suited to an
analysis of team cognition. Essentially, a Cognitive Work Analysis involves identifying
a constraint hierarchy in which degrees of freedom (i.e., constraints) are limited with each
subsequent, more constraining stage inheriting the constraints above it. Socialorganizational constraints are among the constraints considered allowing the researcher to
identify aspects of the work domain that rely on team cognition.
Other elicitation and analysis methods have been adapted to the elicitation and
analysis of team cognition (e.g., Klinger & Klein, 1999). Using a cognitive task analysis
for instance, the researcher can observe the team during task performance (or by
reviewing a recorded performance) in order to develop a model for the sequence of
interactions among team members (Stout, Salas, & Carson, 1994). For example, Klinger
and Klein (1999) used observation and interview methods to understand team issues in an
emergency response organization of a nuclear power plant. Through these methods they
were able to identify system weaknesses and suggest improvements. Structured
interviews have also been conducted (Gugerty, DeBoom, Walker, & Burns, 1999) to
elicit team cognition from UAV operators. Results from these interviews were then used
to design a synthetic environment that serves as a testbed for understanding team
cognition in the UAV domain (Cooke & Shope, 2002, 2004).
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 15
Assessment and Diagnosis
Although the elicitation and analyses methods provide critical information and are
prerequisites for assessment methodologies, they do not alone afford more than
qualitative or descriptive information about team cognition. Without additional methods,
questions about how teams compare cognitively, how team cognition develops over time,
or the success of interventions to improve team cognition are restricted to qualitative
analyses.
Assessment of team cognition entails comparison of some output that reflects team
cognition to for example, an expert team or some other referent that serves as an idealized
standard. Referents used to assess individual cognition are either developed logically
based on analysis and deep understanding of the domain or are based on the products of
expert elicitation. Team cognition can similarly be compared across teams or compared
to some referent representation of an expert team.
Team process behaviors, which are sometimes associated with team-level cognitive
process behaviors have been assessed in this manner. That is, expert judges observe and
rate team behaviors (e.g., communication, leadership, conflict management) and judges’
ratings are assumed to involve implicit comparisons of the team behavior with idealized
behavior in the mind of the judge. However, interrater reliability is an issue with
generalized ratings of this type. Event-based checklists (e.g., TARGETS; Fowlkes,
Dwyer, Oser & Salas, 1998) represent an approach to improving observations by
grounding them in contextually-relevant events (e.g., instead of rating team
communication, check whether or not a specific piece of information was passed between
team members at a given event). A checklist approach like this is a useful method for
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 16
assessing the sequencing and timing of the team process behaviors thought to underlie
team cognition (Proctor, Panko, & Donovan, 2003).
Although individual situation awareness has been measured using a mixture of
subjective, performance-based, and query methods, team situation awareness has
primarily been assessed using aggregate query-based techniques (e.g., SAGAT: Situation
Awareness Global Assessment Technique; Endsley, 1995) in which queries concerning
task related items are presented to each team member during a break in task performance
(Bolstad & Endsley, 2003). Team situation awareness is then measured by aggregating
(e.g., summing) the accuracy scores of all team members (Endsley & Jones, 1997). The
Situation Present Assessment Method (SPAM; Durso, Hackworth, Truitt, Crutchfield,
Nikolic, & Manning, 1998) is another query method that has been applied to teams and
that does not involve separating the team from the visual cues in the situation. Similar to
SAGAT, a metric of team SA can be computed by aggregating the accuracy of individual
team members.
Team or shared mental models have been assessed primarily through the
comparisons of conceptual representations such as those elicited using proximity
judgments and represented using Pathfinder (e.g., Stout, Cannon-Bowers, Salas, &
Milanovich, 1999). At the most basic level, the degree to which a mental model is shared
by two team members can be estimated through a comparison of the representations of
those two team members. Pathfinder similarity can be quantified in terms of proportion
of shared links. Accuracy of a conceptual representation can similarly be estimated
through comparison with a referent representation.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 17
In sum, methods have been adapted from individual cognitive measurement and
used to assess team cognition, both in terms of quality or accuracy of the knowledge and
similarity between team members. Though diagnostic information is also required for
effective applications, the methods have been thus far limited to assessment. This is
likely due to the fact that diagnostic information necessitates a deeper understanding of
the mechanisms of effective and ineffective team cognition. Further empirical work,
modeling, and theoretical development are needed to achieve this level of understanding
and the concomitant diagnostic tools and methods.
Some Measurement Limitations and Proposed Solutions
The application of measures developed to elicit, analyze, and assess individual
cognition to measure team cognition was an important first step, but the jump from
individuals to teams brought with it some new measurement challenges (Table 1).
Table 1. Some challenges to measuring team cognition and some solutions.
Challenges
Solutions
Measures applicable to heterogeneous teams
Heterogeneous knowledge metrics
Measures that capture emergent cognition
Holistic Metrics
Holistic, embedded, real-time metrics
Communication analysis
Measures of emergent team situation awareness CAST
The first challenge pertains to the definition of teams offered by Salas, et al, 1992,
especially the part that states that team members have “each been assigned specific roles
or functions to perform.” What does team cognition mean in a heterogeneous group with
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 18
very different roles? For instance, what does it mean for an anesthesiologist, a surgeon,
and a nurse to have a shared mental model? What does it mean for a commander and his
subordinates to have a shared understanding of the situation? What does it mean for an
emergency response team to have team situation awareness? Early applications of
measurement treated teams as homogeneous and assumed that shared knowledge (or
situation awareness, mental models, understanding) is similar knowledge. These metrics
elicited knowledge at the individual level and examined similarity (and sometimes
accuracy) across team members (e.g., Langan-Fox, Code, & Langfield-Smith, 2000).
Although appropriate perhaps, for homogeneous groups like juries, these similarity-based
metrics do not measure cognition for heterogeneous teams.
Cooke, Salas, Kiekel and Bell (2004) describe knowledge metrics for
heterogeneous teams. This approach derives knowledge referents for each position on
the team (e.g., the ideal nurse’s knowledge) and assesses positional accuracy for
individual team members by comparison of some knowledge output to a role-specific
referent. They refer to this as “positional knowledge.” “Interpositional knowledge” can
similarly be assessed by comparison of each team member’s knowledge output with that
of each of the other role referents. These metrics have shed some light on the manner in
which knowledge is optimally distributed in high-performing teams (Cooke, et al., 2003).
Another characteristic of the metrics that were adapted for measuring team
cognition is that they focused on eliciting and assessing individual knowledge and
aggregating (e.g., averaging) results across team members. Not only is the aggregation
process questionable for a heterogeneous group (i.e., equating “apples and oranges”), but
this approach seems to lose sight of the essence of team cognition. If we view team
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 19
cognition as an emergent phenomenon, that is, a result that emerges through interactions
among team members, then aggregation-based metrics are unsatisfying. These metrics
miss the mark in terms of adaptive interaction on the part of team members that underlies
team cognition.
As a solution to this problem, metrics could focus more on process. Cooke, Salas,
et al. (2004) developed “holistic” metrics that do just this. Knowledge is elicited at the
team level through a consensus process. For instance, a team, not an individual will be
asked to provide relatedness ratings for pairs of task-related concepts. Results from this
holistic approach are different from the aggregate approach as seen in Figure 1 in which
3-person teams were ranked in terms of aggregate or collective knowledge accuracy or
holistic knowledge accuracy (Cooke, DeJoode, Pedersen, Gorman, Connor, & Kiekel,
2004). Though the rank orders are different, the results concerning the predictive validity
of one metric over the other are mixed. Indeed, this solution is not completely
satisfactory because it assumes that the process used in the consensus task mirrors that of
operational team-level cognitive processing, however, it does allow for dynamic
distribution of team member resources in formulating a team-level response.
Another potential alternative to aggregate measurement is to measure team
cognition through team communication analysis (Kiekel, Cooke, Foltz, Gorman, &
Martin, 2002). Communications occur naturally in most team tasks, and can thus be
thought of as a sort of naturally occurring “think-aloud” and thus a holistic metric of team
cognition. Rather than providing a window to team cognition, communication provides
direct access to the team member interactions that are team cognition. Many researchers
have relied on communication analysis for understanding the cognitive demands and
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 20
Figure 1. Team’s Rank Order According to their Collective or Holistic Taskwork
Knowledge (Experiment 2, Cooke, DeJoode, et al., 2004)
constraints in various task domains in order to generate team-oriented theories of task
acquisition and/or performance in the domain (e.g., Kanki & Foushee, 1989; Achille,
Schultz, & Schmidt-Nielson, 1995; Gorman, Cooke, & Kiekel, 2004).
Communication can be measured as either communication content data or
communication flow data. Each of these types of communication data has their own
unique advantages and drawbacks, however, content data can be extremely cumbersome
for the fact that time and effort required to record, code, and analyze data (Emmert, 1989)
is prohibitive. Researchers have made efforts to facilitate (if not automate) content
analysis using methods such as linguistic inquiry, word count (Sexton & Helmreich,
2000), and latent semantic analysis (Landauer, Foltz, & Laham, 1998) in order to derive
metrics of team communication content. Alternatively, communication flow data
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 21
consisting of time-stamped records of who is interacting with whom, when and for how
long are also promising in this regard. Although these communication data are relatively
low level, they have been demonstrated to be useful for inferring relatively high-level
team cognition (Kiekel, Gorman, & Cooke, 2004) and are robust in the face of nonverbal
and otherwise degraded communication data.
Most attempts to measure team situation awareness have been collective in nature
in that individuals are assessed for situation awareness and the team is characterized
according to a summation or average of the individual scores (e.g., Bolstad & Endsley,
2003). These measures tend to assume homogeneity and do not seem to get at the
emergent, dynamically coordinated aspects of teams encountering a quickly changing
task environment. A method called CAST (Coordinated Awareness of Situation by
Teams; Gorman, Cooke, & Winner, submitted) that assesses the coordinated perception
and action that emerges from team member interactions (beyond the static knowledge of
team members) when faced with unusual situation constraints or “road blocks” begins to
address this limitation.
In sum, measures used to elicit and assess individual knowledge have been applied
to team cognition. Whereas this is a useful start, there are limits in extending these
metrics to team cognition. In particular, the emergent quality of team cognition that
accompanies interactions of heterogeneous team members is not captured by
individually-oriented and aggregation approaches. Team-level properties, including team
cognition, are not necessarily reducible to the summed properties of team members
because team members interact. In this regard, metrics relying on consensus judgments,
communication, and coordination have potential for the measurement of team cognition.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 22
EMPIRICAL WORK ON TEAM COGNITION
Early empirical efforts to address the problem of team cognition were conducted in
social psychology (e.g., small group decision making) and business management with
applications to groups that were relatively homogeneous (e.g., juries, business teams).
Some concepts relevant to team cognition such as social loafing, social decision schemes,
and transactive memory have been studied in this context. The events of the 1980’s and
programs like the TADMUS program created the need to extend work like this to teams
in highly complex settings. Synthetic environments were built and empirical work was
conducted. Much of the empirical work examined team process and performance, but
there was always a strong emphasis on team cognition. In particular, the concepts of
shared mental models and team situation awareness received the most attention. In the
typical study variables are manipulated (e.g., distributed vs. co-located environments,
workload, team structure, communication mode) and their impact on team performance
and cognition is noted. We summarize some of this empirical work in this section.
Empirical Work on Small Group Decision Making
Social loafing is a phenomenon in which group size has deleterious effects on group
output. Specifically, the efforts (or motivations) exhibited by individual team members
are inversely related to group size (Latané, Williams, & Harkins, 1979), leading to
reduced collective output by the team as a whole. In an additive task, for example rope
pulling adding more and more pullers actually decreases the collective amount of
exertion in proportion to the summed pulling strengths of individual rope pullers
(Schoggen, 1989). According to the social loafing effect, the proportion of agents
expending maximal effort can be expected to drop as group size increases. Interestingly,
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 23
this effect cannot be explained in terms of overall process or coordination costs (cf.,
Steiner, 1972). For instance, researchers have replicated the social loafing effect while
controlling for process loss (e.g., Ingham, Levinger, Graves, & Peckham, 1974; Latané et
al., 1979) by simulating a coordinated task when in reality only one individual was
participating. One possible explanation for the social loafing effect is that the level of
social pressure for a given task remains constant regardless of group size, and that social
loafing manifests itself as this constant level of social pressure is distributed over larger
groups of agents.
A number of factors have been studied as possible mediators of the social loafing
effect on group output. For example, the requirement of individual accountability in a
group setting may actually reduce the social loafing effect (Tata, 2002) with direct
consequences for group performance (cf. Harcum & Badura, 1990). Everett, Smith, and
Williams (1992) found a negative relationship between increased group cohesion and
social loafing for female, but not male, swim teams. Social exchange relationships (e.g.,
leadership, dominance) may also be a useful factor for mediating the effects of social
loafing (Murphy, Wayne, Liden, & Erdogan, 2003). Perhaps most germane to the study
of team cognition, increased task interdependence (i.e., heterogeneity) in highly critical
(meaningful) task domains have been related to decreased social loafing (Wageman,
1999), such that agents involved in these types of groups may expend more, rather than
less effort proportional to group size. The increased interdependency among individuals
in teams may counteract the diffusion of responsibility, however social loafing by any
one team-member can lead to a loss of integrity for the whole team.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 24
Decision making in small groups has also been a topic of study originating with the
early work of Janis, 1972) on group think. Festinger (1954) found that members of a
group will likely distribute themselves in terms of decision input based upon the decision
making prowess of their counterparts through social comparisons. By setting and
attaining their comparisons to individuals of higher ability than themselves (an upward
comparison), the group decision can in some sense be of a higher quality than the
aggregate of individual decision making abilities. On the contrary, Steiner’s (1972)
model promotes the notion that this maximum is rarely, if ever, attained due to process
loss that occurs during the group interaction process. Social decision scheme theory
(Davis, 1973) uses these sorts of views to establish a priori group decision rules and then
examines the deviation of an observed group decision from a decision based combining
individual inputs via these decision rules (e.g., Hinsz, 1995; 1999).
The concept of individual memory has also been extended to small groups. A
transactive memory system is group memory considered in terms of group members’
knowledge about themselves and their knowledge about the capabilities of other group
members (Wegner, 1986). Numerous researchers have found a positive relationship
between transactive memory systems and group performance (Liang, Moreland &
Argote, 1995; Hollingshead, 1998; Moreland, 1999; Moreland & Myaskovsky, 2000).
For example, Liang, et al. (1995) found that participants who trained as a group in a radio
assembly task had significantly lower assembly errors and better procedural recall (i.e.,
an index of transactive memory) than participants who trained individually.
Efforts have been made to identify the source of the transactive memory benefit to
groups. Hollingshead (1998) investigated the effect of communication on learning and
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 25
recall of words in transactive memory systems. Specifically, in a study comparing recall
in dyads of dating couples or strangers, the effect of communication on word recall was
moderated by the nature of the dyad’s relationship. Moreland and Myaskovsky (2000)
conducted a study to clarify the role of communication in the radio assembly task and
found that the increase in performance found for groups was not the result of
communication during training. Groups trained without the chance to communicate
performed as well as those who could communicate once they were able to receive
information regarding others’ skills. Similarly Rulke and Rau (2000) found that groups
with high transactive memory declared domains of expertise early in group interaction,
and evaluations of others' expertise and ability increased in frequency over time.
In this regard, shared (overlapping) resource knowledge in transactive memory
systems has been proposed to elevate the memory capacity of a group to a level greater
than the sum of individual agents (Wegner, 1986; Liang, et al., 1995; but see also Pavitt,
2003). This result suggests that transactive memory systems are more efficient than
systems in which agents do not interact, instead aggregating their memory capacity based
on static, individual-level knowledge. In accordance with Steiner’s (1972) model of
process loss, however, this efficiency may break down as group size increases (e.g.,
Pavitt, 2003; Wittenbaum, 2003) due to exponential increases in establishing resource
knowledge. Although transactive memory systems may indeed be advantageous
constructs for studying smaller groups, the adaptability of transactive memory systems to
larger organizations raises some scalability issues. Other factors that may also affect the
viability of transactive memory systems include differential information distribution
(Fraidin, 2004), gender stereotyping in resource knowledge (Hollingshead & Fraidin,
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 26
2003), and mismatched distribution of expertise across agents (Hollingshead, 2000;
2001).
Shared Mental Models
Numerous researchers have reported that team mental model similarity influences
team processes and performance (Mathieu, Goodwin, Heffner, Salas, & Cannon-Bowers,
2000; Stout, et al., 1999). Specifically, the shared mental model literature indicates that
high similarity within a team should lead to preemptive process behaviors (Entin &
Serfaty, 1999) and thus effective team performance (Blickensderfer, Cannon-Bowers &
Salas, 1997; Converse, Cannon-Bowers, & Salas, 1991; Stout, 1995; Mathieu et al.,
2000). Additionally, team member mental models are assumed to converge over time
because of increased intrateam interaction (Clark & Brennan, 1991; Levesque, Wilson, &
Wholey, 2001; Moreland, 1999; Rentsch & Hall, 1994; Liang, et al., 1995). However,
the results have been mixed.
Mathieu, et al. (2000) reported a study assessing the influence of shared mental
models on team process and performance. Using a personal-computer-based flight
combat simulation, both task- and team-based mental models were assessed. Though it
was determined that both task- and team-based mental models were positively related to
team process and performance, neither type of mental model converged within a team
over time. Interestingly, Levesque, et al. (2001) found that the similarity of mental
models decreased over time, which may be functional for teams with highly specialized
roles (Cooke, et al., 2003).
Smith-Jentsch, Campbell, Milanovich, and Reynolds (2001) examined
convergence over a longer time frame. They assessed mental model similarity and
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 27
accuracy for teamwork knowledge through card sorting techniques and comparison to an
empirically-derived referent. In the first study, similarity with an expert model was higher
for Navy personnel with higher rank. Rentsch and Klimoski (2001) found that in a
naturalistic setting, factors associated with team homogeneity increased team member
schema similarity for teamwork knowledge and in some cases indirectly affected team
effectiveness (subjectively measured by the team members).
In some cases mental models have been “manipulated” through cross training
(e.g., Cooke, et al., 2003) or information available to participants through displays or
instructions. In some of these cases, however, though the manipulation impacted the
knowledge or mental model, it did not impact team performance (Cooke, et al., 2003;
Cooke, DeJoode et al., 2004).
Thus there are generally mixed results concerning the relationship between shared
mental models and team performance. As Cooke, Salas, Cannon-Bowers, and Stout
(2000) point out, there is probably some confusion stemming from the ambiguity of
terminology. What is meant by mental model that is not covered by the construct
“knowledge”? What type of knowledge or mental models should be considered (e.g.,
taskwork, teamwork, team member beliefs)? Even the term sharing can imply either
knowledge similarity among team members in which everyone knows the same thing or
knowledge distribution (see Figure 2). The measurement issues described earlier follow
in the footsteps of the ambiguous conceptualizations with the role of similarity, accuracy,
and applicability to heterogeneous teams being questioned (Cooke et al., 2000; SmithJentsch, et al., 2001). As a result of this early research, the concept of a shared mental
model, whereby team members are thinking the same thing has been tempered by use of
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 28
the concept of team mental model, in which team member knowledge is not overlapping,
but rather complimentary to a degree such that all domain knowledge is represented by
the summed knowledge of team members (Cooke et al., 2000; Klimoski & Mohammed,
1994).
Shared
Knowledge as
the Same
Knowledge
Shared Knowledge
as Common and
Complementary
Knowledge
Shared Knowledge as
Distributed Across
Team Members
Specialization
Figure 2. Varieties of shared knowledge.
Mixed results may also reflect the variability inherent in tasks and teams. For
extremely heterogeneous teams similar knowledge (of some form) should be expected
less as teams grow and team members become more specialized. Also, the overlap of
individual mental models is likely to be greater for highly structured task (Kraiger &
Wenzel, 1997). Finally, even if all of these measurement, task, and team factors are taken
into account it is not clear that the “knowledge construct” is strongly tied to team
performance. For example, the fact that highly experienced individuals within the
sesame domain are more cognitively similar than novices has been illustrated in multiple
domains such as combat flight maneuvers (Schvaneveldt, Durso, Goldsmith, Breen,
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 29
Cooke, Tucker, & DeMaio, 1985) and computer programming (Cooke & Schvaneveldt,
1988), however, this does not mean that the similarity or accuracy of a mental model
causes performance. Gorman and Cooke (in press) and Ensley and Pearce (2001) have
indicated that processes leading to shared cognition may be more important than the
outcome of shared cognition.
Team Situation Awareness
Situation awareness (SA) at the individual level has been a topic of much recent
interest, stemming from the world of aviation and phenomena recognized as important by
pilots (Durso & Gronlund, 1999; Endsley, 1995; Fracker, 1989; Orasanu, 1995;
Robertson & Endsley, 1995; Wellens, 1993). Endsley (1988) defined SA as “the
perception of the elements in the environment within a volume of time and space, the
comprehension of their meaning, and the projection of their status in the near future” (p.
97). Results in this area have shown that early collection and exchange of information,
along with planning, are linked with high levels of SA (Orasanu, 1995) and that high
levels of SA are linked with high levels of performance.
What then is team SA? In order to address the issue of heterogeneous teams with different
SA requirements, some have distinguished between shared (i.e., common to team members) SA
requirements and individual SA requirement, each of which is important to team SA (Endsley &
Jones, 1997). So, team SA has been primarily defined in terms of the collection of the SA
(shared or unique) of individual team members.
Team SA has been challenging to measure at the individual level because the situation
often changes more rapidly than individuals can be queried. Bolstad and Endsley (2003)
reported results for a study involving U.S. Army officers participating in a simulation
exercise. SAGAT, administered using the freeze technique, was used to measure each
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 30
individual’s SA. Composite scores were created by averaging the individual query score
for each SAGAT stop. Results indicated that accuracy on SA queries varied across the
cells in the Objective Force structure and was not shared to the degree expected within
cells; however, there was no information on performance provided and it is not clear
whether these teams required a common understanding of the information tested to do
their jobs. Cooke, DeJoode, et al. (2004) measured team SA in a UAV ground control
task using individual queries, as well as a consensus procedure. Though they found that
collective team SA correlated positively with team performance, they were concerned
that the measure was not as pertinent to the team’s awareness of the situation, as much as
the awareness of the experimental procedure (e.g., anticipating upcoming queries).
The two constructs of shared mental models and team SA have been theoretically
linked in that shared mental models or a long term shared understanding of the task,
team, or equipment is thought to be an important factor in team SA, and specifically the
construction of a team situation model (Cooke, et al., 2001). Therefore, some empirical
work has examined manipulations that would foster both shared mental models (e.g., via
shared information) and team SA (e.g., via shared displays) in order to improve team
performance (Bolstad & Endsley, 1999).
Empirical work on team SA is challenging given the difficulty pinning down a
precise and agreed upon definition and a satisfactory methods of measurement. Some
ideas and measures emerging from newer theoretical perspectives (e.g., CAST; Gorman,
et al., submitted) may provide reasonable alternatives to measurement.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 31
THEORETICAL PERSPECTIVES ON TEAM COGNITION
As we have described, research efforts in team cognition were initiated by a need
and quickly followed by application, measures, and empirical work. These early
developments were inspired by theory, measures, research, and applications in the closely
related fields of social, industrial-organizational, and cognitive psychology with close ties
to specific areas of small group behavior, organizational management, groupware, and
information processing. Theoretical developments were no different, with early
perspectives borrowing heavily from related disciplines and there was some convergence
on a theory of team cognition. We refer to this generic perspective as the IP (Information
Processing) perspective. More recent developments in those sister disciplines have led to
some new ways of thinking about team cognition. Specifically, research and theory on
distributed cognition (Hutchins, 1991), ecological psychology (Reed, 1996), dynamical
systems (Alligood, Sauer, & York, 1996), and Soviet-era activity theory (Leontev, 1990)
have inspired this view.
We call this perspective THEDA (Team Holistic Ecology and
Dynamic Activity). In this section of the chapter we briefly delineate these two
theoretical perspectives on team cognition.
The Information Processing Perspective
The earliest models of team cognition were heavily influenced by process-oriented
theories coming from the social psychology of small groups and industrial-organizational
psychology. Tushman (1979) found that high performing work teams will match their
coordination structure to the changing demands of their work. For instance, under high
levels of uncertainty and workload, decentralized coordination structure emerged in order
to adaptively distribute resources. Katz (1982) reported that although a stable or fixed
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 32
coordination structure may be preferable in the short-to-medium term, that very stability
can have deleterious effects on team information processing over the long term if it is not
flexible enough to handle the influx of new information. In this tradition Hackman
(1987) suggested that interaction processes could be studied as mediators of the effects of
individual, group, and environmental factors on team output and cohesiveness. This
approach to understanding team performance was called the input-process-output (I-P-O)
framework for work team productivity and became the standard model for early
conceptualizations of team cognition.
Applying the I-P-O framework to team cognition, cognition at the team level is
analogous to cognition at the individual level, insofar that knowledge structure is
distributed over team members, instead of over long term memory, and is operated on by
team process behaviors, instead of memory processes. A generic I-P-O framework is
presented in Figure 3.
Individual
Taskwork
Knowledge
Individual
Teamwork
Knowledge
Team
Process
Behaviors
Team
Outcome
Individual
Dynamic
Knowledge
Figure 3. A Generic Input-Process-Output (I-P-O) framework.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 33
The distribution of the various types of knowledge in Figure 3 are sensitive to
factors at the individual level, for example, team member demographics (Rentsch &
Klimoski, 2001); at the group level, for example, inter-team member perceptions (Dirks,
1999; Fiore, Salas, & Cannon-Bowers, 2001), as well as environmental factors such as
data quality (Mallubhatla, Pattipati, Kleinman, & Tang, 2001). These factors then feed
directly into the processing component of I-P-O frameworks. This is the stage analogous
to processes such as storage and retrieval at the individual level. In terms of team
cognition, process behaviors mediate between team member knowledge and team
outcomes. Various team processes include behaviors such as assertiveness, adaptability,
leadership, and communications (Brannick, Prince, Prince, & Salas, 1995) that are
thought to transform individual knowledge into effective team outcomes.
How does team cognition fit into the I-P-O framework? Interestingly, some have
conceptualized team cognition as an outcome (e.g., Mathieu, et al., 2000). Others have
considered collective cognition as an input in the I-P-O framework (e.g., Mohammed &
Dumville, 2001). In keeping with the early roots of the I-P-O framework, others have
viewed team cognition in terms of process behaviors such as planning and decision
making (e.g., Brannick, et al., 1995). So team cognition can and has been associated with
all parts of the I-P-O framework, however, there has been increasing focus on the “I” part
in which team cognition is thought of as the collection of individual team member
knowledge involving the task and team (Figure 4, Panel A.)
Views of shared mental models and team situation awareness as common
understanding, vision or knowledge across team members and the concomitant emphasis
on knowledge in cognitive theories of individual expertise (Cooke, 1994) turned the
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 34
spotlight toward the input side of the I-P-O framework. The focus was on the knowledge
or mental models and not the sharing processes. For example, these sharing processes
have been tied to knowledge was tied to process (e.g., Entin & Serfaty, 1999). Thus the
information processing perspective is knowledge-centric, rather than behavior-centric
(e.g., Mohammed & Dumville, 2001). At the same time, with this emphasis also came a
shift from decentralized notions of adaptive team coordination (cf. Tushman, 1979) to a
more knowledge-homogeneous, static view.
+
+
+
TEAM COGNITION =
Panel A
TEAM COGNITION =
Panel B
Figure 4. Team cognition as viewed from the IP (Panel A) and THEDA (Panel B)
perspectives.
THEDA: Team Holistic Ecology and Dynamic Activity
The THEDA perspective represents an alternative to the IP perspective on team
cognition, partially motivated by some limitations of the IP perspective (i.e., applicability
to heterogeneous teams, knowledge vs. process focus) and partially motivated by some
alternative views of scientific psychology (i.e., distributed cognition, Hutchins, 1991;
ecological psychology, Reed, 1996; dynamical systems theory, Alligood, Sauer, & York,
1996; and Soviet-era activity theory, Leontev, 1990). THEDA considers team cognition
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 35
as emergent, rather than a linear aggregate, and is thus focused on the dynamic interplay
among team members, rather than the static structure of team member knowledge. It is
accordingly, a perspective on team cognition that supports holistic rather than aggregate
measurement. The THEDA view (Figure 4, Panel B) is that team cognition is not
equivalent to the linear aggregate of individual team member cognition, but instead
emerges from the dynamic interplay between collective cognition and team member
interactions.
The THEDA perspective advocates holistic thinking about team cognition and
holistic measurement (i.e., measurement at the team level) rather than collective
measurement (measurement of individuals and aggregation) and is inspired by Gestalt
psychology (Cooke, et al., 2000; see also “collective cognition,” Gibson, 2001). Simple
aggregation rules (e.g., summing) are inappropriate for heterogeneous teams for which
there is a heterogeneous distribution of knowledge and abilities across team members
(Cooke & Gorman, in press; Gorman, Cooke, & Kiekel, 2004). In an aggregate the parts
are independent of their relations to each other while in a whole relations help determine
the nature of the parts. For holistic team cognition the relations among the parts are of
inherent interest, in addition to the static distribution of knowledge among the parts
themselves. THEDA is concerned with the team processing mechanisms by which the
whole team is structured, beyond the sum of the parts. THEDA’s emphasis on team
member interactions beyond a collection of team knowledge stores is also shared with
much of the small group work on decision making (Festinger, 1954; Steiner, 1972), social
decision schemes (Davis, 1973; Hinsz, 1995; 1999), and even transactive memory with
its emphasis on transaction or communication (Hollingshead & Brandon, 2003).
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 36
Borrowing concepts from ecological psychology, teams can be viewed as a set of
distributed perception-action systems that can become coordinated to the relatively global
stimulus information specifying a team-level event. By analogy, when we encounter fire
we see flames, we smell smoke, we feel the heat, we hear the crackle, etc.; our perceptual
systems are coordinated to the same stimulus information specific to fire. Similarly,
when an event occurs in the team environment, each team member is heterogeneously
attuned to different aspects of the event. These “perception-action” systems are all
attuned to the same event, they just extract information about it in different ways, in such
a manner that these systems need to be coordinated. The THEDA perspective thus
emphasizes team coordination (i.e., a team process) in response to events in the team
environment. In this manner, team cognition is characterized as a single organism,
ebbing and flowing and adapting itself to novel environmental constraints through the
coordination of a team’s perceptual systems. This process of adaptation is also consistent
with Soviet activity theory (Leontev, 1990) or how a team internalizes new information
in terms of information distribution across team members (cf. Artman, 2000).
In contrast to IP-oriented theories of team cognition in which regression is used to
predict team outcome at a single point in time, the THEDA perspective considers the
dynamic evolution of the “team as a system” using dynamical systems theory (Alligood,
et al., 1996; e.g., Losada & Heaphy, 2004). For example, the concepts of circular
causality, self-organization, bifurcation theory, and entrainment derived from dynamical
systems theory are consistent with the THEDA views (Cooke and Gorman, in press;
Gorman et al., submitted). This goes back to the early conceptualizations of team
cognition and the realization that coordination structure is dynamic, not static, and has to
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 37
continually evolve in order to handle the flux of information in highly complex team
environments.
EMERGING APPLICATIONS
As the study of team cognition has progressed, so have applications designed to
facilitate or improve team cognition. Some of these changes evolved with the new
measures and theories, whereas others came about as technology itself changed.
Training team cognition has relied more and more on advance training technologies
such as simulation of tasks, virtual reality, and models of teammates. Especially for
distributed command-and-control environments like the ones that gave birth to team
cognition, there is a growing tendency to blur the distinction between training and
operations. The mantra in the military is “train as we fight.” With these new training
technologies there is a great need for assessment of cognitive training requirements and
examination of issues of learning and transfer and retention in this train-as-we-fight
environment. Does learning happen faster and last longer in this setting or are we better
off isolating the essence of what we want to training from the complexities of the
environment?
Computer modeling of team cognition has also progressed by leaps and bounds
(Carley & Pretula, 1998). Modeling team cognition allows us to question a model in
order to ascertain answers that would perhaps be very difficult to ascertain in any other
way. Individual cognitive modeling approaches have been extended to team modeling
(e.g., Team-Soar, Kang, Waisel, & Wallace, 1998). The approach starts with the
individual cognitive model and populates teams with multiple interacting models
(Chapman, Ryder, & Bell, 2004). Computerized or synthetic agents are becoming
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 38
synthetic teammates and provide a richer training and research context for this work.
Synthetic enemy forces are also being used for similar purposes and indeed create the
realism in training and simulation that seems critical to battlefield applications.
However, this realism is limited by the fact that the enemy forces are programmed and
therefore, lack the flexibility and unpredictability of human enemy forces.
Artificial agents are also touted as decision aids, gathering information and
disseminating it to the appropriate team members as a way to improve team cognition.
However, agent assistants can only perform tasks that are delegated to them (e.g., route
planning, information gathering; Miller, Funk, Goldman, & Wu, 2004; Sycara & Lewis,
2004). Indeed groupware has progressed to facilitate much of the information push-and
pull-required of command-and-control teams through electronic repositories for ongoing
information sharing (e.g., Freeman, Hess, Spitz, et al., 2003). In general, groupware
applications have also been extended from the business domain to the military and have
increasingly focused on issues of team, rather than group, cognition (Gutwin &
Greenberg, 2004; Hinsz, 2001; Klein, 2001).
Finally, there have also been movements toward embedded and automated
assessment of teams for rapid intervention and improvement– a team version of
augmented cognition efforts. The idea is to embed measures of team cognition within an
operational environment in order to assess, diagnose, and ultimately intervene to improve
team cognition. This is a challenge for a number of reasons discussed in this chapter but
due to a concerted effort on team cognition measurement, progress has been made
(Cooke, in press).
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 39
With new ways of thinking about team cognition as represented in the THEDA
perspective discussed in this chapter, also come new applications. Indeed, though
different theoretical perspectives can be used to explain complex team behavior, the real
distinction in focus is often revealed in the applications stemming from each perspective.
For instance, the THEDA perspective would approach the problem of improving team
situation awareness quite differently than someone with an IP perspective. The IP
solution to this problem has been to design shared displays for teammates to enhance
building of a common picture or shared mental model (Boldstad & Endsley, 1999). In
contrast, the THEDA perspective would advocate improving the push-and-pull of
information in the face of novel events, perhaps using an artificial agent as coordinator.
Similarly, whereas the information processing approach focuses on training teamwork
skills and knowledge at the individual level, the THEDA approach suggests more holistic
training such as the kind a team might get coordinating while playing an internet video
game.
THEDA would also advocate modeling team cognition at a higher, more abstract
level (e.g., Decker, 1998) than the agent level modeling that is most common. Multiagent based modeling is inspired by information processing (e.g., Kang, et al., 1998) and
the question is whether groups of agents will be able to replicate phenomena of team
cognition. And if they do, the computational needs will be tremendous. The THEDA
approach would be to use higher level modeling approaches such as dynamical systems
theory to model teams and organizations at the team or organizational level (Gorman, et
al., submitted). Recent approaches to model teams at a higher level as inspired by social
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 40
network theory (Friedkin, 1998) are also consistent with this approach (Sanil, Banks &
Carley, 1995).
Team cognition conceived as an emergent system has a very intriguing application
in regard to individual cognition. Specifically, cognitive psychologists have struggled
with the fact that individual cognition defies direct observation. Theories of individual
cognition rely on inferences from directly observable behavior. This is not satisfactory
but seemingly is the best we can do. Alternatively, team cognition, conceived as an
emergent team property is observable in the coordination and communication behavior of
teams, as an open system that self-organizes around an environmental flux. Team
communication not as a window to team cognition, but is team cognition itself. Team
cognition is cognitive processing, but at the team level. Team cognition is cognitive
processing, but at the team level. That is, the team interactions serve cognitive functions
that integrate the thoughts of the system. And unlike individual cognition, it is out in the
open for all to directly observe. One potential application then of team cognition is to use
theories, methods, and data to generate hypotheses about individual cognition.
CONCLUSION
Problems such as the Vincennes incident are huge. After fifteen years of work in
the area of team cognition dedicated to problems such as this one, have we improved the
situation? Though precise measures of success are few and far between, it certainly
seems that the trajectory is a positive one. There is much more work to be done.
Questions remain unanswered regarding the measurement of team cognition and the
possibilities for empirical research are endless. Applications that directly address the
need do not wait for multi-year research programs and so there is also a need to
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 41
streamline research efforts on teams and organizations. Research in this area is resourceintense in terms of participants, equipment, experimenters, and time. Interestingly the
same technology that is being developed to address the problem (e.g., team-level
modeling, embedded team assessment) can also be applied to research programs to
streamline these efforts. So, yes, we have learned much about team cognition in the last
fifteen years and we have developed tools and technologies to improve decision making
under stress and to facilitate team coordination. But as always, we have much more to
do.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 42
ACKNOWLEDGEMENTS
The authors would like to thank Mike Letsky (Office of Naval Research, N00014-03-10580), Bob Sorkin and John Tangney (Air Force Office of Scientific Research, FA955004-1-0234), and Dee Andrews (Air Force Research Laboratory, FA8650-04-6442) for
ongoing support of the CERTT Lab’s program in team cognition. We are also grateful to
Jasmine Duran and Tamica Smith for their editorial comments on the chapter.
Cooke, N. J., Gorman, J. C., & Winner, J. L. (in press). Team cognition. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky,
and T. Perfect, Handbook of Applied Cognition, 2nd Edition Wiley.
Cooke, Gorman, & Winner 43
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