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Cognitive Engineering and Decision Making: An Overview and Future Course

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Cognitive Engineering and Decision Making:

An Overview and Future Course
Mica R. Endsley
SA Technologies
Robert Hoffman
Institute for Human-Machine Cognition
David Kaber
North Carolina State University
Emilie Roth
Roth Cognitive Engineering

ABSTRACT: The field of cognitive engineering and decision making (CEDM) has grown
rapidly in recent decades. At this writing, it is the largest technical interest group within
the Human Factors and Ergonomics Society. Work that falls into this area of research
and study is also widely practiced across Europe and in countries around the world.
Along with this growth, there is a significant need for a peer-reviewed journal that
focuses specifically on CEDM as a science and applied discipline. The Journal of Cognitive
Engineering and Decision Making is intended to meet that need. It will cover current
research, theory, and practice in ways that not only provide for the sharing of informa-
tion across interested parties but also serve to move the field forward. This will advance
theoretical bases; address outstanding scientific challenges; set new courses and direc-
tions; address methods, measurement, and methodological issues; and show useful
applications of this work in the development of operational and training systems in many
domains that are of significance to society, government, and business. In this article,
we provide background on the CEDM field and its areas of research. We use this as a
platform for further specifying the scope of this journal along three technical tracks.
Objectives are identified for each track as it supports the overall mission of the journal.
Finally, we provide information on an electronic companion to the journal that is intended
to support advances in the field through dialogue and access to further resources.

Overview of the Field

COGNITIVE ENGINEERING AND DECISION MAKING (CEDM) INVOLVES THE STUDY OF COG-
nitive work and the application of this knowledge to the design and development of
technology. This research has resulted in a large body of work that is employed by
practitioners in a wide variety of domains. Largely driven by the increased cognitive
demands inherent in sociotechnical systems that employ high levels of technology
(e.g., computerization and automation), the need arose for those practicing human
factors/ergonomics to exceed the boundaries of specifying the human-machine inter-
face at only a surface level (i.e., its perceptual and physical characteristics). Designing

ADDRESS CORRESPONDENCE TO: Mica Endsley, SA Technologies, 4731 E. Forest Peak, Marietta, GA
30066, mica@satechnologies.com. Visit the JCEDM Online Companion at http://cedm.webexone.com.
Journal of Cognitive Engineering and Decision Making, Volume 1, Number 1, Spring 2007, pp. 1–21
©2007 Human Factors and Ergonomics Society. All rights reserved. 1
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human-machine systems that truly support cognitive work requires a substantially

different class of human factors approaches, one that also specifies the logical struc-
ture, design, and functioning of the technologies as interactive components with the
human operator. The CEDM field incorporates an understanding of human cogni-
tion in many types of tasks and often in the presence of teamwork. Efforts to address
this need have fallen under a number of different topic areas and communities of
practice, which developed in parallel but largely focus on similar concerns and which
constitute the makeup of the CEDM field. These various areas of the CEDM field
are described next.

Cognitive Systems Engineering

The term cognitive engineering (CE) can be traced to work by Norman (1981,
1986) and Hollnagel and Woods (1983). CE emphasizes the application of knowl-
edge and techniques from cognitive psychology to the design of human-machine
systems (Woods & Roth, 1988). Cognitive Systems Engineering (CSE) is a variant of
CE that emerged at about the same time. CSE offers a broader, systems perspective
to the analysis and design of human-machine systems (Hollnagel & Woods, 1983).
The unit of analysis in CSE encompasses the phenomena that emerge at the intersec-
tion of people, technology, and work. Humans, technology, and work are analyzed
as a joint cognitive system (Woods & Hollnagel, 2006). People are viewed as goal
directed agents that adapt to the demands of the work settings and the affordances
(and limitations) of the technology available. The study of joint cognitive systems is
a process of discovering how the strategies and behaviors of people are adapted to
the purposes and constraints of the field of activity. This includes uncovering how
people adapt to exploit affordances in the environment and to work around com-
plexities as they pursue their goals.
Woods and Roth (1988) outlined several key aspects or features that need to be
explicitly taken into account by CE/CSE: (a) the importance of context, examining
what people actually do given real-world situations, goals, and constraints; (b) the
importance of the accessibility and utilization of situation-relevant knowledge and
its impact on cognitive behavior; (c) the need to rely on a systems perspective in
analysis and design that incorporates an active role for humans in shaping their envi-
ronment; (d) the need to incorporate complex systems and complex problems into
research paradigms to achieve useful and applicable research and design results;
and (e) a consideration of multiple cognitive agents, both human and machine, that
may interact as a basis for characterizing domains. Improving performance in these
environments calls for understanding the full problem-solving context and the chal-
lenges inherent in it, examining how people actually behave, and the knowledge and
strategies they employ when faced with real-world demands and when working in
conjunction with available tools. This can be achieved through cognitive task analy-
sis and cognitive work analysis methods as a basis for engineering systems.
“The focus of CSE is on how humans can cope with and master the complexity
of processes and technological environments, initially in work contexts, but increas-
ingly also in every other aspect of daily life” (Hollnagel & Woods, 2005, p. 1).

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Hollnagel and Woods (2005) emphasize that the focus of CSE is on analyzing what
a joint cognitive system does and why it does it – in terms of the external constraints
and affordances that shape performance – rather than explaining how it does it at a
detailed cognitive processing level. This emphasis on a macrolevel analysis of the
joint cognitive system and the forces that shape its performance contrasts with some
other approaches within the CEDM field, which work to provide detailed descrip-
tions of the cognitive processes and functions that underlie performance.

Naturalistic Decision Making

The study of naturalistic decision making (NDM) has also evolved as a focused
effort to describe how people make decisions in the real world. While some earlier
researchers described decision making as being based on recognizing patterns in the
situation that were matched to known patterns in memory (e.g., DeGroot, 1965;
Kuhn, 1970; Mintzberg, 1973; Dreyfus, 1979, 1981), the area of NDM largely blos-
somed around the work of Gary Klein (1989, 1993, 1986). NDM rejects certain pre-
vious research on decision theory (e.g., utility theory) as being largely normative
instead of descriptive; therefore, such research fails to capture critical aspects of how
people – particularly experts – actually make decisions. NDM specifically seeks to
provide rich descriptions of how people make decisions in the real world, as opposed
to within artificially contrived and constrained laboratory tasks. The environments
on which NDM focuses may encompass ill-structured problems, uncertainty, time
stress, risk, multiple and changing goals, multiple individuals, and experienced de-
cision makers.
Although NDM focused originally on the human as a decision maker, as opposed
to the more general cognitive agent discussed in the CSE literature, both fields have
broadly defined this role to include problem formulation (and reformulation), situ-
ation assessment, goal definition, plan generation and evaluation, plan monitoring,
and adaptive behaviors. Both fields also stress the importance of real-world task set-
tings for capturing and understanding the true nature of human cognition. NDM has
been very focused on cognitive processes in describing how people perform cog-
nitive work. In the NDM approach, it is the analysis of knowledge and skills under-
lying novice and expert performance that provides the basis for identifying leverage
points for improving performance and specifying requirements for training and deci-
sion aids (Crandall, Klein, & Hoffman, 2006).
More recently, NDM has expanded to include the analysis of macrocognition
(Klein, Ross, Moon, Klein, & Hollnagel, 2003). Similarly focused on the behavior of
experts, it concentrates on developing a description of a wide range of cognitive
functions. This focus is somewhat broader than historical NDM research and includes
processes such as attention management, mental simulation, common ground main-
tenance, mental model development, uncertainty management, and course of action
generation. Klein et al. (2003) described a key collection of functions, including prob-
lem detection, sensemaking and situation assessment, coordination, planning, adap-
tation and replanning, and naturalistic decision making. In macrocognition research,
these facets of the cognitive experience are contrasted with the microcognitive

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processes studies of traditional psychology (e.g., memory and attention) and are
thought to be the basis for causal descriptions of how fundamental mental operations
trigger one another.

Ecological Interface Design

Ecological interface design (EID) and cognitive work analysis (CWA) form another
branch of relevant work that falls into the CEDM field. EID was formulated by
Rasmussen and Vicente (1989) based partially on the work of Gibson (1979) and the
broader ecological psychology tradition, and also on the experiences of Rasmussen
and his colleagues in the process control industry at the RISØ National Laboratory
of Denmark. EID rejects the information-processing view of traditional psychology
and instead views aspects of the world as being seen directly in terms of their affor-
dances to the operator. “Ecological approaches tend to focus on meaning in terms
of functional significance” (Flach & Hancock, 1992, p. 1057). Like NDM, EID has
diverged from the practice of experimental psychology that often separated the study
of cognitive functions from the environment and the complex tasks in which the
tasks normally occur (Flach, 1990). Like CSE, ecological EID explicitly avoids dis-
cussion of cognitive constructs (such as attention or memory) and instead focuses
on the attributes of the work domain (its structure and constraints) and the affor-
dances which that domain offers based on operator goals.
EID and CWA were developed as a means of carrying out system design based
on ecological psychology tenets and on work domain models (Burns & Hajdukie-
wicz, 2004; Rasmussen, Pejtersen, & Goodstein, 1994; Vicente, 1999). These work
domain models focus on operator goals via a means-ends analysis and an abstrac-
tion hierarchy that describes the system to be controlled or the work domain. CWA
directs designers to create interfaces that reflect the work constraints captured in this
analysis and to group information based on the means-ends analysis (see Hoffman
& Lintern, 2006). In practice, EID/CWA work has concentrated more on analysis and
design and less on descriptions of cognitive processes or experimental approaches to
the study of cognition.

Mental Constructs
A significant amount of work in CEDM has also been conducted by a commu-
nity of practice that focuses on exploring and enhancing key mental constructs such
as situation awareness, mental workload, and mental models – all of which are seen
as fundamental contributors to effective human functioning in complex systems.
The mental construct area of CEDM includes work consistent with information-
processing models of human cognition (Wickens, 1992) but also embraces the wider
range of processes and functions examined in NDM and macrocognition research.
It contrasts with the CSE and EID approaches for describing and predicting human
interaction with complex systems by dealing more with internal aspects of cognition
and how they affect systems design.
Mental models are a cognitive construct that remains a focus of much CEDM
literature (Bainbridge, 1981; Johnson-Laird, 1980; Rasmussen, 1981; Reason, 1988;

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Rouse & Morris, 1985). Capturing and representing the mental models of individu-
als has proven difficult, yet mental models are believed to play a central role in guid-
ing human interaction with complex systems. Although CEDM often advocates the
design of systems to support people’s mental models, the difficulty of specifying those
models a priori has often made this difficult to put into practice.
While the concept of mental effort dates from early experimental psychology, the
study of mental workload became a major focus in the 1970s, in particular meas-
uring and reducing mental workload in complex and demanding environments
such as aviation (Hancock & Meshkati, 1988; Hart & Sheridan, 1984; Moray, 1979).
In addition to work that addresses automation approaches to reducing mental work-
load, current work in CEDM often focuses on the design of displays and multimodal
interface approaches based on multiple resource theory (Wickens, 1992) as a means
to reduce mental workload and improve the design of systems.
Vigilance in low-workload environments has also remained an issue of research
focus for the CEDM field even though its roots can be traced back many decades
(Macworth, 1948). This work continues even today, in areas such as security screen-
ing, aviation, and military systems. The effect of automation on vigilance and com-
placency has kept this stalwart of human factors in the stream of current relevance.
Beginning in the late 1980s, a focus on situation awareness (SA) began to emerge
as a key cognitive construct of interest, springing from the terminology and chal-
lenges of the aviation field. Situation awareness, an operator’s mental representa-
tion of the world around him at any given time, forms a key construct that largely
guides moment-to-moment decision making and performance in complex systems
(Endsley, 1988). Situation awareness differs from mental models in its emphasis on
the dynamic and changing situational features that an operator must keep up with.
By contrast, mental models evolve more slowly than situation awareness, which can
change from moment to moment.
Research on situation awareness has examined how people develop and main-
tain accurate and up-to-date mental representations of the systems they operate and
the world in which they and their systems operate, as well as designing systems that
support that critical construct. In a sense, NDM picks up where SA leaves off, deal-
ing with how the decisions are made based on SA; this makes SA largely complemen-
tary with NDM’s focus on decision making. Approaches to SA research have a variety
of theoretical ties (Adams, Tenney, & Pew, 1995; Durso & Gronlund, 1999; Endsley,
1988, 1995b). Often employing a more experimental approach than NDM or CSE,
work in situation awareness, like that on mental workload, has also focused on meas-
urement as a means of furthering research on the topic and evaluation of system
designs (Endsley, 1995a; Endsley & Garland, 2000b).
Under the premise that a key means of improving decision making and perform-
ance in complex, dynamic systems lies in improving and supporting operator situ-
ation awareness, SA-oriented design (SAOD; Endsley, Bolte, & Jones, 2003) has been
developed to create systems that enhance operator SA. This system is based on
Endsley’s theoretical model of SA and the body of research on SA conducted across
many domains. SAOD provides a methodology for design that is mapped to large-scale

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systems development, and features (a) SA requirements analysis using goal-directed

task analysis; (b) SA design based on 50 design principles, which include specific de-
sign guidance derived from the theoretical model in addition to principles for design-
ing to deal with system complexity, uncertainty, automation, alarms and diagnosis,
and team operations; and (c) SA measurement for system evaluation. SAOD training
programs are similarly developed based on this work (Endsley & Robertson, 2000;
Kaber et al., 2005; Riley et al., 2005; Strater, Jones, & Endsley, 2003).
In contrast to NDM and CSE, work in the mental construct area of CEDM is often
more experimental in nature. It relies on studies in natural settings or complex sim-
ulations, coupled with the controlled presentation of conditions and measurement
of mental constructs to further define these constructs and how they interact with
technologies to produce human-system performance. Unlike laboratory experiments
in traditional psychology, experimental work in CEDM emphasizes the importance
of task realism (per the relevant domain) and the incorporation of domain experts.
Virtual reality systems may be used to deliver high levels of fidelity in real-world
task simulations and, at the same time, allow for a high degree of experimental con-
trol to better elucidate the mediating effects of system design features.
Research on mental constructs can be seen as largely complementary with the
more descriptive approaches in CEDM, including CSE; it makes it possible to test
and explore, in more detailed and objective ways, the behaviors and hypotheses gen-
erated in the field through more subjective observations. This area of CEDM is also
heavily involved in system design – it emphasizes testing the effects of various tech-
nology implementations on the emergent properties of the human-machine system
and its performance characteristics. Thus, mental construct measurement has formed
an important part of this area of practice, along with the development of simula-
tions and synthetic environments for valid and generalizable testing of concepts and
system designs. Work on mental constructs also frequently serves as a foundation
for other CEDM work.

Computational Modeling of Cognition

Arising from the body of work on human cognition in using interactive systems
(Card, Moran, & Newell, 1983), computer models and simulations of cognition have
grown in popularity as mechanisms for describing human behavior with tools and
support systems, for predicting human-system interaction during the development
of new systems, and as a means of testing and further developing cognitive theories
themselves. This work features a wide variety of modeling approaches, including ACT-
R (Anderson & Lebiere, 1998), EPIC (Meyer & Kieras, 1997), GOMSL (John &
Kieras, 1986), SOAR (Newell, 1990), IMPRINT (Allender et al., 1997), and Bayesian
networks (Zacharias, Miao, Illgen, Yara, & Siouris, 1996).
Historical work in this area has focused on interface evaluations using these
cognitive modeling approaches or on selecting among design alternatives for actu-
al applications. Studies such as that by Gray, John, and Atwood (1993) have vali-
dated task-specific cognitive models against human performance data and provided
successful bases for decision making on system alternatives. Others have developed

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cognitive models as explicit hypotheses on aspects of cognition or behavior in par-

ticular contexts, including human performance with automated systems (Kieras &
Meyer, 1997). These models have been tested against human performance data to
determine whether the assumptions about memory processes or overarching cog-
nitive strategies coded in the models, for example, result in computer behavior that
mimics that of the human. This work has been useful for gaining further insights into
cognitive constructs as well as revealing limitations of specific modeling approaches.
Recent advances in software tools for coding and running computational sim-
ulations of cognition in context have dramatically increased the accessibility and
applicability of specific approaches – for example, ACT-Simple (Salvucci & Lee,
2003), G2A (St. Armant & Ritter, 2004), and EGLEAN (Kieras & Wood, 2002) – for
describing and predicting human behavior with complex systems. These tools can
serve to accelerate the cognitive model-based evaluation of interfaces from a usabil-
ity perspective and may advance the pace of discovery of aspects of cognition.
Cognitive modeling has a rich pedigree in the mental construct area of CEDM
and traditional psychology research. It has emerged as an important area for usabil-
ity evaluation and mental construct development. This direction of work may pro-
vide a bridge between the more descriptive approaches of CEDM – including CSE
and CWA – with the mental constructs area in the future.

Automation
As our previous discussion makes clear, all of the communities of practice that
CEDM represents focus (at least in part) on the design of better information technolo-
gies to support cognitive work. This includes those who work extensively on auto-
mated systems and decision support systems (DSS), to cite two examples. Although
complex technologies and systems underlie the work that is studied in much of the
CEDM field, automation and DSS pose a particular challenge for human cognition.
They form unique (semi-) intelligent machines of their own with which operators
must interact in order to accomplish their work, thereby creating unique challenges
for the operator, who must understand and effectively interact with the automation
(Wiener & Curry, 1980). This often can create new workload of a different type,
which can be as demanding as that which is replaced (Bainbridge, 1983; Wiener,
1988). Automation usage can lead to unique problems with understanding the
automation and force the operator to develop creative approaches for dealing with
the automation – approaches that are seldom what the developer intended (Para-
suraman & Riley, 1997; Sarter & Woods, 1995; Koopman & Hoffman, 2003).
In response to these challenges, researchers in the field have focused on automa-
tion reliability and trust (Lee & Moray, 1992; Riley, 1994), on determining appropri-
ate levels of automation in order to avoid out-of-the-loop problems and to maintain
operator SA (Endsley & Kaber, 1999; Endsley & Kiris, 1995; Kaber & Endsley, 1997;
Parasuraman, Sheridan, & Wickens, 2000), and on approaches to adaptive automa-
tion that result in effective transfers of control between humans and automation (Kaber
& Endsley, 2004; Kaber & Riley, 1999; Parasuraman, 1993; Scerbo, 1996). This
broad corpus of work continues to expand as engineers develop different and more

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sophisticated forms of automation. These include decision support systems and intel-
ligent agents of various kinds for a wide variety of domains, including medical sys-
tems, driving, aviation, and process control. Similar to work in the mental constructs
area, most work on automation has featured experimentation in both field settings
and simulations, although observations in domain settings have also been prominent.

Collaborative Work
A more recent focus in CEDM has been on the cognitive work of people in
teams, either colocated or distributed, who must interact and collaborate to accom-
plish their tasks. Although there is a large body of research on teams similar to that
on individuals, much of it is derived from laboratory studies with contrived tasks
and teams of undergraduate students (McGrath, 1991). The focus on collaborative
work in the CEDM field has been, rather, on how actual teams perform tasks in com-
plex, real-world settings such as aviation, medicine, and command and control
(Bolstad, Riley, Jones, & Endsley, 2002; Klein, Zsambok, & Thordsen, 1993; Orasanu,
1990; Salas, Dickinson, Converse, & Tannenbaum, 1992; Xiao, Mackenzie, & Patey,
1998). The processes and dynamics observed in these situations are often different
from those observed in laboratory studies because of real-world circumstances and
the expertise of the operators. This growing body of research clearly expands CEDM
toward the development of training and technologies to support team collabora-
tion (Bolstad & Endsley, 2005; Potter & Balthazard, 2002; Prince & Salas, 2000;
Sonnenwald & Pierce, 2000). This work incorporates a wide variety of observation-
al and experimental methods and actively contributes to the development of train-
ing programs as well as collaborative tools and shared displays for supporting shared
situation awareness and team performance.

Summary
As we have shown, these various branches of the CEDM field come together
and complement one another in often serendipitous ways. As the same phenomena
are viewed through a multitude of perspectives, the results form a mosaic that can be
pieced together to form a more complete guide for systems design and training appli-
cations than is possible through any single approach.

Future Directions
Although the foregoing account depicts the origin of CEDM and how it is prac-
ticed today, it is also worth discussing where the field should be headed in the future,
given that a key goal for the Journal of Cognitive Engineering and Decision Making is
to advance the science and its applications. First, though much of CEDM developed
from dissatisfaction with the tools, concepts, and methods of previous engineering
psychology and decision-making research, as well as the sterility of the psychology
laboratory, it is not enough to be against something. To move forward as a field of
research, CEDM must provide methods for analysis, design, and evaluation that pro-
vide guidance for improving human performance in these complex domains.

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In many cases, the groundwork for these tools and methods is largely in place;
there are a variety of cognitive task analysis and modeling methods, for instance, and
experimental approaches and measures are widely available. In other areas the field
needs to move forward. Defining what is acceptable for descriptive studies of cogni-
tive phenomena is necessary, just as other areas of science (such as anthropology) have
done for their fields.
Another major area for growth will be the need to more clearly delineate how
to translate the many theories and findings of CEDM research into effective system
designs for supporting these cognitive processes. If that translation remains largely
subjective, varying from designer to designer and nongeneralizable from system to
system, then we will have failed at our central mission. For CEDM to move forward
as an effective force in system design, its theories and research must be specifiable
into clearly articulated and repeatable design guidance. Likewise, there is a need to
develop systematic approaches for translating static measures of learning and dy-
namic measures of performance that are generated by computational models into
specific methods for determining usability in interface and system design.
It is also evident that the domains of CEDM practice have expanded over the
past decade. Early concentrations featured work in aviation, air traffic control and pro-
cess control, with more recent work also encompassing the medical field, command
and control in both military and commercial applications, and unmanned vehicle
control. This shift most likely represents a broadening of the domains that are seek-
ing CEDM solutions rather than a shift in the domains that CEDM researchers think
are important.
It is likely that the domains of interest will continue to grow and change in large-
ly unpredictable ways. One of the great strengths of CEDM is its applicability across
many seemingly disparate areas, from the control of unmanned aerial vehicles to
diabetics’ self-monitoring of glucose levels. A resultant opportunity is provided by
this fact, as we will find new types of cognitive phenomena under new contexts and
unique challenges that can be solved with CEDM approaches. It also allows for the
exploration of the boundaries of our current methods and theories and provides an
advantageous path for evolution and the bridging of gaps between the various com-
munities of practice that make up CEDM.

Purpose and Scope of the Journal

It should be clear that CEDM is comprised of a mix of research approaches and
contributions that are in some ways heterogeneous and at the same time reflect
decades of cross influence. In some cases, researchers within communities of prac-
tice have come to recognize phenomena or issues that have been well known to prac-
titioners in other communities. In other cases, seminal ideas from one community
have been embraced by others. Thus, there has been significant cross-fertilization.
As such, we are taking a “big tent” approach to the Journal of Cognitive Engineering
and Decision Making in the belief that the science and the field will be best served by
capturing the best of what these different approaches have to offer.

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Because there is so much diversity, however, a one-size-fits-all approach to the

review and evaluation of such work would be neither appropriate nor fruitful. There-
fore, we have chosen to feature three different (although not completely independ-
ent) tracks in this journal as a means of facilitating the review process. This need
not lead to confusion for CEDM researchers – manuscripts are submitted to the
journal editor and then are directed to the appropriate track editor for evaluation
and further action.
The first track, Cognition in Context, features work in the naturalistic and de-
scriptive vein of CEDM, sometimes called cognitive field research. It covers papers that
focus on describing the nature of work in various settings and the types of cognitive
process that people employ “in the wild.” The second track, Studies in Simula-
tions and Synthetic Environments, features work that is typically of a more exper-
imental nature, generally employing more manipulable high-fidelity representations
of the task domain that allow for objective assessments of cognitive phenomena and
technology and automation characteristics. The third track, Design of Complex and
Joint Cognitive Systems, addresses the need for an outlet for very different types
of papers that show how CEDM theories, principles, and research can be translat-
ed into training programs or workplace technologies that support individual and
collaborative cognitive work. In all three tracks, research studies, theoretical papers,
case studies, and methodological analyses are all appropriate contributions. We de-
scribe each track in more detail in the following sections.

Cognition in Context Track: Scope, Criteria, and Objectives

The Cognition in Context track features reports that represent advances in the
science of cognition and macrocognitive phenomena in natural work environments.
This includes naturalistic and ecological studies of domain-embedded knowledge
and reasoning and may involve cognitive task analyses, cognitive work analyses, cog-
nitive field research, or knowledge elicitation. Empirical studies are not constrained
by method and can span the range of naturalism-experimentalism, including vari-
ous kinds of interviews (ethnographic, sociological, and cognitive), experiment-like
procedures conducted in field settings, observations of work patterns and work-
spaces, and attempts to “bring the world into the laboratory.” Likewise, viewpoints
or theoretical emphases are not constrained and can include cognitive views, com-
putational ones, situated or distributed cognition views, or sociological, ethno-
graphic, and anthropological views. As with the other tracks in Journal of Cognitive
Engineering and Decision Making, we are interested in studies with important appli-
cations in areas such as human-centered computing.
Criteria include the dimensions listed in Table 1, especially ecological and epis-
temological utility, novelty, and generality. The track is especially suited for studies
that advance the methodology of cognitive task analysis, including evaluations of re-
search methods, comparisons of methods, the development of new methods, and the
general exploration of methodological issues and issues of measuring cognitive work.
Priority will be given to papers that are salient for the CEDM community in that
they represent a contribution to theory or methods beyond what is already known

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in such fields as human factors, naturalistic decision making, cognitive systems

engineering, and the sociology of the professions. Ideally, papers are desired that
are salient for the domains of practice that are studied in the research. Thus, this
track focuses on studies that include analyses of many domains of cognitive work
and that rely on the participation of domain practitioners.

Studies in Simulations and Synthetic Environments Track:

Scope, Criteria, and Objectives
The focus of this track is on the discovery and description of cognitive process-
es and constructs, information processing theories and methods (as well as alternate
viewpoints and theories), and an assessment of the implications of technology

TABLE 1. Dimensions that typify CEDM research (after Hoffman & Deffenbacher, 1993)
The Relation of Methods to the Ecology of Cognitive Work
Ecological validity Materials, tasks, and settings present events in a way that preserves
their natural forms and the natural covariation of dimensions or
cues.
Ecological relevance Materials or tasks involve things that people actually perceive or do.
Ecological salience Materials or tasks involve important things that people actually
perceive or do.
Ecological representativeness Materials or tasks involve things that people often perceive or do.

The Relation of Methods to Scientific Understanding

Epistemological validity Materials and tasks make sense in terms of available theories and
accepted methodologies
Epistemological relevance Materials and tasks link to current theoretical or methodological
issues.
Epistemological salience Materials and tasks link to theoretical concepts or research issues
that are generally regarded as important.
Epistemological Materials and tasks rely on theoretical and methodological
representativeness concepts on which scientists often rely.

The Relation of Results to Action

Ecological utility The results help you do important things.
Ecological novelty The results help you do new things.
Ecological generality The results help you do things in diverse contexts.

The Relation of Results to Scientific Understanding

Epistemological utility The results lead to refinements in hypotheses and theories.
Epistemological novelty The results suggest new theoretical concepts or hypothetical
mechanisms.
Epistemological generality The results have implications for diverse theories or hypotheses.

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characteristics on human performance through the use of cognitively rich simula-

tions involving individual experts or teams. Reports of research with computer-based
simulations or Internet-based simulation studies are also sought.
Historically, human factors research has focused on perceptual and psychomotor
aspects of information processing as a basis for guiding human-machine interaction
and systems design (e.g., Sanders & McCormick, 1993). This was partly attributable
to a lack of sensitive and reliable measures for assessing more imbedded cognitive
constructs and the difficulty of relating results to performance and design decisions.
Systematic approaches to design that integrated the knowledge of engineering psy-
chology with concepts of automation, such as supervisory control (Sheridan, 1992),
were developed, resulting in many general guidelines for human-machine interface
features for practitioner use. Although this work forms an important foundation for
the human factors discipline, there remains a need for experimental and modeling
work in CEDM that addresses the challenges of complex systems and cognitive work.
This research should provide a basis for design guidelines rooted in explanations
of why cognitive behaviors occur and in some cases cause errors or performance
decrements in complex systems control. A deeper understanding of cognitive process-
es in human-automation interaction under critical conditions can lead directly to
design decisions that support cognitive performance.
The aim of the Studies in Simulations and Synthetic Environments track is to
showcase empirical and modeling research that provides a basis for systems engi-
neering and design by elucidating cognitive phenomenon under critical task circum-
stances. This type of research typically requires realistic simulations of actual task
environments that allow a high degree of control over independent variables and
the use of advanced cognitive and information-processing models for the specifica-
tion of hypotheses on mental constructs and for testing. This track provides a forum
for research that is relevant to all aspects of cognition and the use and development
of advanced simulation tools (e.g., virtual reality) for assessment. It is a forum for
presenting to the human factors community new CEDM research tools, new CEDM
methods for empirical research, designs of interactive simulation systems, results
from field and lab studies involving simulations and task experts, and descriptions
of new mental construct theories based on simulation research.
Relevant topic areas include the following:

1. Design and development of simulation platforms and synthetic environments

for studying contemporary CEDM domains, including health care, medical
systems, and human-robot interaction (e.g., unmanned-vehicle control).
2. Design and validation of generalizable PC-based or Internet simulations (e.g.,
high-fidelity flight simulations, PC-based human-in-the-loop simulations) for
CEDM research and evaluation of specific interface interaction techniques.
3. Adaptation of existing CEDM research methods, including measures of cog-
nitive workload (e.g., EEG indices of engagement) and situation awareness
(e.g., probes and testable responses), for use in synthetic environments and
real working environments through validation using simulation.

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4. Development and demonstrations of cognitive models (e.g., GOMSL, ACT-R,

SOAR, IMPRINT) for explaining and predicting task expert performance in
complex systems controls under nominal and hazardous states of awareness.
5. Development of new approaches for systematically translating the results of
cognitive model applications to guidelines for systems interface design to
support operator SA and workload management.
6. Use of cognitive models in conjunction with synthetic environments for eval-
uating explicit theories on mental constructs and complex task performance
strategies involving human interaction with decision support systems or intelli-
gent agents.

The review of papers submitted to this JCEDM track includes a consideration

of the following criteria:

• Papers must fit the types of research considered by the Studies in Simulations
and Synthetic Environments track.
• Papers must specifically address the success or failure of an approach and
provide explanations.
• Papers must make clear the new insight provided into one of the areas of
CEDM research and how its results compare with previous research, as well
as any broader impact for advancing CEDM practice.
• Papers must provide a validation section on any new methods or an example
of the application of new theories.

Beyond these criteria, papers must provide a clear rationale for design decisions,
and the stages of the design process must flow logically. The outcomes of the re-
search must be directly linked to specific CEDM research needs. The manuscript
must demonstrate how any new simulation or synthetic environment will advance
CEDM research.
For empirical studies, papers must present a concise set of hypotheses that moti-
vate the specific experimental manipulations in a simulation. Results of the experi-
ment must be linked back to the hypotheses through concise discussion. Theory
and research modeling manuscripts must summarize a corpus of simulation or syn-
thetic environment-based studies in an area of CEDM research that supports the new
general theory. That is, all theories must be given a pedigree in existing related CEDM
theories. Manuscripts of this nature must also provide at least one detailed example
of how the new theory may serve to identify underlying factors in CEDM research
problems or explain human information processing in the context of interaction with
complex systems.
The electronic or online companion (described later) can also be used as a re-
source by authors who submit their work to the Studies in Simulations and Synthetic
Environments track, particularly for presenting dynamic content, such as videos of
simulations used in experiments. As for the Design of Complex and Joint Cognitive

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Systems track, we encourage potential authors to consider the use of the electronic
companion to JCEDM.

Design of Complex and Joint Cognitive Systems Track:

Scope, Criteria, and Objectives
The Design of Complex and Joint Cognitive Systems track focuses on the process
and product of innovative design. This includes the design of training and support
systems for individuals and teams working with complex sociotechnical systems.
Much research in CEDM has traditionally addressed the analysis of cognitive sys-
tems in context. It relies on a rich set of analysis tools that yield powerful insights
into how individuals and teams make decisions in naturalistic situations and the
contextual factors and constraints that shape performance. However, the process of
drawing design implications from analyses of naturalistic decision making in context
remains largely an art. Although there have been some advances in developing meth-
ods and tools for deriving principled design guidance, there is often a significant gap
with regard to how to convert the results of contextual analyses into specific impli-
cations for design of innovative systems that more effectively support performance
(Potter, Elm, Roth, Gualtieri, & Easter, 2002; Wampler et al., 2006).
The Design of Complex and Joint Cognitive Systems track aims to showcase
research targeted at bridging the gap between analysis and design. The track encom-
passes research papers that are broadly relevant to the design of innovative systems.
It provides a vehicle for presenting creative design research that has not traditionally
had outlets within the human factors and related communities, as well as theoretical
and methodological papers aimed at advancing the theory and practice of design.
Relevant topic areas include the following:

1. Theories, methods, and studies of design that contribute to our understanding

of how to design systems that more effectively foster cognitive and collabora-
tive work of individuals, teams, and organizations. Studies might involve
evaluations of systems or envisioning exercises and empirical analyses that
capture critical information about the impact of system designs on cognition
and collaboration.
2. Innovative design concepts and their theoretical rationale. The goal is to
describe the design concepts, support rationale, and design principles they
embody. Formal evaluation studies of the design concepts are not required,
but papers need to present clear, persuasive evidence of the effectiveness of
the design in the domain of application and the generality of the design or
design principles embodied in the design, beyond the particular application.
3. Theories, methods, and case studies addressing the role of CEDM in the wider
systems design enterprise. This includes papers that examine the role of analy-
sis, envisioning, and evaluation within the broader context of design and
engineering of large, complex systems.
4. Theoretical perspectives that stimulate dialogue on important topics in the
theory or practice of design. This includes literature synthesis papers that pre-

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sent a particular perspective on the theory or practice of design. Papers that

can provide a focal point for additional shorter commentary papers reflecting
alternative perspectives on the core theme are particularly appropriate for
the track.

Evaluation criteria for papers submitted to the design track will necessarily dif-
fer from evaluation criteria traditionally used in the human factors field to evaluate
research using conventional experimental design methods. Criteria for evaluating
design papers include the originality of the presented concepts, how well argued and
rigorously supported are the claims made, and how significant the contribution is to
CEDM theory and practice (see also Table 1). In the case of papers that present inno-
vative designs, additional evaluation criteria are reflected in these questions:

• Are the proposed design concepts grounded in an analysis of the domain and
work context?
• Is persuasive evidence provided for the effectiveness of the design? (This can
include laboratory or field study results.)
• Are the results generalizable beyond the specific application?
• (In the case of new methods, models, or tools for design) How innovative is
the approach? What evidence is provided that the method contributes to suc-
cessful design? (This can include presentation of illustrative case studies of its
application or formal evaluations.)
• (In the case of papers that present literature syntheses and new theory) Does
it address an important gap in the theory or practice of design? Is it likely to
stimulate dialogue among research and practitioners so as to advance the state
of the art?

A final point to highlight is the availability of a companion online forum for

Journal of Cognitive Engineering and Decision Making readers (see the next section). This
electronic resource is particularly attractive for communicating aspects of innovative
design research that are not well captured by traditional media. Authors can include
links to companion electronic media to provide dynamic depictions of design con-
cepts, video clips of user interactions that motivated the design, or video clips that
depict use of the design in context. We strongly encourage authors to take advan-
tage of this powerful multimedia communication resource.

JCEDM Online Forum

In addition to a regular journal format, we envision a broader approach that
takes advantage of the new capabilities provided by the Internet. The accelerating
pace of development and evolution in electronic publishing, including the increased
rate of citation of electronic venues, seems to include ample evidence of the utility
of the new format for scientists. In this vein, we are providing an online forum for
JCEDM. This Internet companion is intended to spur the growth of the CEDM field
and to serve the needs of researchers and practitioners.

CEDM: An Overview and Future Course 15

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To take advantage of this tool, go to http://cedm.webexone.com. Click on the link

to enter as a guest. On this site you will find

• traditional and electronic contact information for authors;

• links to the online version of the journal for viewing by those who subscribe
to the online version, or for direct purchasing of articles;
• links to online resources (e.g., Web sites) associated with material discussed
in an article, as provided by the authors;
• an index of JCEDM articles
• online discussions and debates concerning the articles in the journal or
topics of interest in the field;
• calendars of conferences and events of interest to CEDM researchers and
practitioners;
• databases for sharing documents or other information amongst participants
(e.g., bibliographies or cognitive task analyses)
• links to universities and organizations that practice CEDM.

This unique feature allows the Web site and JCEDM to jointly serve as a central
hub for researchers in this field. It is up to those within CEDM to supply the con-
tent and energies that will make it successful. It is a movable landscape and one that
will adapt as the field changes and grows.

What’s Next?
We invite you to enjoy this inaugural issue of the Journal of Cognitive Engineering
and Decision Making. We want you to make this journal your journal. Information on
subscribing to JCEDM and on submitting manuscripts for publication is provided
inside the printed issue (or at http://www.hfes.org/Publications/ProductDetail.aspx?
ProductID=64). If you have ideas or recommendations for JCEDM, please contact
us by e-mail or through the online companion Web site, http://cedm.webexone.com.
As editors of the journal, we stand ready, along with the JCEDM online forum, for
your contributions to the CEDM field.

Acknowledgments
The contribution of the first two authors was made possible partly through par-
ticipation in the Advanced Decision Architectures Collaborative Technology Alliance,
sponsored by the U.S. Army Research Laboratory under Cooperative Agreement
DAAD19-01-2-0009.

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Mica R. Endsley is president of SA Technologies, 3750 Palladian Village Dr., Building 600,
Marietta, GA 30066, mica@satechnologies.com. Dr. Endsley was a visiting associate professor
at MIT in the Department of Aeronautics and Astronautics and associate professor of indus-
trial engineering at Texas Tech University. She received a Ph.D. in industrial and systems engi-
neering from the University of Southern California. She has published extensively in the areas
of situation awareness, decision making and automation, and is coauthor of Designing for
Situation Awareness.
Robert R. Hoffman is a senior research scientist at the Institute for Human and Machine
Cognition in Pensacola, FL. He is a Fellow of the American Psychological Society, a Fulbright
Scholar, and an Honorary Fellow of The Eccles Center for American Studies of the British
Library. He received his B.A., M. A., and Ph.D. in experimental psychology at the University of
Cincinnati. His latest books are Working Minds: A Practitioner’s Handbook of Cognitive Task
Analysis, Expertise Out of Context, Minding the Weather: How Expert Forecasters Think, and The
Cambridge Handbook of Expertise and Expert Performance.
David B. Ka ber is an associate professor in the Edward P. Fitts Department of Industrial &
Systems Engineering and an associate faculty in the Department of Psychology at North
Carolina State University. He received his Ph.D. in industrial engineering from Texas Tech
University in 1996.
Emilie Roth is a cognitive psychologist (Ph.D. from the University of Illinois at Champaign-
Urbana, 1980). She is principal scientist of Roth Cognitive Engineering. She has been involved
in cognitive systems analysis and design in a variety of domains, including nuclear power plant
operations, railroad operations, military command and control, medical operating rooms,
and intelligence analysis.

CEDM: An Overview and Future Course 21

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