Mark W.
Scerbo
Department
of Psychology
Abstract
Adaptive automation refers to
systems in which both the user and the system can initiate changes in the level
of automation. The first adaptive automation systems were implemented in
associate systems based on models of operator behavior and workload. Recently,
however, systems have been developed that follow the neuroergonomics approach
and use psychophysiological measures to trigger changes in the state of
automation. Studies have shown that this approach can facilitate operator
performance. Further, evidence is beginning to show that people not only think
of adaptive systems as coworkers, they may even expect them to behave like humans. Consequently, adaptive
automation creates new challenges for both users and designers that go beyond
traditional ideas of human-computer interaction and system design.
We humans
have always been adept at dovetailing our minds and skills to the shape of our
current tools and aids. But when those
tools and aids start dovetailing back – when our technologies actively,
automatically, and continually tailor themselves to us just as we do to them –
then the line between tool and human becomes flimsy indeed.
-- Andy Clark, Natural-Born Cyborgs: Minds, Technologies and the Future of Human
Intelligence (p. 7)
Introduction
Neuroergonomics
has been described as the study of brain and behavior at work (Parasuraman,
2003). This emerging area focuses on current research and developments in the
neuroscience of information processing and how that knowledge can be used to
improve performance in real-world environments.
Parasuraman argues that an understanding of how the brain processes
perceptual and cognitive information can lead to better designs for equipment,
systems, and tasks by enabling a tighter match between task demands and the
underlying brain processes. Ultimately, research in neuroergonomics can lead to
safer and more efficient working conditions.
Ironically,
interest in neuroergonomics evolved from research surrounding how operators
interact with a form of technology designed to make work and our lives easier –
automation. In general, automation can
be thought of as a machine agent capable of carrying out functions normally
performed by a human (Parasuraman & Riley, 1997). For example, the
automatic transmission in an automobile allocates the tasks of depressing the
clutch, shifting gears, and releasing the clutch to the vehicle. Automated
machines and systems are intended and designed to reduce task demands and
workload. Further, they allow individuals to increase their span of operation
or control, perform functions that are beyond their normal abilities, maintain
performance for longer periods of time, and perform fewer mundane activities.
Automation can also help reduce human error and increase safety. The irony
behind automation arises from a growing body of research demonstrating that
automated systems often increase
workload and create unsafe working
conditions.
In his book, Taming HAL: Designing interfaces beyond 2001, Degani (2004) relates
the story of an airline captain and crew performing the last test flight with a
new aircraft. This was to be the second such test that day and the captain,
feeling rather tired, requested that the copilot fly the aircraft. The test
plan required a rapid take-off, followed by engaging the autopilot, simulating
an engine failure by reducing power to the left engine, and then turning off
the left hydraulic system. The test flight started out just fine. Four seconds
into the flight, however, the aircraft was pitched about 4 degrees higher than
normal, but the captain continued with the test plan and attempted to engage
the autopilot. Unfortunately, the autopilot did not engage. After a few more
presses of the autopilot button, the control panel display indicated that the
system had engaged (although in reality, the autopilot had not assumed
control). The aircraft was still pitched too high and was beginning to lose
speed. The captain apparently did not notice these conditions and continued
with the next steps requiring power reduction to the left engine and shutting
down the hydraulic system.
The aircraft
was now flying on one engine with increasing attitude and decreasing speed.
Moreover, the attitude was so steep that the system intentionally withdrew
autopilot mode information from its display.
Suddenly, the autopilot engaged and assumed the attitude capture mode to
take the aircraft to the preprogrammed setting of 2,000 ft., but this
information was not presented on the autopilot display. The autopilot initially began lowering the
nose, but then reversed course. The attitude began to pitch up again and
airspeed continued to fall. When the captain finally turned his attention from
the hydraulic system back to the instrument panel, the aircraft was less than
1,500 ft above ground, pitched up 30 degrees, with airspeed dropping to about
100 knots. The captain then had to compete with the envelope protection system
for control of the aircraft. He
attempted to bring the nose down and then realized he had to reduce power to the right engine in
order to undo a worsening corkscrew effect produced by the simulated left
engine failure initiated earlier. Although he was able to bring the attitude
back down to zero, the loss of airspeed coupled with the simulated left engine
failure had the aircraft in a 90-degree roll.
The airspeed soon picked up and the captain managed to raise the left
wing, but by this time the aircraft was only 600 ft. above ground. Four seconds later the aircraft crashed into
the ground killing all on board.
Degani
(2004) discusses several factors that contributed to this crash. First, no one
knows why the autopilot’s attitude was preprogrammed for 2,000 ft, but it is
possible that the pilot never entered the correct value of 10,000 ft. Second,
although the pilot tried several times to engage the autopilot, he did not
realize that the system’s logic would override his requests because his
copilot’s attempts to bring the nose down were canceling his requests. Third,
there was a previously undetected flaw in the autopilot’s logic. The autopilot
calculated the rate of climb needed to reach 2,000 ft when both engines
were powered up, but did not recalculate the rate after the left engine had
been powered down. Thus, the autopilot continued to demand the power it needed
to reach the preprogrammed altitude despite that the aircraft was losing speed.
Last, no one knows why the pilot did not disengage the autopilot when the
aircraft continued to increase its attitude. Degani suggests that pilots who
have substantial experience with autopilot systems may place too much trust in
them. Thus, it is possible that assumptions regarding the reliability of the
autopilot coupled with the absence of mode information on the display left the
captain without any information or reason to question the status of the
autopilot.
This
incident clearly highlights the complexity and problems that can be introduced
by automation. Unfortunately, it is not a unique occurrence. Degani (2004)
describes similar accounts of difficulties encountered with other automated
systems including cruise control in automobiles and blood pressure devices.
Research on human interaction with
automation has shown that it does not always make the job easier. Instead, it changes the nature of work. More
specifically, automation changes the way activities are distributed or carried
out and can therefore introduce new and different types of problems (Woods,
1996). Automation can also lead to
different types of errors because operator goals may be incongruent with the
goals of systems and subsystems (Sarter & Woods, 1995; Wiener, 1989). Woods
(1996) argues further that in systems where subcomponents are tightly coupled,
problems may propagate more quickly and be more difficult to isolate. In
addition, highly automated systems leave fewer activities for individuals to
perform. Consequently, the operator becomes a more passive monitor instead of
an active participant. Parasuraman, Mouloua, Molloy, and Hilburn (1996) have
shown that this shift from performing tasks to monitoring automated systems can
actually inhibit one’s ability to detect critical signals or warning
conditions. Further, an operator’s manual skills can begin to deteriorate in
the presence of long periods of automation (Wickens, 1992).
Adaptive Automation
Given the
problems associated with automation noted above, researchers and developers
have begun to turn their attention to alternative methods for implementing
automated systems. Adaptive automation is one such method that has been
proposed to address some of the shortcomings of traditional automation. In
adaptive automation, the level of automation or the number of systems operating
under automation can be modified in real time. In addition, changes in the
state of automation can be initiated by either the human or the system
(Hancock & Chignell, 1987; Rouse, 1976; Scerbo, 1996). Consequently,
adaptive automation enables the level or modes of automation to be tied more
closely to operator needs at any given moment (Parasuraman et al., 1992).
Adaptive
automation systems can be described as either adaptable or adaptive. Scerbo (2001) has described a
taxonomy of adaptive technology. One dimension of this taxonomy concerns the
underlying source of flexibility in the system, i.e., whether the information
displayed or the functions themselves are flexible. A second dimension
addresses how the changes are invoked. In adaptable systems, changes among
presentation modes or in the allocation of functions are initiated by the user.
By contrast, in adaptive systems both the user and the system can initiate
changes in the state of the system.
The
distinction between adaptable and adaptive technology can also be described
with respect to authority and autonomy. Sheridan and Verplank (1978) have
described several levels of automation that range from completely manual, to
semiautomatic, to fully automatic. As
the level of automation increases, systems take on more authority and autonomy.
At the lower levels of automation, systems may offer suggestions to the user.
The user can either veto or accept the suggestions and then implement the
action. At moderate levels, the system may have the autonomy to carry out the
suggested actions once accepted by the user. At higher levels, the system may
decide on a course of action, implement the decision, and merely inform the
user. With respect to Scerbo’s (2001) taxonomy, adaptable systems are those in
which the operator maintains authority over invoking changes in the state of
the automation (i.e., they reflect a superordinate-subordinate relationship
between the operator and the system). In adaptive systems, on the other hand,
authority over invocation is shared. Both the operator and the system can
initiate changes in state of the automation.
There
has been some debate over who should have control over changes among modes of
operation. Some argue that operators
should always have authority over the system because they are ultimately
responsible for the behavior of the system. In addition, it is possible
that operators may be more efficient at managing resources when they can
control changes in the state of automation (Billings & Woods, 1994; Malin
& Schreckenghost, 1992). Many of these arguments are based on work with
life critical systems in which safe operation is of utmost concern. However, it is not clear that strict operator
authority over changes among automation modes is always warranted. There may be
times when the operator is not the best judge of when automation is needed. For
example, changes in automation may be needed at the precise moment the operator
is too busy to make those changes (Weiner, 1989). Further, Inagaki, Takae and
Moray (1999) have shown mathematically that the best piloting decisions
concerning whether to abort a take-off are not those where either the human or
the avionics maintain full control. Instead, the best decisions are made when
the pilot and the automation share control.
Scerbo (1996) has argued that in some
hazardous situations where the operator is vulnerable, it would be extremely
important for the system to have authority to invoke automation. If lives
are at stake or the system is in jeopardy, allowing the system to intervene and
circumvent the threat or minimize the potential damage would be paramount. For
example, it is not uncommon for many of today's fighter pilots to sustain G
forces high enough to render them unconscious for periods of up to 12 seconds.
Conditions such as these make a strong case for system-initiated invocation of
automation. An example of one such adaptive automation system is the Ground
Collision-Avoidance System (GCAS) developed
and tested on the F-16D (Scott, 1999). The system assesses both internal and
external sources of information and calculates the time it will take until the
aircraft breaks through a pilot determined minimum altitude. The system issues
a warning to the pilot. If no action is taken, an audio “fly up” warning is
then presented and the system takes control of the aircraft. When the system has maneuvered the aircraft out of the way of the
terrain, it returns control of the aircraft to the pilot with the message, “You
got it”. The intervention is designed to right the aircraft quicker than any
human pilot can respond. Indeed, test pilots who were given the authority to
override GCAS eventually conceded control to the adaptive system.
Adaptive Strategies
There
are several strategies by which adaptive automation can be implemented
(Morrison & Gluckman, 1994; Rouse & Rouse, 1983). One set of strategies
addresses system functionality. For instance, entire tasks can be allocated to
either the system or the operator, or a specific task can be partitioned so
that the system and operator each share responsibility for unique portions of
the task. Alternatively, a task could be
transformed to a different format to make it easier (or more challenging) for
the operator to perform.
A
second set of strategies concerns the triggering mechanism for shifting among
modes or levels of automation (Parasuraman et al., 1992; Scerbo, Freeman, &
Mikulka, 2003). One approach relies on goal-based strategies. Specifically,
changes among modes or levels of automation are triggered by a set of criteria
or external events. Thus, the system
might invoke the automatic mode only during specific tasks or when if it
detects an emergency situation. Another approach would be to use real-time
measures of operator performance to invoke the changes in automation. A third
approach uses models of operator performance or workload to drive the
adaptive logic (Hancock & Chignell, 1987; Rouse, Geddes & Curry, 1987,
1988). For example, a system could estimate current and future states of an
operator’s activities, intentions, resources, and performance. Information about the operator, the system,
and the outside world could then be interpreted with respect to the operator’s goals
and current actions to determine the need for adaptive aiding. Finally,
psychophysiological measures that reflect operator workload can also be
used to trigger changes among modes.
Examples of Adaptive Automation
Systems
Adaptive
automation has its beginnings in artificial intelligence. In the 1970s, efforts were directed toward
developing adaptive aids to help allocate tasks between humans and computers.
By the 1980s, researchers began developing adaptive interfaces. For instance,
Wilensky, Arens, and Chin (1984) developed the UNIX Consultant (UC) to provide
general information about UNIX, procedural information about executing UNIX
commands, as well as debugging information. The UC could analyze user queries,
deduce the user goals, monitor the user’s interaction history, and present the
system’s response.
Associate systems. Adaptive aiding concepts were applied in a
more comprehensive manner in the Defense Advanced Research Projects Agency
(DARPA) Pilot’s Associate program (Hammer & Small, 1995). The goal of the
program was to use intelligent systems to provide pilots with the appropriate
information, in the proper format, at the right time. The Pilot’s Associate
could monitor and assess the status of its own systems as well as events in the
external environment. The information could then be evaluated and presented to
the pilot. The Pilot’s Associate could also suggest actions for the pilot to
take. Thus, the system was designed to function as an assistant for the pilot.
In
the 1990s, the U.S. Army attempted to take this associate concept further in
its Rotorcraft Pilot’s Associate (RPA) program. The goal was to create an
associate that could serve as a “junior crew member” (Miller
& Hannen, 1999). A major component of the RPA is the Cognitive Decision Aiding System
(CDAS) which is responsible for detecting and organizing incoming data,
assessing the internal information regarding the status of the aircraft,
assessing external information about target and mission status, and feeding
this information into a series of planning and decision-making modules. The Cockpit
Information Manager (CIM) is the adaptive automation system for the CDAS. The
CIM is designed to make inferences about current and impending activities for
the crew, allocate tasks among crew members as well as the aircraft, and
reconfigure cockpit displays to support the ability of the “crew-automation
team” to execute those activities (see Figure 1). The CIM monitors crew
activities and external events and matches them against a database of tasks to
generate inferences about crew intentions. The CIM uses this information to
make decisions about allocating tasks, prioritizing information to be presented
on limited display spaces, locating pop-up windows, adding or removing
appropriate symbology from displays, and adjusting the amount of detail to be
presented in displays. Perhaps most important, the CIM includes a separate
display that allows crew members and the system to coordinate the task
allocation process and communicate their intentions (located above the center
display in Figure 1). The need for communication among members is important for
highly functioning human teams and, as it turned out, was essential for user
acceptance of the RPA. Evaluations from a
sample of pilots indicated that the RPA often provided the right information at
the right time. Miller and Hannen reported that in the initial tests, no pilot
chose to turn off the RPA.
The
RPA was an ambitious attempt to create an adaptive automation system that would
function as a team member. There are several characteristics of this effort
that are particularly noteworthy. First, a great deal of the intelligence
inherent in the system was designed to anticipate user needs and be proactive
about reconfiguring displays and allocating tasks. Second, both the users and
the system could communicate their plans and intentions, thereby reducing the
need to decipher what the system was doing and why it was doing it. Third, unlike many other adaptive automation
systems, the RPA was designed to support the simultaneous activities of multiple
users.
Although the RPA is a significant demonstration of adaptive automation, it was not designed from the neuroergonomics perspective. It is true that the a good deal of knowledge about cognitive processing related to decision-making, information representation, task scheduling and task sharing, was needed to create the RPA, but the system was not built around knowledge of brain functioning.
Brain-based systems. An example of adaptive automation that follows
the neuroergonomics approach can be found in systems that use
psychophysiological indices to trigger changes in the automation. There are
many psychophysiological indices that reflect underlying cognitive activity,
arousal levels, and external task demands. Some of these include cardiovascular
measures (e.g., heart rate, heart rate variability), respiration, galvanic skin
response (GSR), ocular motor activity, speech, as well as those that reflect
cortical activity such the electroencephalogram (EEG), event-related potentials
(ERPs) derived from EEG signals to stimulus presentations, functional magnetic
resonance imaging (fMRI), and near infrared spectrometry (NIRS) that measures
changes in oxygenated and deoxygenated hemoglobin (see Byrne & Parasuraman
1996 for a review). One of the most important
advantages to brain-based systems for adaptive automation is that they
provide a continuous measure of activity in the presence or absence of overt
behavioral responses (Byrne & Parasuraman 1996; Scerbo et al. 2001).
The first
brain-based adaptive system was developed by Pope, Bogart and Bartolome
(1995). Their system uses an index of
task engagement based upon ratios of EEG power bands (alpha, beta, theta,
etc.). The EEG signals are recorded from several locations on the scalp and are
sent to a LabView Virtual Instrument that determines the power in each band for
all recording sites and then calculates the engagement index used to change a
tracking task between automatic and manual modes. The system recalculates the
engagement index every two seconds and changes the task mode if necessary. Pope
and his colleagues studied several different engagement indices under both
negative and positive feedback contingencies. They argued that under negative
feedback the system should switch modes more frequently in order to maintain a
stable level of engagement. By contrast, under positive feedback the system
should be driven to extreme levels and remain there longer (i.e., fewer
switches between modes). Moreover, differences in the frequency of task mode
switches obtained under positive and negative feedback conditions should
provide information about the sensitivity of various engagement indices. Pope
et al. found that the engagement index based on the ratio of beta/(alpha
+theta) proved to be the most sensitive to differences between positive and
negative feedback.
The study
by Pope et al. (1995) showed that their system could be used to evaluate
candidate engagement indices. Freeman, Milkulka, Prinzel and Scerbo (1999)
expanded upon this work and studied the operation of the system in an adaptive
context. They asked individuals to perform the compensatory
tracking, resource management, and system monitoring tasks from the Multi-Task
Attribute Battery (MAT; Comstock & Arnegard, 1991). Figure 1 shows a
participant performing the MAT task while EEG signals are being recorded. In
their study, all tasks remained in automatic mode except the tracking task
which shifted between automatic and manual modes. They also examined
performance under both negative and positive feedback conditions. Under
negative feedback, the tracking task was switched to or maintained in automatic
mode when the index increased above a pre-established baseline reflecting high
engagement. By contrast, the tracking task was switched to or maintained in manual
mode when the index decreased below the baseline reflecting low engagement. The
opposite schedule of task changes occurred under the positive feedback
conditions. Freeman and his colleagues argued that if the system could moderate
workload, better tracking performance should be observed under negative as
compared to positive feedback conditions. Their results confirmed this
prediction. In subsequent studies, similar findings were found when individuals
performed the task over much longer intervals and under conditions of high and
low task load (see Scerbo et al., 2003).
More
recently,
Taken
together, the findings from these studies suggest that it is indeed possible to
obtain indices of one’s brain activity and use that information to drive an
adaptive automation system to improve performance and moderate workload. There
are, however, still many critical conceptual and technical issues (e.g., making
the recording equipment less obtrusive and obtaining reliable signals in noisy
environments) that must be overcome before systems such as these can move from
the laboratory to the field (Scerbo et al., 2001).
Further,
many issues still remain surrounding the sensitivity and diagnosticity of
psychophysiological measures, in general. There is a fundamental assumption
that psychophysiological measures provide a reliable and valid index of underlying
constructs such as arousal or attention. In addition, variations in task
parameters that affect those constructs must also be reflected in the measures
(Scerbo et al. 2001). In fact, Veltman and Jansen (2004) have recently argued that
there is no direct relation between information load and physiological measures
or state estimators because an increase in task difficulty does not necessarily
result in a physiological response. According to their model, perceptions of
actual performance are compared to performance requirements. If attempts to
eliminate the difference between perceived and required levels of performance
are unsuccessful, one may need to increase mental effort or change the task
goals. Both actions have consequences.
Investing more effort can be fatiguing and result in poorer performance.
Likewise, changing task goals (e.g., slowing down, skipping low priority tasks,
etc.) can also result in poorer performance. They suggest that in laboratory
experiments, it is not unusual for individuals to compensate for increases in
demand by changing task goals because there are no serious consequences to this
strategy. However, in operational environments, where the consequences are real
and operators are highly motivated, changing task goals may not be an option.
Thus, they are much more likely to invest to effort needed to meet the required
levels of performance. Consequently, Veltman and Jansen contend that
physiological measures can only be valid and reliable in an adaptive automation
environment if they are sensitive to information about task difficulty,
operator output, the environmental context, and stressors.
Another criticism of current
brain-based adaptive automation systems is that they are primarily reactive.
Changes in external events or brain activity must be recorded and analyzed
before any instructions can be sent to modify the automation. All of this takes
time and even with short delays, the system must still wait for a change in
events to react. Recently, however, Forsythe (in press) has described a
brain-based system that also incorporates a cognitive model of the operator. The
system is being developed by DaimlerChrysler through the DARPA Augmented
Cognition program to support driver behavior. Information is recorded from the
automobile (e.g., steering wheel angle, lateral acceleration) as well as the
operator (e.g., head turning, postural adjustments, and vocalizations) and
combined with EEG signals to generate inferences about workload levels corresponding
to different driving situations. In this regard, the system is a hybrid
of brain-based and operator modeling approaches to adaptive automation and can
be more proactive than current adaptive systems that rely solely on psychophysiological
measures.
Workload.
One of the arguments for developing adaptive automation is that this approach
can moderate operator workload. Most of
the research to date has assessed workload through primary task performance or
physiological indices (see above). Kaber and Riley (1999), however, conducted
an experiment using both primary and secondary task measures. They had their
participants perform a simulated radar task where the object was to eliminate
targets before they reached the center of the display or collided with one
another. During manual control, the
participants were required to assess the situation on the display, make
decisions about which targets to eliminate, and implement those decisions.
During a shared condition, the participant and the computer could each perform
the situation assessment task. The computer scheduled and implemented the
actions, but the operator had the ability to override the computer’s plans. The
participants were also asked to perform a secondary task requiring them to
monitor the movements of a pointer and correct any deviations outside of an
ideal range. Performance on the
secondary task was used to invoke the automation on the primary task. For half
of the participants, the computer suggested changes between automatic or manual
operation of the primary task and for the remaining participants, those changes
were mandated.
Kaber and
Riley (1999) found that shared control resulted in better performance than
manual control on the primary task. However, the results showed that mandating
the use of automation also bolstered performance during periods of manual
operation. Regarding the secondary task, when use of automation was mandated,
workload was lower during periods of automation; however, under periods of
manual control, workload levels actually increased and were similar to those
seen when its use was suggested. These
results show that authority over invoking changes between modes had
differential effects on workload during periods of manual and automated operation.
Specifically, Kaber and Riley (1999) found that the requirement to “consider”
computer suggestions to invoke automation led to higher levels of workload
during periods of shared/automated control than when those decisions were
dictated by the computer.
Situation
awareness. Thus far, there have been few attempts to study the
effects of adaptive automation on situation awareness (SA). Endsley (1995)
describes SA as the ability to perceive elements in the environment, understand
their meaning, and to make projections about their status in the near
future. One might assume that efforts to
moderate workload through adaptive automation would lead to enhanced SA;
however, that relationship has yet to be demonstrated empirically. In fact,
within an adaptive paradigm periods of high automation could lead to poor SA
and make returning to manual operations more difficult. The findings of Kaber
and Riley (1999) regarding secondary task performance described above support
this notion.
Recently, Bailey and his colleagues
(2003) examined the effects of a brain-based adaptive automation system
on SA. The participants were given a self-assessment measure of complacency
toward automation (i.e., the propensity to become reliant on automation; see
Singh, Molloy, & Parasuraman, 1993) and separated into groups who scored
either high or low on the measure. The participants performed a modified
version of the MAT battery that included a number of
digital and analog displays (e.g., vertical speed indicator, GPS heading, oil
pressure, and auto pilot on/off) used to assess SA. Participants were asked to
perform the compensatory tracking task during manual mode and to monitor that
display during automatic mode. Half of
the participants in each complacency potential group were assigned to either an
adaptive or yoke control condition. In the adaptive condition, Bailey et al.
used the system modified by Freeman et al. (1999) to derive an EEG-based
engagement index to control the task mode switches. In
the other condition, each participant was yoked to one of the individuals in
the adaptive condition and received the same pattern of task mode switches;
however, their own EEG had no effect on system operation. All participants
performed three 15-minute trials and at the end of each trial, the computer
monitor went blank and the experimenter asked the participants to report the
current values for a sample of five displays. Participants’ reports for each
display were then compared to the actual values to provide a measure of SA
(Endsley, 2000).
Bailey and his colleagues (2003) found that the
effects of the adaptive and yoke conditions were moderated by complacency
potential. Specifically, for individuals in the yoke control conditions, those
who were high as compared to low in complacency potential had much lower levels
of SA. On the other hand, there was no difference in SA scores for high and low
complacency individuals in the adaptive conditions. More important, the SA
scores for both high and low complacency individuals were significantly higher
than those of the low complacency participants in the yoke control condition.
The authors argued that a brain-based adaptive automation system could
ameliorate the effects of complacency by increasing available attentional
capacity and in turn, improving SA.
Recently, there has been interest in the merits of an
etiquette for human-computer interaction. Miller (2002) describes etiquette as
a set of prescribed and proscribed behaviors that permit meaning and intent to
be ascribed to actions. Etiquette serves to make social interactions more
cooperative and polite. Importantly, rules of etiquette allow one of form
expectations regarding the behaviors of others. In fact, Nass, Moon, and
Carney (1999) have shown that people adopt many of the same social
conventions used in human-human interactions when they interact with computers.
Moreover, they also expect computers to adhere to those same conventions when
computers interact with users.
Miller (2004) argues that when
humans interact with systems that incorporate intelligent agents they may expect
those agents to conform to accepted rules of etiquette. However, the norms may
be implicit and contextually dependent: what is acceptable for one application
may violate expectations in another. Thus, there may be a need to understand
the rules under which computers should behave and be more polite.
Miller (2004) also claims that users ascribe expectations
regarding human etiquette to their interactions with adaptive automation. In
their work with the RPA, Miller and Hannen (1999) observed that much of the
dialog between team members in a two-seat aircraft was focused on communicating
plans and intentions. They reasoned that any automated assistant would need to
communicate in a similar manner to be accepted as a “team” player.
Consequently, the CIM described earlier was designed to allow users and the
system to communicate in a conventionally accepted manner.
The benefits of adopting a human-computer
etiquette are described by Parasuraman and Miller (2004) in a study of
human-automation interactions. In particular, they focused on interruptions. In
their study, participants were asked to perform the tracking and fuel resource
management tasks from the MAT battery. A third task required participants to
interact with an automated system that monitored engine parameters, detected
potential failures, and offered advice on how to diagnose faults. The
automation support was implemented in two ways. Under the “patient” condition,
the automated system would withhold advice if the user was in the act of
diagnosing the engines or provide a warning, wait five seconds, and then offer
advice if it determined the user was not interacting with the engines. By
contrast, under the “impatient” condition the automated system offered its
advice without warning while the user was performing the diagnosis. Parasurman
and Miller referred to the patient and impatient automation as examples of good
and poor etiquette, respectively. In addition, they examined two levels of system
reliability. Under low and high reliability, the advice was correct 60 and 80
percent of the time, respectively.
As might be expected, performance was better under high
as opposed to low reliability. Further, Parasuraman and Miller (2004) found that
when the automated system functioned under the good etiquette condition,
operators were better able to diagnose engine faults regardless of reliability
level. In addition, overall levels of trust in the automated system were much
higher under good etiquette within the same reliability conditions. Thus,
“rude” behavior made the system seem less trustworthy irrespective of
reliability level. Several participants commented that they disliked being
interrupted. The authors argued that systems designed to conform to rules of
etiquette may enhance performance beyond what might be expected from system
reliability and may even compensate for lower levels of reliability.
Parasuraman and Miller’s (2004) findings were obtained
with a high criticality simulated system; however, the rules of etiquette (or
interruptions) may be equally important for business or home applications. In a recent study, Bubb-Lewis and Scerbo
(2002) examined the effects of different levels of communication on task
performance with a simulated adaptive interface. Specifically, participants
worked with a computer “partner” to solve problems (e.g., determining the
shortest mileage between two cites or estimating gasoline consumption for a
trip) using a commercial travel planning software package. In their study, the computer partner was actually
a confederate in another room who followed a strict set of rules regarding how
and when to intervene to help complete a task for the participant. In addition,
they studied four different modes of communication that differed in the level
of restriction ranging from context sensitive natural language to no
communication at all. The results showed
that as restrictions on communication increased, participants were less able to
complete their tasks, which in turn, caused the computer intervene more often
to complete the tasks. This increase in interventions also led the participants
to rate their interactions with the computer partner more negatively. Thus, these findings suggest that even for
less critical systems, poor etiquette makes a poor impression. Apparently, no
one likes a show-off even if it is the computer.
Living with Adaptive Automation
Adaptive
automation is also beginning to find its way into commercial and more common
technologies. Some examples include adaptive cruise control found on several
high-end automobiles and “smart homes” that control electrical and heating
systems to conform to user preferences.
Recently,
Mozer (2004) described his experiences living in an adaptive home of his own
creation. The home was designed to regulate air and water temperature and
lighting. The automation monitors the
inhabitant’s activities and makes inferences about the inhabitant’s behavior,
predicts future needs, and adjusts the temperature or lighting accordingly.
When the automation fails to meet the user’s expectations, the user can set the
controls manually.
The
heart of the adaptive home is the adaptive control of home environment (ACHE)
and functions to balance two goals: user desires and energy conservation.
Because these two goals can conflict with one another, the system uses a
reinforcement learning algorithm to establish an optimal control policy. With
respect to lighting, the ACHE controls multiple, independent light fixtures,
each with multiple levels of intensity (see Figure 3). The ACHE encompasses a
learning controller that selects light settings based on current states. The
controller receives information about an event change that is moderated by a
cost evaluator. A state estimator generates high-level information about
inhabitant patterns and integrates it with output from an occupancy model as
well as information regarding levels of natural light available to make
decisions about changes in the control settings. The state estimator also
receives input from an anticipator module that uses neural nets to predict
which zones are likely to be inhabited within the next two seconds. Thus, if the inhabitant is moving within the
home, the ACHE can anticipate the route and adjust the lights before he arrives
at his destination. Mozer (2004) recorded the energy costs and as well as costs
of discomfort (i.e., incorrect predictions and control settings) for a month
and found that both decreased and converged within about 24 days.
Mozer
(2004) had some intriguing observations about his experiences living in the
adaptive house. First, he found that he generated a mental model of the ACHE’s
model of his activities. Thus, he knew
that if he were to work late at the office, the “house” would be expecting him
home at the usual time and he often felt compelled to return home! Further, he
admitted that he made a conscious effort to be more consistent in his
activities. He developed a meta-awareness of his occupancy patterns and
recognized that as he made his behavior more regular, it facilitated the
operation of the ACHE, which in turn, helped it to save energy and maximize his
comfort. In fact, Mozer claimed, “the ACHE trains the inhabitant, just as the
inhabitant trains the ACHE” (p. 293).
Mozer
(2004) also discovered the value of communication. At one point, he noticed a
bug in the hardware and modified the system to broadcast a warning message
throughout the house to reset the system. After the hardware problem had been addressed,
however, he retained the warning message because it provided useful information
about how his time was being spent. He argued that there were other situations
where the user could benefit from being told about consequences of manual
overrides.
Conclusion
The
development of adaptive automation represents a qualitative leap in the evolution
of technology. Users of adaptive automation will be faced with systems that
differ significantly from the automated technology of today. These systems will be much more complex from
both the users’ and designers’ perspective. Scerbo (1996) argued that adaptive
automation systems will need time to learn about users and users will need time
to understand the automation. In the Mozer’s (2004) case, he and his home
needed almost a month to adjust to one another. Further, users may find that adaptive systems
are less predictable due to the variability and inconsistencies of their own behavior. Thus, users are less likely to think of these
systems as tools, machines, or even traditional computer programs. As Mozer
(2004) indicated, he soon began to think about how his adaptive home would
respond to his behavior. Others have
suggested that interacting with adaptive systems is more like interacting with a
teammate or coworker (Hammer & Small, 1995; Miller & Hannen, 1999;
Scerbo, 1994).
The
challenges facing designers of adaptive systems are significant. Current
methods in system analysis, design, and evaluation fall short of what is needed
to create systems that have the authority and autonomy to swap tasks and
information with their users. These systems require developers to be knowledgeable
about task sharing, methods for communicating goals and intentions, and even
assessment of operator states of mind.
In fact, Scerbo (1996) has argued that researchers and designers of
adaptive technology need to understand the social, organizational, and
personality issues that impact communication and teamwork among humans to
create more effective adaptive systems. In this regard, Miller’s (2004) ideas regarding
human-computer etiquette may be paramount to the development of successful
adaptive systems.
Thus far, most of the adaptive
automation systems that have been developed address life critical activities
where the key concerns surround the safety of the operator, the system itself,
and recipients of the system’s services. However, the technology has also been
applied in other contexts where the consequences of human error are less severe
(e.g., Mozer’s adaptive house). Other potential applications might include a
personal assistant, butler, tutor, secretary, or receptionist. Moreover,
adaptive automation could be particularly useful when incorporated in systems
aimed at training and skill development as well as entertainment.
To date,
most of the adaptive automation systems that have been developed were designed
to maximize the user-system performance of a single user. Thus, they are user
independent (i.e., designed to improve the performance of any operator). However, overall user-system performance is
likely to be improved further if the system is capable of learning and
adjusting to the behavioral patterns of its user as was shown by Mozer (2004).
Although building systems capable of becoming more user-specific might seem
like a logical next step, that approach would introduce a new and significant
challenge for designers of adaptive automation – addressing the unique needs of
multiple users. The ability of Mozer’s house to successfully adapt to his
routines is due in large part to his being the only inhabitant. One can imagine
the challenge faced by an adaptive system trying to accommodate the wishes of
two people who want the temperature set at different levels.
The problem
of accommodating multiple users is not unique to adaptive automation. In fact,
the challenge arises from a fundamental aspect of humanity. People are social
creatures and as such, they work in teams, groups, and organizations. Moreover,
they can be co-located or distributed around the world and networked together.
Developers of collaborative meeting and engineering software realize that one
cannot optimize the individual human-computer interface at the expense of
interfaces that support team and collaborative activities. Consequently, even systems designed to work
more efficiently based on knowledge of brain functions must ultimately take
into consideration groups of people. Thus, the next great challenge for the
neuroergonomics approach may lie with an understanding of how brain activity of
multiple operators in social situations can improve the organizational work
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Figure Captions
Figure 1.
An operator performing the MAT task while EEG signals are recorded.
Figure 2.
The Rotorcraft Pilot’s Associate cockpit in a simulated environment.
Figure 3. Michael Mozer’s adaptive
house. An interior photo of the great room is shown on the left. On the right
is a photo of the data collection room where sensor information terminates in a
telephone punch panel and is routed to a PC. A speaker control board and a
microcontroller for the lights, electric outlets, and fans are also shown here.