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The overriding principle in human factors engineering is to center
the design process around people, thus making it a humancentered
design process [32]. In other words “honor thy user.” The
human-centered design of a system or product revolves around
the users. It mustmeet their needs and be compatible with their
abilities [33].

We put this principle into practice involving users in all stages
of the design process. That is, the human factors specialist will
study the users’ job or tasks, elicit their needs and preferences,
ask for their insights and design ideas, and collect data on their
response to design solutions. User-centered design does not mean
that the user designs the product. The goal of the human factors
specialist is to find a systemdesign that supports the user’s needs
rather than designing a system to which users must adapt.

A holistic perspective or systems thinking is an important part of user-centered design. Rather than considering elements of the
design as independent, unrelated parts, systems thinking focuses
on the interaction and relationships of parts—a focus on the whole
rather than just the parts. Such holistic thinking can identify important
benefits for integrating elements of a device, such as making it
possible to place a call from a smartphone by touching (rather than
dialing) a number on a webpage. Systems thinking can also avoid
unintended consequences. For example, shortening the shifts of
healthcare workers to reduce fatigue and improve healthcare quality
can have the unintended consequence of increasing the need
to handoff a patient from one healthcare worker to another. More
handoffs can undermine healthcare quality [34].

major phases: understand users, create a prototype, and evaluate
the prototype [35]. Understanding users involves careful observations
of people and the tasks they perform to the point of
establishing empathy for their situation. Creating a prototype involves
designers combining this understanding with a knowledge
of human characteristics, interface guidelines, and principles of
human behavior, which we will discuss in later chapters, to produce
initial design concepts. Soon after these initial concepts are
developed, designers evaluate these prototypes. Evaluating can
include heuristic evaluations and usability tests with low-fidelity
mock-ups or prototypes. Usability tests are particularly useful because
they often help designers better understand the users and
their needs. This enhanced understanding provides input to the
next cycle of creating an improved prototype.

Figure 2.2

The Understand, Create, and Evaluate cycle describes design
as an iterative cycle that repeatsmany times at multiple time scales.

Figure 2.2 shows the cycles of the design process, gravitating
from inside to out, as the prototype evolves into a final product.
The cycles vary in how long they take to complete, with the outer
cycles taking months or years and inner cycles taking minutes. In
the extreme, onemight complete a cycle during an interview with
a user where the designer creates a simple paper prototype of a possible solution, and the user provides immediate feedback. Taking
hours rather than seconds, a heuristic evaluation, where the
design principles and guidelines are applied to the prototype, can
quickly assess how design might violate human capabilities. Usability
tests typically take days or weeks to collect data from how
end users respond to the system, and so provide amore detailed
and precise understanding of how people will react to a design.
The inner elements of the design cycle provide rapid, but approximate
information about how a particular design might succeed
in meeting people’s needs, and the outer elements of the cycle
are more time consuming, butmore precise. This speed-accuracy
tradeoff means that the time and resources needed to understand,
create, and evaluate should be matched to the system being developed.
Rapidly changingmarkets place a premiumon fast and
approximate methods.

Y Human factors experts conduct
heuristic evaluations
and involve no end users; usability
tests collect data from
end users.

Usability tests are conducted multiple times as the interface
design goes through modifications. Each repetition of the testing
and modification cycle can produce significant improvements, and
many iterations of design should be expected. At the beginning,
it is not necessary to worry about the details of screen design or
making the screens look elegant. Rather, the emphasis should
be on identifying useful functions, and how the user responds
to those functions. This iterative process has been shown to be
incredibly valuable in refining software, hardware, and even work
process designs. Although each usability test typically includes
only five people (see Chapter 3 for more detail), as many as 60
cycles of testing can provide benefits that outweigh the costs [36].
At a minimum, three to five iterations should be considered and
one can expect improvements of 25–40% for each iteration [37].

Y Iterative design is central to
understanding and meeting
people’s needs.

When the system design nears completion, it may be placed in
an operational environment for comprehensive testing and evaluation
(see Chapter 3). This evaluation can be considered the final
step of product development. It can also be considered as the first
step in developing a better understanding of the user for the next
version of the product. The outermost cycle in Figure 2.2 indicates
that even after the product is released, the cycle continues with
data being collected to understand how people use the system.
For many consumer products, early beta versions of products are
released for this purpose, but this is not the case for high-risk systems.
For high-risk systems post-release surveillance to detect
design flaws is important. In the automotive industry, post-release
surveillance occasionally results in recalls to fix design flaws that
were not detected during the design process. The remainder of this
chapter describes critical elements of each of these three phases—
Understand, Create, and Evaluate—with a focus on understanding
the user.

Many products and systems are designed without adequate consideration
of human factors. Designers tend to focus on the technology
without fully considering its use from the human point of
view. In a book every engineer should read, Norman [23] writes:
Why do we put up with the frustrations of everyday
objects, with objects thatwe can’t figure out howto use,
with those neat plastic-wrapped packages that seem
impossible to open, with doors that trap people, with
washing machines and dryers that have become too
confusing to use, with audio-stereo-television-videocassette-
recorders that claim in their advertisements
to do everything, but that make it almost impossible
to do anything?

Even when designers attempt to consider human factors, they
often complete the product design first and only then hand off the
blueprint or prototype to a human factors expert to evaluate. This
expert is then placed in the unenviable position of having to come
back with criticisms of a design that took several months to develop.
It is not hard to understand why the design team would be less than
thrilled to receive the results of a human factors analysis. Designers
clearly believe in the design, and so are often reluctant to accept human
factors recommendations. Bringing human factors analysis at
the end of the design process places everyone involved at odds with
one another. Because of the initial investment and the designer’s
resistance to change, the result is often a product that is not particularly
successful in supporting human safety, performance, and
satisfaction. Effectively integrating human factors considerations
depends on understanding the system design process.

Y Considering human factors
at the start of the design
smooths the design process.

2.1.1 System Design Processes
Systematic design processes specify a sequence of steps for product
analysis, design, and production. Even though there are many
different design processes, they generally include stages that reflect
understanding the users needs (pre-design or front-end analysis activities),
creating a product or system (prototypes, pre-production
models), evaluating how well the design meets user’s needs; all of
which is an iterative process that cycles back to understanding the
user’s needs. Product lifecycle models, are design processes that include
product implementation, utilization andmaintenance, and
dismantling or disposal. Design processes differ to the degree that
they are defined by sequential steps or by iteration, flexibility, and
adaption to uncertainty.

Vee process

Figure 2.1 shows three common design processes,
the first is the Vee process, which is often used in the design of
large, high-risk systems, such as the design of a new aircraft, where
sequential development is possible and verification, validation,
and documentation are critical. The Vee shape starts with a broad
system description and design requirements, which are decomposed
into detailed requirements. For the dashboard of a car, these
detailed requirementsmight include information elements, such
as speed and level of the gas tank. Design of these components
are then integrated and verified by comparing them to the original
system requirements. In the Vee process, the general specifications
are well-defined at the start and emphasis is given to documenting
a successful implementation of those specifications.

Plan-Do-Check-Act cycle.

A second design model is the Plan-
Do-Check-Act cycle (PDCA), which is commonly used to enhance
workplace efficiency and production quality [30]. The cycle begins
with the target improvement. The Plan stage describes objectives
and specifies the targeted improvement. The Plan is then implemented
in the Do stage where a product, prototype or process
is created. The Check stage involves assessing the intervention
defined by the Do stage to understand what effect it had. Act completes
the cycle by implementing the intervention or developing a
new Plan based on the outcomes. This cycle reflects the scientific
management approach of Taylor in that each plan represents a
hypothesis of how the system or product might be improved.

Scrum process

A third design model is the Scrum approach,
which is more typical of consumer software products, such as
smartphone and web applications, where an iterative and incremental
approach is needed to resolve uncertainty in design requirements.
The Scrum approach focuses on creating products
and using those products to discover requirements [31]. Early prototypes
reveal design opportunities that are visible only after the
technology has been implemented. Central to the Scrumapproach
is delivering systemcomponents quickly and accommodating requirements
discovered during development. “Sprints,” which are
short duration efforts, typically 24 hours to 30 days, focus effort

Figure 2.1

Three system design processes that correspond roughly to
design of high-risk systems, the work-place, and consumer products.

on quickly producing new iterations of the product. The Scrum
approach is well-suited to situations that demand high degree
of innovation, such as those where technology changes rapidly
and potential applications emerge abruptly. This flexibility is why

such techniques are sometimes termed agile design The Scrum
approach relies on close interaction between co-located workers
to develop solutions in an ad-hoc manner and therefore, the approach
tends to place less emphasis on standardized work processes,
documentation, and testing.

As noted in the introduction, cars are increasingly becoming
highly computerized consumer products. Consequently, one might
think a Scrumapproachmight be appropriate for designing a car
given the rapidly changing technology and the associated need
for innovation to stay ahead of competitors. Rapid technology
change makes it difficult to specify detailed requirements in advance.
Cars also have elements of high-risk systems that intensify
the demands to verify and validate critical safety features,making
the “Vee” model more appropriate. Such design situations demonstrate
the need for a hybrid approach that combines elements of
the Vee, Plan-Do-Check-Act, and Scrum.

Vee process focuses on methodical
implementation,
PDCA guides incremental
improvement, and Scrum
focuses on fast iteration.

Integrating Human Factors into design processes.

Effectively
integrating human factors considerations depends on matching
the methods to the demands and opportunities of the particular
design process. For example, with a short development timeline
there may be no opportunities for time consuming human factors
methods. Some of themethods described in this chapter, such as a
comprehensive task analysis, provide an accurate description, but
require weeks to months to complete. Such comprehensive methods
best fit the Vee model. Other methods that provide a less accurate
description, such as an informal observations or an Internetbased
survey, might be completed in days. These rapidmethods
best fit the Scrum model. Human factors methods trade accuracy
for speed. Understanding how to make this speed-accuracy tradeoff
is critical for inserting human factors considerations into design.

Y Select human factors methods
that fit the demands of
the design process.

At the end of this chapter you will be able to...

1. identify appropriate design process for high-risk systems, the work place, and consumer products

2. apply human-centered design using the understand, create, and evaluate iterative cycle

3. identify the role of human factors in system design processes

4. identify design opportunities using focus groups, observations, and accident investigation

5. define design requirements using task analysis 6. create prototypes using iterative design and refinement

Thomas Edison was a great inventor but a poor businessman. Consider the phonograph. Edison invented it, he had better technology than his competitors, but he built a technology-centered device that failed to consider his customers’ needs, and his phonograph business failed. One of Edison’s failings was to neglect the practical advantages of the disc over the cylinder in terms of ease of use, storage, and shipping. Edison scoffed at the scratchy sound of the disc compared to the superior sound of his cylinders. Edison thought phonographs could lead to a paperless office in which dictated letters could be recorded and the cylinders mailed without the need for transcription. The real use of the phonograph, discovered by a variety of other manufacturers, was for prerecorded music. Once again, he failed to understand the real desires of his customers. Edison decided that big-name, expensive artists did not sound that different from the lesser-known professionals. He is probably correct. Edison thought he could save considerable money at no sacrifice to quality by recording those lesser-known artists. He was right; he saved a lot of money. The problem was, the public wanted to hear the well-known artists, not the unknown ones. Edison bet on a technology-centered analysis and lost. The moral of this story is to know your customer. Being first, being best, and even being right do not matter; what matters is understanding what your customers want and need. Many technology-oriented companies are in a similar muddle. They develop technology-driven products without understanding their customers (Adapted from Norman [23]). The goal of a human factors specialist is to make systems successful by enhancing safety, performance, and satisfaction. This is achieved by applying human factors principles, methods, and data to the design of products or systems. The concept of “design” is very broad and can include activities such as: • Creating new products, systems, and experiences • Improving existing products to address human factors problems • Ensuring safety in the workplace, car, and home • Implementing safety-related activities, such as hazard analyses, industrial safety programs, and safety-related training • Developing performance support materials, such as checklists and instruction manuals • Developing methods to train and assess groups and teams • Guiding team and organizational design In this chapter, we review some of the methods that human factors specialists use to support design, with particular emphasis on the early stages of design. Human factors methods and principles are applied in all product design phases: front-end analysis, prototyping, technical design, and final test and evaluation.

Although interface design may be the most visible design element, human factors specialists go beyond interface to design the tasks, interaction, overall experience, and even the organization of people and technology. Cooper [28] argues that focusing solely on interface design is ineffective and calls it “painting the corpse.” Making a pretty, 3-D graphical interface cannot save a system that does not consider the job or organization it supports. Reflecting this need to go beyond user interface (UI), is the increasing prominence of user experience (UX) design, which extends beyond the interface to include all aspects of users’ interaction with a system [29]. This chapter provides an overview of the process needed to address these broad considerations, and later chapters provide the basic content necessary to carry out those processes. Later chapters also provide specialized processes needed to address considerations beyond user experience design, such as organizational design.

 7.8 Metacognition

Throughout this chapter we have cited the importance of metacognition: thinking about ones’ own thinking and cognitive processes. Metacognition influences the decision-making process by guiding how people adapt to the particular decision situation. Here we highlight five of the most critical elements of metacognition for macrocognition.

1. Knowing what you don’t know. That is, being aware that your decision processes or those necessary to maintain adequate situation awareness are inadequate because of important cues that are missing, and, if obtained, could substantially improve situation awareness and assessment.

2. The decision to “purchase” further information. This can be seen as a decision within the decision. Purchasing may involve a financial cost, such as the cost of an additional medical test required to reduce uncertainty on a diagnosis. It also may involve a time cost, such as the added time required before declaring a hurricane evacuation, to obtain more reliable information regarding the forecast hurricane track. In these cases, metacognition is revealed in the ability to balance the costs of purchase against the value of the added information [476]. The metacognitive skills here also clearly involve keeping track of the passage of time in dynamic environments, to know when a decision may need to be executed even without full information.

3. Calibrating confidence in what you know. As we have described above, the phenomenon of overconfidence is frequently manifest in human cognition [351], and when one is overconfident in ones’ knowledge, there will be both a failure to seek additional information to reduce uncertainty, and also a failure to plan for contingencies if the decision maker is wrong in his/her situation assessment. 4. Choosing the decision strategy adaptively. As we have seen above, there are a variety of different decision strategies that can be chosen; using heuristics, holistic processing, System 1, recognition primed decisions, or deploying the more elaborate effort-demanding algorithms, analytic decision strategies using System 2. The expert has many of these in her toolkit, but metacognitive skills are necessary to decide which to employ when, as Amy did in our earlier example, by deciding to switch from an RPD pattern match, to a more time analytical strategy when the former failed. 5. Processing feedback to improve the toolkit. Element 4 relates to a single instance of a decision—in Amy’s case, the diagnosis and choice of treatment for one patient. However metacognition can and should also be employed to process the outcome of a series of decisions, realize from their negative outcomes that they may be wanting, and learning to change the rules by which different strategies are deployed, just as the student, performing poorly in a series of tests, may decide to alter his/her study habits. To deploy such metacognitive skills here obviously requires some effort to obtain and process the feedback of decision outcomes, something we saw was relatively challenging to do with decision making.

7.8.1 Principles for Improving Metacognition

As with other elements of macrocognition, metacognition can be improved by some combination of changing the person (through training or experience) or changing the task (through task and technology).

1. Ease information retrieval. Requiring people to manually retrieve or select information is more effortful than simply requiring them to scan to a different part of the visual field [138, 477], a characteristic that penalizes the concepts of multilevel menus and decluttering tools that require people to select the level of decluttering they want. Pop-up messages and other automation features that infer and satisfy a person’s information needs and relieve the effort of accessing information [478].

2. Highlight benefits and minimize effort of engaging decision aids. Designers must understand the effort costs generated by potentially powerful features in interfaces. Such costs may be expressed in terms of the cognitive effort required to learn the feature or the mental and physical effort and time cost required to load or program the feature. Many people are disinclined to invest such effort even if the anticipated gains in productivity are high, and so the feature will go unused.

3. Manage cognitive depletion. An extended series of demanding decisions can incline people towards an intuitive approach to decisions, even when an analytic one would be more effective. Coaching people on this tendency might help them take rest breaks, plan complicated decisions early rather than late in the day, and avoid systems that introduce unnecessary decisions. People tend to make the easy or default decision as they become fatigued. As an example, Figure 7.9 shows how cognitive depletion changes the ruling of Israeli judges making parole decisions [479]. The timeline starts at the beginning of the day and each open circle represents the first decision after a break. The pattern cannot be explained by obvious confounding factors such as the gravity of the offense or time served. Similar effects are seen in other domains such as physicians choosing to prescribe more antibiotics as they become cognitively depleted over the day [480].

4. Training metacognition. Training can improve metacognition by teaching people to: (1) consider cues needed to develop situation awareness, (2) check situation assessments or explanations for completeness and consistency with cues, (3) analyze data that conflict with the situation assessment, and (4) recognize when too much conflict exists between the assessment and the cues. Training metacognition also needs to consider when it is appropriate to rely on the automation and when it is not [435].

Figure 7.9 Effect of cognitive depletion on rulings in favor of prisoners (Adapted from Proceedings of National Academy of Sciences, Dantziger, Levav, and Pesso (2011), Extraneous factors in judicial decisions. PNAS, 108, 17, Figure 1, p. 6890. [479].)

7.9 Summary

We discussed decision making and the factors that make it more and less effective. Normative mathematical models of utility theory describe how people should compare alternatives and make the “best” decision. However, limited cognitive resources, time pressure, and unpredictable changes often make this approach unworkable, and people use simplifying heuristics, which make decisions easier but also lead to systematic biases. In many situations people often have years of experience that enables them to refine their decision heuristics and avoid many biases. Decision makers also adapt their decision making by moving from skill- and rule-based decisions to knowledge-based decisions according to the degree of risk, time pressure, and experience. This adaptive process must be considered when improving decision making through task redesign, choice architecture, decision-support systems, or training.

Techniques to shape decision making discussed in this chapter offer surprisingly powerful ways to affect decisions and so the ethical dimensions of these choices should be carefully considered. As an example, should the default setting be designed to provide people with the option that aligns with their preference, what is best for them, what is likely to maximize profits, or what might be best for society [18]? The concepts in this chapter have important implications for safety and human error, discussed in Chapter 16. In many ways the decision-support systems described in this chapter can be considered as displays or automation—Chapter 11 addresses automation, and we turn to displays in the next chapter.

The cognitive processes of planning and scheduling are closely related to those discussed in the previous section, because informed problem solving and troubleshooting often involve careful planning of future tests and activities. However, troubleshooting and diagnosis generally suggest that something is “wrong” and needs to be fixed. Planning and scheduling do not have this implication. That is, planning may be invoked in the absence of problem solving, as when a routine schedule of activities is generated. Planning often accompanies decision making to implement the course of action decided upon. In many dynamic systems, the future may be broken down into two separate components: the predicted state of the system that is being controlled and the ideal or command state that should be obtained. Thus, a factory manager may have predicted output that can be obtained over the next few hours (given workers and equipment available) and a target output that is requested by external demands (i.e., the factory’s client). When systems cannot change their state or productive output easily, we say they are sluggish, or have “high inertia.” In these circumstances of sluggish systems, longer range planning becomes extremely important to guarantee that future production matches future demands. This is because sudden changes in demand cannot be met by rapid changes in system output. Examples of such sluggish systems—in need of planning—are the factory whose equipment takes time to be brought online, the airspace in which aircraft cannot be instantly moved to new locations, or any physical system with high inertia, like a supertanker or a train. In time-critical operations effective planning depends vitally upon anticipating events in the world that might derail the plan implementation. Unfortunately people are not very good at envisioning such events [351], nor the time required to address them. Hence the planning bias, discussed earlier in the chapter, is prevalent. You will recognize the importance to planning of two concepts discussed earlier in this chapter. First, level 3 situation awareness is another way of expressing an accurate estimate of future state and future demands. Second, skilled operators often employ a mental model of the dynamic system to be run through a mental simulation in order to infer the future state from the current state [375]. Mental simulation imposes heavy demands on cognitive resources. If these resources have been depleted or are diverted to other tasks, then prediction and planning may be poor, or not done at all, leaving the operator unprepared for the future. 7.7.1 Principles for Improving Planning and Scheduling Human limits in the area of planning and scheduling are often addressed with automation. Operations research offers many approaches to design the best plan given certain assumptions. Unfortunately, reality often violates these assumptions and people must intervene. 1. Create contingency plans and plan to re-plan. In general, people tend to avoid complex planning schedules over long time horizons [468], a decision driven both by a desire to conserve the resources imposed by high working memory load and by the fact that in an uncertain world accurate planning is impossible, and plans may need to be revised or abandoned altogether as the world evolves in a way that is different from what was predicted. Re-planning is essential. Here, unfortunately, people sometimes fail to do so, creating what is known as a plan continuation error [469, 470], a form of behavior that has much in common with cognitive tunneling, the confirmation bias and the sunk cost bias. Contingency plans and planning to re-plan can avoid these tendencies. 2. Create predictive displays. As with problem solving and troubleshooting, a variety of automation tools are proposed to reduce these cognitive demands in planning [471]. Most effective are predictive displays that offer visual representations of the likely future, reducing the need for working memory [472]. We discuss these in the next chapter. Also potentially useful are computer-based planning aids that can either recommend plans [473] or allow fast-time simulation of the consequence of such plans to allow the operator to try them out and choose the successful one [474]. Air traffic controllers can benefit from such a planning aid known as the User Request Evaluation Tool (URET) to try out different routes to avoid aircraft conflicts [475].