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Objective: The aim of this article is to present a comprehensive review of eye-tracking measures and discuss different application areas of the method of eye tracking in the field of aviation. Background: Psychophysiological measures such as eye tracking in pilots are useful for detecting fatigue or high-workload conditions, for investigating motion sickness and hypoxia, or for assessing display improvements and expertise. Method: We review the uses of eye tracking on pilots and include eye-tracking studies published in aviation journals, with both a historical and contemporary view. We include 79 papers and assign the results to the following three categories: Human performance, aircraft design, health and physiological factors affecting performance. We then summarize the different uses of eye tracking in each category and highlight metrics which turned out to be useful in each area. Our review is complementary to that of Ziv (2016). Results: On the basis of these analyses, we propose useful application areas for the measurement of eye tracking. Eye tracking has the potential to be effective in terms of preventing errors or injuries by detecting, for example, fatigue or performance decrements. Applied in an appropriate manner in simulated or real flight it can help to ensure optimal functioning of man–machine systems. Conclusion: Further aviation psychology and aerospace medicine research will benefit from measurement of eye movements.
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The International Journal of Aerospace Psychology
ISSN: 2472-1840 (Print) 2472-1832 (Online) Journal homepage:
Eye-Tracking Measures in Aviation: A Selective
Literature Review
Sylvia Peißl, Christopher D. Wickens & Rithi Baruah
To cite this article: Sylvia Peißl, Christopher D. Wickens & Rithi Baruah (2018): Eye-Tracking
Measures in Aviation: A Selective Literature Review, The International Journal of Aerospace
Psychology, DOI: 10.1080/24721840.2018.1514978
To link to this article:
© 2018 The Author(s). Published with
license by Taylor and Francis Group, LLC
Published online: 27 Sep 2018.
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Eye-Tracking Measures in Aviation: A Selective Literature Review
Sylvia Peißl
, Christopher D. Wickens
, and Rithi Baruah
Department of Psychology, Leopold Franzens University, Innsbruck, Austria;
Department of Psychology, University
of Illinois, Urbana-Champaign, and Department of Psychology, Colorado State University, Fort Collins, CO, USA;
Baruah, Department of Psychology, Christ University, Bengalore, India
Objective: The aim of this article is to present a comprehensive review of
eye-tracking measures and discuss different application areas of the
method of eye tracking in the field of aviation.
Background: Psychophysiological measures such as eye tracking in pilots are
useful for detecting fatigue or high-workload conditions, for investigating
motion sickness and hypoxia, or for assessing display improvements and
Method: We review the uses of eye tracking on pilots and include eye-
tracking studies published in aviation journals, with both a historical and
contemporary view. We include 79 papers and assign the results to the
following three categories: Human performance, aircraft design, health and
physiological factors affecting performance. We then summarize the different
uses of eye tracking in each category and highlight metrics which turned out
to be useful in each area. Our review is complementary to that of Ziv (2016).
Results: On the basis of these analyses, we propose useful application areas for
the measurement of eye tracking. Eye tracking has the potential to be effective
in terms of preventing errors or injuries by detecting, for example, fatigue or
performance decrements. Applied in an appropriate manner in simulated or real
flight it can help to ensure optimal functioning of manmachine systems.
Conclusion: Further aviation psychology and aerospace medicine research
will benefit from measurement of eye movements.
Geratewohl (1987) said the eye was the most important sensory organ of the pilot as it was said to
process 80% of all flight information. With the continuing development of technology, automation,
and sophisticated sensing in both military and commercial aviation, human information processing
changed. For instance, pilots do not process as much visual information outside the cockpit as in the
early years of aviation, but have to extract information from multiple sources (from more instru-
ments inside the cockpit) and integrate it into a coherent picture to manage the flight. Human
processing of visual data remains one of the key elements of aviation safety and effectiveness
(Mosier, 2010; Vidulich, Wickens, Tsang, & Flach, 2010).
From this perspective it seems obvious that visual information processing should be analyzed when
trying to understand flying performance decrements or factors affecting performance such as fatigue or
stress. A good method to analyze visual information processing is to examine eye movement data (e.g.,
Rayner, 1978). Ziv (2016) argued that optimal scanning behavior is important to achieve better aviation
performance. As systematic reviews on the application of eye tracking in aviation have been rare up to now,
this article attempts to close this gap and presents a comprehensive review of eye-tracking use in aviation
research, specifically on the flight deck, by addressing the areas and studies not addressed by the
comprehensive review of cockpit scanning literature carried out by Ziv (2016).
CONTACT Sylvia Peißl Department of Psychology, Leopold Franzens University, Innrain 52,
Innsbruck 6020, Austria.
© 2018 The Author(s). Published with license by Taylor and Francis Group, LLC
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the
original work is properly cited, and is not altered, transformed, or built upon in any way.
This study provides a comprehensive review of eye-tracking studies in the field of aviation with
results applicable to pilots. The goal of this article is to complement the recent review of aviation
eye-movement studies published by Ziv (2016). His review covered many important topics, and
this article is intended to cover additional topics and research in this important area that were
not addressed by Ziv, and thereby, collectively, with the two articles, to provide the reader with a
relatively exhaustive summary of all research done in this area. In this article, we address the
topics of human performance, aircraft design, and health and physiological factors affecting pilot
performance, and their relation to scanning and aviation, specifically research that was not
covered by Ziv (2016). The databases PsycINFO and Web of Science were searched using the
terms (eye mov* OR eye track*) and (aviat* OR pilots; PsycINFO: 500 results; Web of Science:
698 results). Papers in the field of air traffic control and papers dealing with eye movements and
alcohol or drugs were excluded. Additional literature could be found by using the reference lists
of the identified papers and by searching with the terms (eye mov* OR eye track*) AND (uav*
OR unmanned aerial vehicle OR drone* OR remotely piloted aircraft*). Eye-tracking studies that
were not conducted on pilots but published in an aviation journal such as The International
Journal of Aviation Psychology or Aviation, Space and Environmental Medicine were included in
the review. The papers were published between 1976 and 2017. Altogether 79 papers were
identified and included in the review.
The recently published review by Ziv (2016) covered 50 papers. The two reviews are intended to
be complementary and are overlapping in only 14 papers because the authors felt those papers
contributed directly to the themes of the current review. 65 papers of the present review are not
mentioned in Zivs review, whereas Ziv included 36 papers not mentioned in the present review. An
overview of the applied classification is shown in Table 1. Papers also included in Zivs review are
italicized. In particular, this review contains sections on workload, the comparison of experts versus
novices, fatigue, motion sickness and hypoxia, spatial disorientation, and the use of eye tracking in
unmanned aerial vehicle (UAV) operators, topics that were not highlighted in Zivs review.
Results: Content of Reviewed Articles
Human Performance
General Characteristics of Eye Movements
In the 1970s and 1980s, researchers were especially interested in visual search behavior and general
characteristics of eye movements (Baloh & Honrubia, 1976; for a review on visual scan patterns see
Ziv, 2016). Barnes, Benson, and Prior (1978) examined, for example, the degree to which inap-
propriate vestibular reflex eye movements could be suppressed by visual means. They demonstrated
that the mechanisms responsible for suppression of vestibular reflex eye movements and pursuit eye
movements were very similar. Enderle (1988) investigated saccadic eye-movement system (SEM)
performance in Air National Guard pilots to determine whether motor and neurosensory function
could be improved by training. His findings suggest that a time-optimal central nervous system
control mechanism exists that cannot be improved by training. Saccadic eye movements and saccade
latency in response to auditory and visual stimuli were investigated in several further studies
(Engelken & Stevens, 1989; Engelken, Stevens, & Enderle, 1991). Furthermore, coordination of
head, hand, and eye movements (e.g., to fixate targets) had been an earlier topic of eye-tracking
research (Gauthier, Martin, & Stark, 1986; Mather & Lackner, 1980; Regan & Beverley, 1980;
Vercher, Lolle, & Gauthier, 1993). Direct visually guided control of the hand can, for example,
compensate for performance decrements caused by hand vibration in the cockpit (Martin, Roll, & Di
Renzo, 1991).
Table 1. Overview of papers included in the review.
Human Performance Aircraft Design Health & Physiological Factors Affecting Performance
General Characteristics of
Eye Movements
SA Workload Cognitive
Experts vs.
Remotely Piloted
Hypoxia Spatial
Allsop & Gray, 2014 X
Baloh & Honrubia,
Barnes et al., 1978 X
Bellenkes et al.,
Beringer & Ball,
Bos et al., 2002 XX
Causse et al., 2011 X
Charbonneau et al.,
Cheung & Hofer,
Cheung et al., 2004 X
Dahlstrom et al.,
Dahlstrom &
Nahlinder, 2009
DeMaio et al., 1978 X
Di Stasi et al., 2014 X
Di Stasi et al., 2016 X
Diaz-Piedra et al.,
Diels et al., 2007 X
Doane & Sohn,
Duley, 2001 X
Enderle, 1988 X
Engelken &
Stevens, 1989
Engelken et al.,
Gauthier et al.,
Hankins & Wilson,
Heaton et al., 2014 X
(Continued )
Table 1. (Continued).
Human Performance Aircraft Design Health & Physiological Factors Affecting Performance
General Characteristics of
Eye Movements
SA Workload Cognitive
Experts vs.
Remotely Piloted
Hypoxia Spatial
Helleberg &
Wickens, 2003
Hoepf et al., 2015 X
Hu & Stern, 1998 X
Hughes & Creed,
Itoh et al., 1990 XX
Kilingaru et al.,
Kim et al., 2010 X
Kirby et al., 2014 X
Kooi, 2011 X
Kotulak & Morse,
Kotulak & Morse,
Kowalczuk et al.,
LeDuc et al., 2005 X
Lefrancois et al.,
Li et al., 2016 XX
Malcolm, 1984 X
Martin et al., 1991 X
Mather & Lackner,
McIntire et al., 2013 X
McKinley et al.,
Morris, 1985 X
Morris & Miller,
Muehlethaler &
Knecht, 2016
Muthard &
Wickens, 2006
Ottati et al., 1999 X
Pialoux et al., 1976 X
(Continued )
Table 1. (Continued).
Human Performance Aircraft Design Health & Physiological Factors Affecting Performance
General Characteristics of
Eye Movements
SA Workload Cognitive
Experts vs.
Remotely Piloted
Hypoxia Spatial
Previc et al., 2009 X
Regan & Beverley,
Robinski & Stein,
Rowland et al.,
Schriver et al., 2008 XX
Sirevaag et al.,
Stepanek et al.,
Stern et al., 1990 X
Sullivan et al., 2011 X
Szczechura et al.,
Tichon et al., 2014 X
Tole et al., 1982 X
Tvaryanas, 2004 X
Van De Merwe
et al., 2012
Vercher et al., 1993 X
Vine et al., 2015 X
Webb & Griffin,
Wickens &
Alexander, 2009
Wickens et al., 2003 XX
Wickens et al., 2007 XX
Wickens, 2015 XX
Wright & McGown,
Wright et al., 2005 X
Wu et al., 2015 X
Yang et al., 2013 X
Yu et al., 2014 XXX
Yu et al., 2016 XX
Ziv, 2016 XX
Note. General characteristics of eye movements = research on basic scanning behavior, on basic attentional models and on general characteristics of eye movements; SA = situation awareness.
Papers included in Zivs(2016) review are shown in italics.
Performance Decrements and Situational Awareness
The most common use of eye tracking in the field of aviation is still in the context of performance.
Several researchers have attempted to find explanations for performance decrements by means of
eye-tracking measures. For instance, Sirevaag et al. (1999) studied performance of helicopter pilots in
a high-fidelity simulator along with indices of oculomotor activity during low-level flight. This study
was part of a larger project with the goal of developing an oculomotor-based system capable of real-
time attention monitoring especially in aviation operational situations. The study revealed that time
on task, across the 50-min simulation, resulted in longer blink duration and fewer and later reactive
saccades, and this was coupled with increased performance variability, even as overall performance
did not decline. Overall, Sirevaag et al. (1999) found several relationships between performance and
other nonvisual psychophysiological metrics.
Performance decrements and pilot errors sometimes occur because of a lack of situation aware-
ness (SA; Kilingaru, Tweedale, Thatcher, & Jain, 2013). SA is frequently defined as the perception of
elements in the environment within a volume of time and space [Level 1], the comprehension of
their meaning [Level 2] and the projection of their status in the near future [Level 3](Endsley,
1988). Inasmuch as the first stage of SA depends on perception and because the majority of
information in a cockpit is presented visually, one method to measure situational awareness in a
cockpit is by examining eye movements. For this reason it seems plausible to assess a pilotsSAby
observing eye movements (Kilingaru et al., 2013; Van De Merwe, Van Dijk, & Zon, 2012). Wickens
et al. (2007) assessed Stage 1 of SA directly via the allocation of visual attention. The authors
attention-situational-awareness model using SEEV (salience, effort, expectancy, value; see Wickens,
2015) was based on the notion that eye movements can be a direct indicator of attention among
pilots and that at least Stage 1 of SA can be assessed via eye tracking. Van De Merwe et al. (2012)
measured SA by studying the pilotssearch patterns (fixation rates on the display, dwell time on the
display, and scanning entropy) in relation to information acquisition. The authors stated that this
was done to assess Stage 1 as well as Stage 3 SA via eye tracking. Specifically, pilots had to deal with a
fuel leak that was expected to hamper SA. Pilots with high SA as assessed by their scanning measure
(e.g., high fixation rate on the electronic centralized aircraft monitoring display) found the source of
the malfunction earlier, showing more structured and predictable cockpit scanning. Yu, Wang, Li,
and Braithwaite (2014) suggested integrating eye-tracking devices into simulators for promoting SA
training. They found pilots with better SA performance in the simulator showing lower perceived
workload. Muehlethaler and Knecht (2016) developed a SA training design for General Aviation
pilots using eye measurements.
Wickens and McCarley (2008) developed an attentionsituation awareness (ASA) model that
predicted the level of pilot SA from the optimality of the pilots scan path as prescribed by the SEEV
model of visual scanning (see earlier). A second component of the model was related to the decay of
SA of a displayed variable, during the unattended interval following the most recent visual scan of
that display. They found that the ASA model could predict differences in traffic awareness of
General Aviation pilots.
Of course, pilot errors and performance decrements can result from causes beyond a loss of SA.
Lefrancois, Matton, Gourinat, Peysakhovich, and Causse (2016) named automation addiction due to
pressure and fatigue as a factor leading to errors in monitoring flight instruments. They examined 20
pilots who were instructed to land an Airbus A320 manually in a flight simulator. A quarter of the
pilots were unable to stabilize the aircraft and made the decision to go around. The authors
concluded that gaze patterns for these pilots were suboptimal in comparison to those of the pilots
who stabilized and landed the aircraft most precisely. For example, they did not sufficiently scan
primary flight instruments to fly the approach. The authors assumed that these pilots were not
sufficiently trained to fly manual approaches. In a closely related experiment, Dehais, Behrend,
Peysakhovich, Causse, and Wickens (2017) compared pilot groups who inappropriately flew on
during an ill-advised approach and those who appropriately decided to go around. They observed
differences in visual allocation of attention between the two groups, as well as differences between
the pilot flying and the copilot navigating. The latter differences were quantified and reflected both
in where the two types looked inside and outside the cockpit, and also in the qualitative style of eye
Some researchers have attempted to use eye movements for an assessment of mental workload
during different tasks or for assessing workload influence on flight performance (e.g., Dahlstrom &
Nahlinder, 2009; Hankins & Wilson, 1998; Li, Yu, Braithwaite, & Greaves, 2016; Szczechura, Terelak,
Kobos, & Pinkowski, 1998). In addition, the visual scanning of flight instruments was found to vary
as a function of the level of difficulty of a task (Tole, Stevens, Harris, & Ephrath, 1982), in a way that
indicated that the average dwell time of each fixation on the instrument panel increased as a function
of the load and increased as a function of the estimated skill level of a pilot. The authors concluded
that visual scanning of instruments might be an indicator for both skill and workload (besides
showing a difference between experts and novices, with novices being affected by high workload
much more than experts).
Cognitive Processes
In addition to measuring workload and attentional processes, eye tracking is used for representing
cognitive processes involved in decision making or action planning. As an example, Doane and Sohn
(2000) presented a predictive cognitive model called ADAPT that models performance in a changing
flight environment and predicts pilotsvisual attention. Another result in the context of decision
making is that fixation times are longer for situations with high uncertainty than for situations with
low uncertainty and low risk (fixating on a simplified instrument-landing-system instrument during
landing; Causse, Baracat, Pastor, & Dehais, 2011).
Experts and Novices
Performance-related studies often include pilots with varied flying experience, and thus examine
how scanning changes with expertise, where this can be operationally defined by the total flight
hours, flying qualifications, and the proficiency level of the pilot (Wickens & Dehais, 2018). More
experienced pilots seem to make better decisions in terms of speed and accuracy, allocate more
attention to relevant cues when failures are present (measured by percentage dwell time on areas of
interest), and show better performance in motion anticipation (Schriver, Morrow, Wickens, &
Talleur, 2008). Additionally, the amount of attention to cues seems to be associated with decision
accuracy (Schriver et al., 2008). Expert pilots adapt their attentional strategies by more flexibly
responding to changing demands (Bellenkes, Wickens, & Kramer, 1997). In comparison with novice
pilots, they better maintain a constant altitude with a helicopter and differ generally in visual
instrument scan patterns or in eye movements in response to motion (Bos, Bles, & De Graaf,
2002; DeMaio, Parkinson, & Crosby, 1978; Kirby, Kennedy, & Yang, 2014; Pialoux et al., 1976;
Sullivan, Day, & Kennedy, 2011). For example, experienced helicopter pilots and novice pilots differ
in the frequency of scanning out of the window (Sullivan et al., 2011; Yang, Kennedy, Sullivan, &
Fricker, 2013; Yu, Wang, Li, Braithwaite, & Greaves, 2016). Whether experts or novices show more
or less out-the-window (OTW) gazes is not generally agreed and depends on factors such as mission
demands. Independently, it can be expected that scans by experts are more targeted than scans by
novices. Robinski and Stein (2013) investigated experienced and inexperienced military helicopter
pilots and proposed eye tracking as a feedback tool for pilots during training as the pilots were not
always aware of their actual scanning techniques. Along similar lines, studies by Kim, Palmisano,
Ash, and Allison (2010) and Ottati, Hickox, and Richter (1999) also revealed that experienced pilots
develop unique eye-scanning strategies for better performance. Fox, Merwin, Marsh, McConkie, and
Kramer (1996) observed that expert pilots used qualitatively different scanning patterns than novices
and had better mental models of their aircraft.
Several models of visual attention have been proposed. Senders (1964), for example, developed
the first quantitative model of human monitoring and controlling behavior in the tradition of
queuing theory and supervisory control. Carbonell, Ward, and Senders (1968) validated this theore-
tical model in the field, specifically during approach and landing in a flight simulator. Based on this
early research of Senders (1964) and, in the cockpit, of Carbonell et al. (1968), it has been argued that
optimal models of information seeking and scanning can be used to set a gold standardfor optimal
scanning, and therefore maintaining optimal SA. Wickens, Goh, Helleberg, Horrey, and Talleur
(2003) developed such a modelthe SEEV modelbased on the idea that optimal scanning is driven
by the bandwidth (or expectancy) of flight-deck information to the extent that such information is
valuable; and resisting unwanted influences of effort (scanning long distances) and salience, unless
these commodities are positively correlated with expectancy and value. As noted earlier, they found
that pilots who better adhered to these prescriptions were superior in maintaining traffic awareness
(Wickens & McCarley, 2008).
In summary, eye-movement measures have proven to be valuable for detecting certain perfor-
mance decrements, assessing high-workload conditions and SA, and discriminating between more-
experienced and less-experienced pilots. Depending on author and theory, at least Stage 1 of SA can
be assessed via eye tracking. Pilots with high SA have shown, for instance, high fixation rates on the
electronic centralized aircraft monitoring display when searching for a special malfunction together
with a structured scanning behaviour (Van De Merwe et al., 2012). Structured cockpit scanning and
more attention to relevant cues when failures are present (e.g., the percentage dwell time on areas of
interest) can be an indicator of expertise and good performance. The average dwell time of fixations
on the instrument panel can be an indicator of workload, whereas the parameter of fixation time can
be useful to estimate uncertainty and risk of a situation. Different models of visual attention and
scanning were proposed (e.g., modeling performance in a changing flight environment and predict-
ing visual attention; e.g., ADAPT; Doane & Sohn, 2000) or predicting the level of pilot SA from the
optimality of the pilots scan path (ASA; Wickens & McCarley, 2008). An important finding of the
reviewed eye-tracking studies is that pilots were not always aware of their actual scanning techni-
ques. The importance of appropriate visual scanning techniques to support flight performance,
decision making, and SA suggests that eye-tracking devices integrated into flight simulators could
be useful for training to promote SA and efficient and effective scanning behavior.
Aircraft Design
Aviation Displays
A wide variety of eye-tracking studies address the topic of aviation displays. These include examining
eye movements in peripheral vision displays (Malcolm, 1984), cathode-ray-tube (CRT) displays
(Itoh, Hayashi, Tsukui, & Saito, 1990), color-coded avionic displays (Hughes & Creed, 1994), aviator
helmet-mounted displays (e.g., for night vision; Charbonneau, Leger, & Claverie, 2010; Kotulak &
Morse, 1994,1995), cockpit displays of traffic and weather information (CDTI; Duley, 2001;
Muthard & Wickens, 2006), synthetic vision displays (Thomas & Wickens, 2004; Wickens &
Alexander, 2009; Wickens & McCarley, 2008), highway-in-the-sky head-up displays (Beringer &
Ball, 2001), or dual-layer displays (two depth layers, physically separated; Kooi, 2011). In such
studies, measuring eye movements can help one to understand performance (e.g., in terms of task
management) and contributes both to understanding pilotsscanning behaviors, and generating
guidance for better display design.
Details on attentional mechanisms regarding different displays are offered in the recently pub-
lished literature review by Ziv (2016). The results of the review showed that various onboard
technologies such as synthetic-vision displays could divert pilotsattention from outside the cockpit
to the instrument panel. Hazards outside of the cockpit could be missed. Nethertheless visual
presentation of information (in comparison to auditory or redundant representation) seems to be
most beneficial as it is least interruptive of other tasks such as traffic montitoring (e.g., Helleberg &
Wickens, 2003; Wickens et al., 2003).
Unmanned Aerial Vehicle
Only four published papers have addressed eye movements on a UAV flight display. Tvaryanas
(2004)wanted to find out how pilots extracted information from an RQ1-Predator display, which he
referred to as not being constructed according to basic human factors principles. On the display,
airspeed, altitude, angle of attack, and vertical speed are given in text boxes moving linearly up and
down the screen. Tvaryanas found dwell frequencies for the primary flight instruments to be
different from those reported for more traditional aircraft (with a pilot onboard and traditional
arrangement of instrumentation). The moving text boxes called for visual fixations that were typical
of quantitative instruments (long dwells), which the author described as a cognitively inefficient way
to present information.
Hoepf, Middendorf, Epling, and Galster (2015) reported that eye-movement data provide cues
that the UAV pilot is facing increased workload. Blink duration, blink rate, and pupil diameter
evidenced sensitivity to changes of workload. High-workload conditions reflected in low blink rate,
shorter blink duration, and larger pupil diameter than values obrtained in low-workload conditions.
The authors suggested implementing automated systems to monitor changes in physiological data
(e.g., eye movements and heart rate variability), with the potential to detect impending performance
decrements resulting from elevated workloads.
McKinley, McIntire, Schmidt, Repperger, and Caldwell (2011) used the metric of total eye closure
duration to examine the presence of fatigue. They detected no fatigue effects in simulated UAV tasks
following sleep deprivation, whereas they did find fatigue effects for other more traditional flight
simulator tasks, namely a target aquisition task and a psychomotor vigilance test. The authors
explained this result with an optimal arousalfor the UAV task that was more difficult and complex
in relation to the other tasks. The optimal arousal then resulted in better performance for this one
task despite the sleep deprivation (see discussion of fatigue for this study as well).
The results of a study by McIntire, McKinley, McIntire, Goodyear, and Nelson (2013) indicated
that eye tracking can be used to detect changes in vigilance for UAV pilots. Poor attention to a
vigilance task (Cyber Defense Task) could be seen in an increase in blink rate, longer blink
durations, and a longer eye closure time. It is interesting that work has not apparently been done
or published that systematically compares scanning strategies between UAVs and manned aircraft,
flying comparable manuevers with similar flight dynamics.
As can be seen in this section, displays for unmanned aircraft are not always designed according
to basic human factors principles to present information such that it is easily recognized and
interpreted. With synthetic-vision displays, for instance, pilotsattention could be diverted too
much from outside the cockpit to inside the cockpit. The moving text boxes of the RQ1-Predator
(airspeed, altitude, angle of attack, vertical speed) might result in longer dwells compared to a more
traditional instrument panel arrangement. The visual behavior measurements of dwells, blink
duration, blink rate, pupil diameter, and eye closure time have proven to be useful in UAV studies
(e.g., showing changes in workload).
Health and Physiological Factors Affecting Performance
Fatigue, Sleep, and Stress
Fatigue places pilots at elevated risk for impaired performance, errors, and accidents. Therefore, a
research goal is to detect fatigue or fatigue-related performance decrements early to prevent errors
and injury as well as to ensure optimal piloting. The measurement of eye movements has proven to
be a valuable method to achieve this goal (e.g., Heaton, Maule, Maruta, Krystow, & Ghajar, 2014;
McKinley et al., 2011; Morris, 1985).
Researchers have investigated fatigue by depriving individuals of sleep and then trying to find metrics
to detect fatigue. Previc et al. (2009) reported a peak in flying errors after about 24 to 28 hr of wakefulness
together with peaks in subjective fatigue. Instrument scanning was largely unaffected by sleep depriva-
tion. Heaton et al. (2014) observed that attention in terms of reaction time and gaze position (visual
tracking) is impaired after acute sleep deprivation (26 hr). The authors provided an approach for
detecting fatigue and assessing readiness in flight personnel (including ground-support crews).
Rowland et al. (2005) and LeDuc, Greig, and Dumond (2005) found that saccadic velocity was
sensitive to (declined with) total sleep deprivation. Di Stasi et al. (2016) obtained similar results by
finding the metric saccadic velocity decreased after long simulated flights in comparison to short
simulated flights. The authors suggested saccadic velocity as a biomarker for aviator fatigue as did
Diaz-Piedra et al. (2016) for warfighter fatigue. McKinley et al. (2011) evaluated two kinds of eye
metricsnamely total eye closure duration and approximate entropyto detect fatigue in tasks
relevant to military aviation after sleep deprivation (1428 hr). McKinley et al. found the metric
of appropriate entropy (ApEn) correlates with performance decrements and suggested using this
metric as a detector of fatigue in both manned and unmanned military aviation tasks. Their results
regarding eye-closure duration were ambigious, as these generally increased with the level of sleep
deprivation but not consistently over all sessions (as described earlier). Morris and Miller (1996)
found that the eye metrics long closure rate (LCR) and blink amplitude (BL) were the best predictors
of a fatigue-induced increase in errors in straight-and-level flying tasks.
Scanning measures can also be coupled with other measures to measure pilot fatigue passively.
Wright and McGown (2001) suggested using measurements of eye movements, wrist activity or head
movements as the basis of an alarm system to prevent involuntary sleep. Efforts to realize this kind
of alarm system were made by Wright, Powell, McGown, Broadbent, and Loft (2005) in a study in
which 21 Air New Zealand pilots wore a wrist alertness device. During a flight between Auckland
and Perth the presence of sleepiness and sleep was determined using EEG and eye movements. The
alertness device could awaken pilots effectively during flight and was rated acceptable to use by the
aircrew. Wu, Wanyan, and Zhuang (2015) provided a mathematical model connecting pilotsvisual
attention allocation and flight fatigue.
In addition to sleep and fatigue, stress is another factor that can degrade flight performance. Only
a few eye-tracking studies have addressed this topic (Tichon, Mavin, Wallis, Visser, & Riek, 2014;
Vine et al., 2015). Nevertheless, results are interesting, as eye movements seem to react very
consistently to stressors such as an engine failure on take-off or can show negative emotions such
as anxiety. For example, under anxiety, attentional control is negatively affected as reflected by an
increase in the percentage of dwell time toward the outside world (Allsop & Gray, 2014). More
details on studies concerning anxiety and stress are reviewed by Ziv (2016).
Motion Sickness
Eye movements are generally related to motion sickness (MS). Visually induced MS is, for example,
lowered during fixation (Webb & Griffin, 2002). Furthermore, it has been proposed that nystagmus
might be responsible for MS. Nystagmus can be defined as a series of involuntary eye movements
generated by the stimulation of the vestibular system (Newman, 2004). The results indicate that more
rapid eye movement (higher frequency of nystagmus) is linked to the development of symptoms of MS
(Hu & Stern, 1998). Bos et al. (2002) compared aircrew members highly susceptible to MS to aircrew
members less susceptible to MS. Those highly susceptible to MS showed a slower decay of nystagmus
following cessation of motion than less susceptible subjects.
Visual fixation (to suppress eye movements) under certain conditions can reduce nystagmus
(Stern, Hu, Anderson, Leibowitz, & Koch, 1990) and MS (Webb & Griffin, 2002). However, the
effectiveness of such fixation depends on the part of the display that the pilot is fixating. Thus Diels,
Ukai, and Howarth (2007) investigated MS with radial displays and found that MS was worse when
subjects were asked to focus at targets beyond the focus of expansion compared to being asked to
fixate at the focus of expansion (or being free to move ones eyes).
10 S. PEIßL ET AL.
Studies addressing the topic of flight hypoxia in combination with eye tracking are scarce. Early
recognition of the onset of hypoxia symptoms is essential for pilots, as the time for efficient rescue
can be very short when technical systems delivering oxygen in an aircraft fail (Kowoll, Welsch,
Joscht, & Gunga, 2006). Di Stasi et al. (2014) found the eye metric intersaccadic drift velocity
increased in pilots experiencing hypoxia. Stepanek et al. (2014) reported increased blink rates and
increased total saccadic times under hypoxic conditions. However, Kowalczuk et al. (2016) found
that short-term hypoxia is not reflected in saccadic movements.
Spatial Disorientation
Eye tracking has been recorded during spatial disorientation (e.g., Cheung, Hofer, Heskin, & Smith,
2004;Dahlstrom, Nahlinder, Wilson, & Svensson, 2007). The goal of recording psychophysiological
data during disorientation was to provide a model of pilot state and as a consequence be able to
understand performance and cognitive demands during flight. One result of such studies is that
pitch illusiona false sensation of pitch resulting from accelerationaffects visual scanning beha-
vior (Cheung et al., 2004). Another interesting result is that pupil diameter significantly decreases
during acceleration (Cheung & Hofer, 2003).
To summarize this section, it can be said that fatigue, stress, and hypoxia all notably affect eye
movements. Several metrics can be seen as clear indicators of fatigue: visual tracking, saccadic
velocity, and approximate entropy. Total eye-closure duration is responsive to fatigue in tasks
with manned aircraft but not in tasks with unmanned aircraft. Long closure rate and blink amplitude
predict fatigue-induced increases in errors in straight-and-level flight tasks. Anxiety is reflected in an
increase in the percentage of dwell time on the outside world. MS can be reduced by visual fixation
on special points on a display. Under hypoxic conditions blink rates, total saccadic times, and
intersaccadic drift velocity increase. The metric of pupil diameter is mentioned in the context of
spatial disorientation: Under acceleration pupil diameter decreases.
Discussion, Conclusions, and Future Directions
The present review of 79 studies has highlighted and partially integrated the extensive research that
has been conducted to understand how the pilots invaluable resource, the eyes, and by extension
visual attention, contribute to the understanding of cognitive behavior as well as the pilots under-
lying psychological and physiological state.
We wish to emphasize that our review should be considered complementary to that of Ziv (2016),
overlapping in coverage of a few studies so that the reader can gain a relatively complete picture of
the areas highlighted here without having to access that review; but more important, gain a near
complete understanding of the extant research on eye movement in pilot behavior and physiology by
referencing both reviews.
We believe that both reviews taken together provide a foundation from which many important
research questions can be asked and answered by future experiments, particularly with the increas-
ingly agile techniques that newer, lighter, and less intrusive recording techniques can avail, when
coupled with more powerful analysis tools.
In particular, we advocate research to attain a better understanding of the capabilities of oculomotor
measures, coupled with other behavioral and physiological measures, to make online, real-time infer-
ences of the pilots degree of engagement in the flight task. In a complementary fashion, a great deal more
might be understood regarding the pilots possible complacency in monitoring automation.
Sylvia Peißl
Rithi Baruah
Allsop, J., & Gray, R. (2014). Flying under pressure: Effects of anxiety on attention and gaze behavior in aviation.
Journal of Applied Research in Memory and Cognition,3,6371. doi:10.1016/j.jarmac.2014.04.010
Baloh, R. W., & Honrubia, V. (1976). Reaction time and accuracy of the saccadic eye movements of normal subjects in
a moving-target task. Aviation, Space, and Environmental Medicine,47, 11651168.
Barnes, G. R., Benson, A. J., & Prior, A. R. (1978). Visual-vestibular interaction in the control of eye movement.
Aviation, Space, and Environmental Medicine,49, 557565.
Bellenkes, A. H., Wickens, C. D., & Kramer, A. F. (1997). Visual scanning and pilot expertise: The role of attentional
flexibility and mental model development. Aviation, Space, and Environmental Medicine,68, 569579.
Beringer, D. B., & Ball, J. D. (2001). A comparison of pilot navigation performance using conventional instrumenta-
tion, head-down, and head-up highway-in-the-sky primary flight displays. Proceedings of the Human Factors and
Ergonomics Society 46
Annual Meeting,45,1620. doi:10.1177/154193120104500203
Bos, J. E., Bles, W., & De Graaf, B. (2002). Eye movements to yaw, pitch, and roll about vertical and horizontal axes:
Adaptation and motion sickness. Aviation, Space, and Environmental Medicine,73(5), 436445.
Carbonell, J. R., Ward, J. L., & Senders, J. W. (1968). A queuing model of visual sampling experimental validation.
IEEE Transactions on Man-Machine Systems. MMS-9,8287. doi:10.1109/TMMS.1968.300041
Causse, M., Baracat, B., Pastor, J., & Dehais, F. (2011). Reward and uncertainty favor risky decision-making in pilots:
Evidence from cardiovascular and oculometric measurements. Applied Psychophysiology and Biofeedback,36(4),
231242. doi:10.1007/s10484-011-9163-0
Charbonneau, M., Leger, A., & Claverie, B. (2010). Eyepiece focus and vergence/accommodation conflict:
Implications for night-vision devices. Journal of the Society for Information Display,18(5), 376385.
Cheung, B., & Hofer, K. (2003). Eye tracking, point of gaze, and performance degradation during disorientation.
Aviation, Space and Environmental Medicine,74,1120.
Cheung, B., Hofer, K., Heskin, R., & Smith, A. (2004). Physiological and behavioral responses to an exposure of pitch
illusion in the simulator. Aviation, Space, and Environmental Medicine,75, 657665.
Dahlstrom, N., & Nahlinder, S. (2009). Mental workload in aircraft and simulator during basic civil aviation training.
The International Journal of Aviation Psychology,19(4), 309325. doi:10.1080/10508410903187547
Dahlstrom, N., Nahlinder, S., Wilson, G. G., & Svensson, E. (2007). Recording of psychophysiological data during
aerobatic training. The International Journal of Aviation Psychology,21(2), 105122. doi:10.1080/
Dehais, F., Behrend, J., Peysakhovich, V., Causse, M., & Wickens, C. D. (2017). Pilot flying and pilot monitorings
aircraft state awareness during go-around execution in aviation: A behavioral and eye tracking study. The
International Journal of Aerospace Psychology,27(12), 1528. doi:10.1080/10508414.2017.1366269
DeMaio, J., Parkinson, S. R., & Crosby, J. V. (1978). A reaction time analysis of instrument scanning. Human Factors,
20(4), 467471. doi:10.1177/001872087802000411
Di Stasi, L. L., Cabestrero, R., McCamy, M. B., Rios, F., Catena, A., Quiros, P., . . . Martinez-Conde, S. (2014).
Intersaccadic drift velocity is sensitive to short-term hypobaric hypoxia. European Journal of Neuroscience,39(8),
13841390. doi:10.1111/ejn.12482
Di Stasi, L. L., McCamy, M. B., Martinez-Conde, S., Gayles, E., Hoare, C., Foster, M., . . . Macknik, S. L. (2016). Effects
of long and short simulated flights on the saccadic eye movement velocity of aviators. Physiology & Behavior,153,
9196. doi:10.1016/j.physbeh.2015.10.024
Diaz-Piedra, C., Rieiro, H., Suarez, J., Rios-Tejada, F., Catena, A., & Di Stasi, L. (2016). Fatigue in the military:
Towards a fatigue detection test based on the saccadic velocity. Physiological Measurement,37(9), 6275.
Diels, C., Ukai, K., & Howarth, P. A. (2007). Visually induced motion sickness with radial displays: Effects of gaze
angle and fixation. Aviation, Space, and Environmental Medicine,78(7), 659665.
Doane, S. M., & Sohn, Y. W. (2000). ADAPT: A predictive cognitive model of user visual attention and action
planning. User Modeling and User-Adapted Interaction,10(1), 145. doi:10.1023/A:1008311003128
Duley, J. A. (2001). Effects of cockpit display of traffic information on visual attention and eye movements in the
general aviation cockpit. Disseration Abstracts International: Section B: the Sciences and Engineering,62(2B), 1122
Enderle, J. D. (1988). Observations on pilot neurosensory control performance during saccadic eye movements.
Aviation, Space, and Environmental Medicine,59(4), 309314.
Endsley, M. R. (1988). Design and evaluation for situation awareness enhancement. Proceedings of the Human Factors
Society 32nd Annual Meeting, (pp. 97101). Santa Monica, CA: Human Factors and Ergonomics Society.
Engelken, E. J., & Stevens, K. W. (1989). Saccadic eye movements in response to visual, auditory, and bisensory
stimuli. Aviation, Space, and Environmental Medicine,60(8), 762769.
Engelken, E. J., Stevens, K. W., & Enderle, J. D. (1991). Relationships between manual reaction time and saccade
latency in response to visual and auditory stimuli. Aviation, Space, and Environmental Medicine,62(4), 315319.
12 S. PEIßL ET AL.
Fox, J., Merwin, D., Marsh, R., McConkie, G., & Kramer, A. (1996). Information extraction during instrument flight: An
evaluation of the validity of the eye-mind hypothesis. Proceedings of the Human Factors and Ergonomics Society
40th Annual Meeting (pp. 7781). Los Angeles, CA: SAGE Publications Sage CA.
Gauthier, G. M., Martin, B. J., & Stark, L. W. (1986). Adapted head- and eye-movement responses to added-head
inertia. Aviation, Space and Environmental Medicine,57(4), 336342.
Geratewohl, S. (1987). Leitfaden der militärischen Flugpsychologie. München, Germany: Verlag für
Hankins, T. C., & Wilson, G. F. (1998). A comparison of heart rate, eye activity, EEG and subjective measures of pilot
mental workload during flight. Aviation, Space, and Environmental Medicine,69(4), 360368.
Heaton, K. J., Maule, A. L., Maruta, J., Krystow, E. M., & Ghajar, J. (2014). Attention and visual tracking degradation
during acute sleep deprivation in a military sample. Aviation, Space, and Environmental Medicine,85, 497503.
Helleberg, J. R., & Wickens, C. D. (2003). Effects of data-link modality and display redundancy on pilot performance:
An attentional perspective. The International Journal of Aviation Psychology,13(3), 189210. doi:10.1207/
Hoepf, M., Middendorf, M., Epling, S., & Galster, S. (2015). Physiological indicators of workload in a remotely piloted
aircraft simulation. Air Force Research Lab Wright-Patterson AFB, OH: United States Air Force Air Material
Hu, S., & Stern, R. (1998). Optokinetic nystagmus correlates with severity of vection-induced motion sickness and
gastric tachyarrhythmia. Aviation, Space, and Environmental Medicine,69(12), 11621165.
Hughes, P., & Creed, D. J. (1994). Eye movement behavior viewing colour-coded and monochrome avionic displays.
Ergonomics,37(11), 18711885. doi:10.1080/00140139408963681
Itoh, Y., Hayashi, Y., Tsukui, I., & Saito, S. (1990). The ergonomic evaluation of eye movement and mental workload
in aircraft pilots. Ergonomics,33(6), 719733. doi:10.1080/00140139008927181
Kilingaru, K., Tweedale, J. W., Thatcher, S., & Jain, L. C. (2013). Monitoring Pilot Situation Awareness,24(3), 457466.
Kim, J., Palmisano, S. A., Ash, A., & Allison, R. S. (2010). Pilot gaze and glideslope control. ACM Transactions on
Applied Perception,7(3), 18. doi:10.1145/1773965.1773969
Kirby, C. E., Kennedy, Q., & Yang, J. H. (2014). Helicopter pilot scan techniques during low-altitude high-speed flight.
Aviation, Space, and Environmental Medicine,85(7), 740745. doi:10.3357/ASEM.3888.2014
Kooi, F. (2011). A display with two depth layers: Attentional segregation and declutter. In C. Roda (Ed.), Human
attention in digital environments (pp. 245259). Cambridge, UK: Cambridge University Press.
Kotulak, J. C., & Morse, S. E. (1994). Focus adjustment effects on visual acuity and oculomotor balance with aviator
night vision displays. Aviation, Space, and Environmental Medicine,65(4), 348353.
Kotulak, J. C., & Morse, S. E. (1995). Oculomotor responses with aviator helmet-mounted displays and their relation to
in-flight symptoms. Human Factors,37(4), 699710. doi:10.1518/001872095778995544
Kowalczuk, K. P., Gazdzinski, S. P., Janewicz, M., Gasik, M., Lewkowicz, R., & Wylezol, M. (2016). Hypoxia and
coriolis illusion in pilots during simulated flight. Aerospace Medicine and Human Performance,87(2), 108113.
Kowoll, R., Welsch, H., Joscht, B., & Gunga, H. C. (2006). Hypoxie im Flugzeug Flugphysiologische Betrachtungen.
The Physiology of Flight Hypoxia. Deutsches Ärzteblatt,103(13), A 851855.
LeDuc, P. A., Greig, J. L., & Dumond, S. L. (2005). Involuntary eye responses as measures of fatigue in U.S. Army
apache aviators. Aviation, Space, and Environmental Medicine,76(7, Suppl.), C86C91.
Lefrancois, O., Matton, N., Gourinat, Y., Peysakhovich, V., & Causse, M. (2016). The role of pilotsmonitoring strategies
in flight performance. Paper presented at the European Association of Aviation Psychology Conference EAAP32,
Cascais, Portugal. Retrieved from
Li, W. C., Yu, C. S., Braithwaite, G., & Greaves, M. (2016). Pilotsattention distributions between chasing a moving
target and a stationary target. Aerospace Medicine and Human Performance,87(12), 989995. doi:10.3357/
Malcolm, R. (1984). Pilot disorientation and the use of a peripheral vision display. Aviation, Space, and Environmental
Medicine,55(3), 231239.
Martin, B. J., Roll, J. P., & Di Renzo, N. (1991). The interaction of hand vibration with oculomanual coordination in
pursuit tracking. Aviation, Space, and Environmental Medicine,62(2), 145152.
Mather, J., & Lackner, J. R. (1980). Multiple sensory and motor cues enhance the accuracy of pursuit eye movements.
Aviation, Space, and Environmental Medicine,51(9), 856860.
McIntire, L., McKinley, R. A., McIntire, J., Goodyear, C., & Nelson, J. (2013). Eye metrics: An alternative vigilance
detector for military operators. Military Psychology,25(5), 502513. doi:10.1037/mil0000011
McKinley, R. A., McIntire, L. K., Schmidt, R., Repperger, D. W., & Caldwell, J. A. (2011). Evaluation of eye metrics as a
detector of fatigue. Human Factors,53(4), 403414. doi:10.1177/0018720811411297
Morris, T. L. (1985). Electrooculographic indices of changes in simulated flying performance. Behavior Research
Methods, Instruments and Computers,17(2), 176183. doi:10.3758/BF03214378
Morris, T. L., & Miller, J. C. (1996). Electrooculographic and performance indices of fatigue during simulated flight.
Biological Psychology,42(3), 343360. doi:10.1016/0301-0511(95)05166-X
Mosier, K. L. (2010). The human in flight. From kinesthetic sense to cognitive sensibility. In E. Salas & D. Maurino
(Eds.), Human factors in aviation (pp. 175207). London, UK: Elsevier.
Muehlethaler, C. M., & Knecht, C. P. (2016). Situation awareness training for general aviation pilots using eye tracking.
IFAC-PapersOnLine,49(19), 6671. doi:10.1016/j.ifacol.2016.10.463
Muthard, E. K., & Wickens, C. D. (2006). Pilots strategically compensate for display enlargements in survellaince and
flight control tasks. Human Factors,48(1), 166181. doi:10.1518/001872006776412225
Newman, D. (2004). Alcohol and human performance from an aviation perspective: A review (Research Report). Perth,
Australia: Australian Transport Safety Bureau.
Ottati, W. L., Hickox, J. C., & Richter, J. (1999). Eye scan patterns of experienced and novice pilots during visual flight
rules (VFR) navigation. Proceedings of the 43rd Human Factors and Ergonomics Society Annual Meeting (pp. 66
70). Los Angeles, CA: SAGE Publications Sage CA.
Pialoux, P., Fontelle, P., Courtin, P., Gibert, A., Robert, P., Blanc, P., & Lafontaine, E. (1976). Vestibular habituation in
flight crew. Aviation, Space, and Environmental Medicine,47, 302307.
Previc, F. H., Lopez, N., Ercoline, W. R., Daluz, C. M., Workman, A. J., Evans, R. H., & Dillon, N. A. (2009). The
effects of sleep deprivation on flight performance, instrument scanning, and physiological arousal in pilots. The
International Journal of Aviation Psychology,19(4), 326346. doi:10.1080/10508410903187562
Rayner, K. (1978). Eye movements in reading and information processing. Psychological Bulletin,85(3), 618660.
Regan, D., & Beverley, K. I. (1980). Device for measuring the precision of eye-hand coordination while tracking
changing size. Aviation, Space, and Environmental Medicine,51(7), 688693.
Robinski, M., & Stein, M. (2013). Tracking visual scanning techniques in training simulation for helicopter landing.
Journal of Eye Movement Research,6(2), 117.
Rowland, L. M., Thomas, M. L., Thorne, D. R., Sing, H. C., Krichmar, J. L., Davis, H. Q., . . . Belenky, G. (2005).
Oculomotor responses during partial and total sleep deprivation. Aviation, Space, and Environmental Medicine,76
(7), 104113.
Schriver, A. T., Morrow, D. G., Wickens, C. D., & Talleur, D. A. (2008). Expertise differences in attentional strategies
related to pilot decision making. Human Factors,50(6), 864879. doi:10.1518/001872008X374974
Senders, J. (1964). The human operator as a monitor and controller of multidegree of freedom systems. IEEE
Transactions on Human Factors in Electronics, HFE-5,26. doi:10.1109/THFE.1964.231647
Sirevaag, E., Rohrbaugh, J. W., Stern, J. A., Vedeniapin, A. B., Packingham, K. D., & LaJonchere, C. M. (1999). Multi-
dimensional characterizations of operator state: A validation of oculomotor metrics (DOT-FAA-AM-99-28).
Washington, DC: FAA Office of Aviation Medicine.
Stepanek, J., Pradhan, G. N., Cocco, D., Smith, B. E., Bartlett, J., Studer, M., . . . Cevette, M. J. (2014). Acute hypoxic
hypoxia and isocapnic hypoxia effects on oculometric features. Aviation, Space, and Environmental Medicine,85(7),
700707. doi:10.3357/ASEM.3645.2014
Stern, R. M., Hu, S., Anderson, R. B., Leibowitz, H. W., & Koch, K. L. (1990). The effects of fixation and restricted
visual field on vection-induced motion sickness. Aviation, Space, and Environmental Medicine,61(8), 712716.
Sullivan, J. Y., Day, M., & Kennedy, Q. (2011). Training simulation for helicopter navigation by characterizing visual
scan patterns. Aviation, Space, and Environmental Medicine,82, 871878. doi:10.3357/ASEM.2947.2011
Szczechura, J., Terelak, J. F., Kobos, Z., & Pinkowski, J. (1998). Oculographic assessment of workload influence on flight
performance. The International Journal of Aviation Psychology,8(2), 157176. doi:10.1207/s15327108ijap0802_5
Thomas, L. C., & Wickens, C. D. (2004). Eye-tracking and individual differences in off-normal event detection when
flying with a synthetic vision system display.Proceedings of the 48th Annual Meeting of the Human Factors and
Ergonomics Society, Santa Monica, CA: Human Factors and Ergonomics Society.
Tichon, J. G., Mavin, T., Wallis, G., Visser, T. A., & Riek, S. (2014). Using pupillometry and electromyography to track
positive and negative affect during flight simulation. Aviation Psychology and Applied Human Factors,4(1), 2332.
Tole, J. R., Stevens, A. T., Harris, R. L., & Ephrath, A. R. (1982). Visual scanning behavior and mental workload in
aircraft pilots. Aviation, Space, and Environmental Medicine,53(1), 5462.
Tvaryanas, A. P. (2004). Visual scan patterns during simulated control of an uninhabited aerial vehicle (UAV).
Aviation, Space, and Environmental Medicine,75(6), 531538.
Van De Merwe, K., Van Dijk, H., & Zon, R. (2012). Eye movements as an indicator of situation awareness in a flight
simulator experiment. The International Journal of Aviation Psychology,22(1), 7895. doi:10.1080/
Vercher, J.-L., Lolle, M., & Gauthier, G. M. (1993). Dynamic analysis of human visuo-oculo-manual coordination
control in target tracking tasks. Aviation, Space, and Environmental Medicine,64(6), 500507.
Vidulich, M. A., Wickens, C. D., Tsang, P. S., & Flach, J. M. (2010). Information processing in aviation. In E. Salas &
D. Maurino (Eds.), Human Factors in Aviation (pp. 175207). London, UK: Elsevier.
14 S. PEIßL ET AL.
Vine, S. J., Uiga, L., Lavric, A., Moore, L. J., Tsaneva-Atanasova, K., & Wilson, M. R. (2015). Anxiety, Stress & Coping:
An International Journal,28(4), 467477. doi:10.1080/10615806.2014.986722
Webb, N. A., & Griffin, M. (2002). Optokinetic stimuli: Motion sickness, visual acuity, and eye movements. Aviation,
Space, and Environmental Medicine,73(4), 351358.
Wickens, C. D. (2015). Noticing events in the visual workplace: The SEEV and NSEEV Models. In R. Hoffman, P.
Hancock, M. Scerbo, R. Parasuraman, & J. Szalma (Eds.), The Cambridge handbook of applied perception research
(pp. 749768). Cambridge, UK: Cambridge University Press.
Wickens, C. D., & Alexander, A. L. (2009). Attentional tunneling and task management in synthetic vision displays.
The International Journal of Aviation Psychology,19(2), 182199. doi:10.1080/10508410902766549
Wickens, C. D., & Dehais, F. (2018). Aviation Expertise. In P. Ward, J. Schraagen, J. Gore, & E. Roth (Eds.), The
Oxford handbook of expertise: Research and applications. Oxford, UK: Oxford University Press.
Wickens, C. D., Goh, J., Helleberg, J., Horrey, W. J., & Talleur, D. A. (2003). Attentional models of multitask pilot
performance using advanced display technology. Human Factors,45, 360380. doi:10.1518/hfes.45.3.360.27250
Wickens, C. D., & McCarley, J. S. (2008). Applied attention theory. Boca Raton: CRC Press.
Wickens, C. D., McCarley, J. S., Alexander, A. L., Thomas, L. C., Ambinder, M., & Zheng, S. (2007). Attention-
situation awareness (ASA) model of pilot error. In D. C. Foyle & B. L. Hooey (Eds.), Human performance modeling
in aviation (pp. 213237). Boca Raton: CRC Press.
Wright, N., & McGown, A. (2001). Vigilance on the civil flight deck: Incidence of sleepiness and sleep during long-
haul flights and associated changes in physiological parameters. Ergonomics,44(1), 82106. doi:10.1080/
Wright, N., Powell, D., McGown, A., Broadbent, W., & Loft, P. (2005). Avoiding involuntary sleep during civil air
operations: Validation of a wrist-worn alertness device. Aviation, Space, and Environmental Medicine,76, 847856.
Wu, X., Wanyan, X. R., & Zhuang, D. M. (2015). Pilots visual attention allocation modeling under fatigue. Technology
and Health Care,23, 373381. doi:10.3233/thc-150941
Yang, J. H., Kennedy, Q., Sullivan, J., & Fricker, R. D. (2013). Pilot performance: Assessing how scan patterns &
navigational assessments vary by flight expertise. Aviation, Space, and Environmental Medicine,84, 116124.
Yu, C., Wang, E. M., Li, W.-C., & Braithwaite, G. (2014). Pilotsvisual scan patterns and situation awareness in flight
operations. Aviation, Space, and Environmental Medicine,85, 708714. doi:10.3357/ASEM.3847.2014
Yu, C. S., Wang, E. M. Y., Li, W. C., Braithwaite, G., & Greaves, M. (2016). Pilotsvisual scan patterns and attention
distribution during the pursuit of a dynamic target. Aerospace Medicine and Human Performance,87(1), 4047.
Ziv, G. (2016). Gaze Behavior and visual attention: A review of eye tracking studies in aviation. The International
Journal of Aviation Psychology,26(34), 75104. doi:10.1080/10508414.2017.1313096
... Clinical practitioners and researchers are therefore increasingly turning to eye tracking to provide insight into the psychological mechanisms underpinning various clinical disorders, such as depression, autism and a range of anxiety disorders [2]. Eye tracking is also gaining greater interest in applied fields including sports [3,4], medical sciences [5,6], the military and law enforcement [7], aviation [8], driving [9] and civil engineering/hazard recognition [10,11]. However, unlike observations made in more reductionist tasks (e.g., the presentation of threatening/affective versus neutral words) -where outcome measures (and the corresponding underlying mechanisms they represent) are well-established [2] there are inherent difficulties of using eye-tracking to infer specific psychological processes in dynamic and complex (i.e., applied and ecologically valid) tasks. ...
... One variant of this would be to ask whether participants are using information they are not fixating, suggesting a reliance on peripheral vision, to complete specific actions. This might be done relatively easily, since applied research often lets participants freely view their environment while monitoring gaze position (cf., Peißl et al., 2018, for a review on eye tracking in aviation; also Ziv, 2017). For example, if participants do not fixate an obstacle, but step over it, they must have used peripheral vision to do so (Marigold et al., 2007;see Marigold, 2008, for a review). ...
Full-text available
Peripheral vision is fundamental for many real-world tasks, including walking, driving, and aviation. Nonetheless, there has been no effort to connect these applied literatures to research in peripheral vision in basic vision science or sports science. To close this gap, we analyzed 60 relevant papers, chosen according to objective criteria. Applied research, with its real-world time constraints, complex stimuli, and performance measures, reveals new functions of peripheral vision. Peripheral vision is used to monitor the environment (e.g., road edges, traffic signs, or malfunctioning lights), in ways that differ from basic research. Applied research uncovers new actions that one can perform solely with peripheral vision (e.g., steering a car, climbing stairs). An important use of peripheral vision is that it helps compare the position of one’s body/vehicle to objects in the world. In addition, many real-world tasks require multitasking, and the fact that peripheral vision provides degraded but useful information means that tradeoffs are common in deciding whether to use peripheral vision or move one’s eyes. These tradeoffs are strongly influenced by factors like expertise, age, distraction, emotional state, task importance, and what the observer already knows. These tradeoffs make it hard to infer from eye movements alone what information is gathered from peripheral vision and what tasks we can do without it. Finally, we recommend three ways in which basic, sport, and applied science can benefit each other’s methodology, furthering our understanding of peripheral vision more generally.
... In addition, the level of situational awareness of the novice drivers was worse than that of experienced drivers. Dehais et al. 35 and Peißl et al. 36 proposed that the precursor to the lack of situational awareness manifests as increased visual attention to specific information, while drivers' mental demand increases, and visual search behavior decreases. This is consistent with the experimental conclusions of this paper. ...
Full-text available
This study investigated the impact of experience on the visual behavior and driving performance of high-speed train drivers, and explored the correlation between visual behavior and driving performance. Through a simulated driving task, eye movement data and operating data of novice drivers, trainee drivers, and experienced drivers in the traction stage, normal operation process stage, and braking stage were collected. Variance and linear regression were used to analyze the difference and correlation between indicators. The results show that experience could change the driver’s information collection method from long fixation to multi-frequency. Experience also increased the consistency of group operations and reduced the likelihood of hazard occurrences. Therefore, driving performance can be improved by reducing the average fixation duration of information through interface optimization.
... Head-mounted wearable eye-tracking devices allow us to capture and exploit visual information unobtrusively and hence are best suitable for aviation applications [Cognolato, 2018]. Visual cues and acquiring of cockpit instruments information are important aspects of piloting and hence eye is considered as an important sensory organ for a pilot [Peißl, 2018]. The eye gaze movement can be categorised as fixations and saccades. ...
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The Handling Qualities (HQ) of a piloted aircraft continue to pose newer challenges. While the task performance aspect of HQ is quantifiable, there is no definite method to estimate the pilot workload aspect. Hence the evaluation of pilot workload is presently based on subjective pilot opinion ratings. This thesis attempts to address the need to have an analytical approach for Pilot Workload evaluation, to supplement the subjective method of pilot opinion rating. Primary flight control inceptor is the most important interface of a pilot with the aircraft and hence the best tool to study the pilot – in – the – loop characteristics of the aircraft. Thus, Pilot Inceptor Workload (PIW), defined by Dutycycle and Aggressiveness of the inceptor movements, is considered as a good representation of pilot workload and as a simple and direct measure to understand, ‘how hard the pilot is working on the control stick’. The need for understanding the role of cognitive science in HQ evaluation has been recognized in the field of flight testing. Spare mental capacity available in a pilot directly affects his task performance. Thus, pilot cognitive load is considered to be an important domain in flight control tasks and its influence in the pilot workload. In the present research, PIW and cognitive load can be considered to represent the physical and mental aspects of the pilot workload, respectively. Also, Task Performance in a closed-loop task is considered as an independent measure of pilot workload. In 2004, William Gray developed the theory of Boundary Avoidance Tracking (BAT) which states that a pilot often acts to avoid a specific condition, as opposed to maintaining, as assumed in the case of classical point tracking concept. BAT has emerged as a novel theory in the area of handling qualities and has posed a potential scope to be implemented in HQ stress testing in the form of, Workload Buildup Flight Test Technique (WB FTT). Also, BAT theory has opened a new paradigm to characterize the pilot behavior and also provide an analytical means for the pilot-in-the-loop evaluation and supplement the subjective pilot opinion ratings. The present research is aimed to investigate pilot workload based on PIW metrics, Pilot Cognitive Load metrics, and Task Performance, as dependent variables. The statistical model involved three independent variables, namely aircraft flying qualities, secondary task, and boundary size, which individually contributed to the pilot workload in their characteristic way. The experiments were conducted in a buildup approach on a fixed base variable stability HQ research flight simulator. The results with statistical significance validated the relationships of WB FTT, with PIW and task performance, to evaluate aircraft HQ with high pilot gain or bandwidth. The results proved the application and effectiveness of WB FTT in HQ Stress Testing to elevate pilot gain, which is a crucial phase in the development of the flight control system for the new aircraft programs. The HQ of the aircraft in a given environment greatly influences the cognitive load of the pilot. In this research pilot, the cognitive load was investigated using a COTS eye gaze to record ocular parameters like fixations and saccades. The pilot cognitive load aspect of the present study includes two studies - one involving the fixed base variable stability HQ research simulator and another study involving three flights in BAES Hawk and Jaguar aircraft maneuvering in high G conditions and undertaking various training combat missions. Both studies found a significant correlation between the pilot’s cognitive load and ocular parameters, in particular, the rate of fixations. The study confirmed that ocular parameters can be detected using the COTS sensor under high G conditions and can be used to estimate pilots’ cognitive load in real-time. The inflight tests on air-to-ground dive maneuvers validated the correlation between pilot cognitive load measured as fixation rate and the closure rate to the ground as a boundary which is a significant contribution towards research on pilot workload. The outcome of this research is believed to benefit to economise the HQ evaluation process and reduce the flight test effort by having the quantifiable pilot workload measures to supplement subjective pilot opinion rating. Keywords: Handling Qualities, Pilot Workload, Boundary Avoidance Tracking, Workload Buildup Task, Cognitive Load, Eye Gaze Tracking, Fixation, Saccades
... [61] Physiological responses aim to assess workload via the analysis of physiological measures, e.g., heart rate and blood pressure variability, patterns in brain electrical activity, changes in pupil diameter, and differences in eye movement patterns [71]. Particularly eye-tracking metrics have been used and discussed frequently for workload estimation in the field of aviation (for detailed overviews of eye-tracking 3.3 Human F actors Issu es | 29 research in aviation see [72][73][74]). Physiological measures provide the advantage of assessing workload objectively and continuously. ...
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Due to the technological progress, increasingly sophisticated and highly automated systems have replaced human roles in the cockpit of commercial aircraft. Consequently, the crew size has been reduced from initially five to two cockpit crew members over the past decades. Nowadays, a captain and a first officer share the tasks throughout the flight by assuming the roles of pilot flying (PF) and pilot monitoring (PM). However, in light of the ongoing technological advancements, the logical next step seems to be a further de-crewing from two-crew operations (TCO) to single-pilot operations (SPO). To provide adequate support for the single pilot, a redesign of the cockpit is required. The present study contributes to this research area by adopting a human-centered perspective and investigating how the PF is affected by the absence of the PM during commercial SPO. A study was conducted in a fixed-base Airbus A320 flight simulator. Fourteen professional pilots participated. Their task was to fly short approach and landing scenarios at Frankfurt Airport both with and without a PM. A 2x3 factorial within-subject design was used with the factors crew (TCO and SPO) and scenario (baseline, turbulence, and abnormal). A combination of quantitative and qualitative data was collected in the form of subjective workload ratings, eye-tracking data, simulator parameters, video recordings, and debriefing interviews. The results showed that workload was not generally higher during SPO but particularly the temporal demand increased significantly. Additionally, checklist usage was less consistent and pilots handled the abnormal scenario differently when the PM was absent. The pilots’ scanning behavior was also significantly affected by the absence of the PM. Pilots had to spend considerably more time scanning secondary instruments at the expense of primary instruments. Moreover, transition behavior between the cockpit instruments and the external view was less efficient in SPO and was interpreted in terms of an overload on the pilots’ visual modality. This research will help inform the design of commercial SPO flight decks providing adequate support for the single pilot. Several implications for the design of SPO cockpits are discussed, such as headup displays, multisensory interfaces, augmented reality glasses, advanced automation, and additional support from ground operators.
The simulated patient method is becoming an increasingly popular observational method to measure practice behavior in pharmacy practice and health services research. The simulated patient method involves sending a trained individual (simulated patient among other names), who is indistinguishable from a regular consumer, into a healthcare setting with a standardized scripted request. This method has come to be accepted as being well-suited for observing practice in the naturalistic setting and has also been used as an intervention when combined with feedback and coaching. This chapter presents an overview of the method, a brief history of its use, considerations for designing, implementing, and evaluating simulated patient studies, including ethical considerations, as well as methods of analysis and mixed-methods designs.
Human factors and ergonomics (HFE) is a scientific and practical human-centered discipline that studies and improves human work performance and wellbeing in sociotechnical systems. HFE in pharmacy involves the human-centered design of systems to support individuals and teams who perform medication-related work. We define the scope of HFE methods in pharmacy as applications to pharmacy settings, such as inpatient or community pharmacies, as well as to medication-related phenomena such as medication safety, adherence, or deprescribing. This chapter presents seven categories of HFE methods suited to widespread use for pharmacy research and clinical practice, with examples of how these methods have been previously applied in pharmacy. These categories of methods are work system analysis; task analysis; workload assessment; medication safety and error analysis; user-centered and participatory design; usability evaluation; and physical ergonomics. HFE methods from these categories are used in three broad phases of human-centered design and evaluation: study, design, and evaluation. Three cases illustrate the comprehensive application of HFE to (1) medication package, label, and information design; (2) human-centered design of a digital decision aid for medication safety; and (3) risk analysis of medication cancellation.
Conference Paper
View Video Presentation: With the growing demand for air travel, there is a huge increase in the number of flights in the air at any given time. To manage this load of air traffic, system sophistication and automation plays a pivotal role in assisting air traffic controllers (ATCOs). Proficiency can be visualized as a nonlinear function that combines knowledge and its practical application. But in the case of ATCo, Expertise is objectively laid down as a consistent and accurate ability to perform the required controlling tasks. For the invited 56 ATCo’s for the experiment conducted to analyse Eye Tracking and EEG data, vital aspects of cognitive skills such as visual mapping and scanning of the Radar situation, efficient use of working memory and a diverse approach to situational awareness were recorded as key for consistent performance. ATCo responsibilities like guaranteeing safety, nominal ground vehicle/aircraft spacing, efficient runway usage and timely flight schedule and sequencing make the task cognitively demanding. Hence, the training and practice needed for a safety-critical application needs to be all-encompassing and address minute details of the process. For a long time, the traditional criteria for ATCo skill has been directly correlated to factors like age, years in training, and experience. The Longest Sequence of Vectored Aircraft (LSVA), which is the criteria to decide ATCO's efficiency, was used to group controllers and it was found to have low correlation with age. We discuss different approaches to analyze the other criteria of ATCo expertise, which are the non-conventional parameters of performance ability to carry out functions like scanning air traffic and use working memory optimally.
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Objective: Examination of the performance and visual scanning of aircrews during final approach and an unexpected go-around maneuver. Background: Accident and incident analyses have revealed that go-around procedures are often imperfectly performed because of their complexity, their high time stress, and their rarity of occurrence that avails little time for practice. We wished to examine this experimentally and establish the frequency and nature of errors in both flight-performance and visual scanning. Method: We collected flight-performance (e.g., errors in procedures, excessive flight deviations) and eye-tracking data of 12 flight crews who performed final approach and go-around flight phases in realistic full-flight transport-category simulators. Results: The pilot performance results showed that two thirds of the crews committed errors including critical trajectory deviations during go-arounds, a precursor of accidents. Eye-tracking analyses revealed that the cross-checking process was not always efficient in detecting flight-path deviations when they occurred. Ocular data also highlighted different visual strategies between the 2 crew members during the 2 flight phases. Conclusion: This study reveals that the go-around is a challenging maneuver. It demonstrates the advantages of eye tracking and suggests that it is a valuable tool for the explicit training of attention allocation during go-arounds to enhance flight safety.
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For FULL TEXT go here: Objective: The objective of this article was to review a series of studies (n = 50) regarding gaze behavior and performance when piloting an aircraft. Background: Optimal gaze behavior can lead to improved flying performance under both normal and stressful conditions. Method: A computerized as well as a manual search of the literature was conducted. Articles were grouped according to prevalent themes, such as basic cockpit visual scanning, visual scanning in the automated cockpit, effects of new technology on visual scanning, nonnormal flight circumstances, differences between experts and novices, and mathematical models of visual scanning. A summary and key findings for each theme were reported. Results: The review revealed specific gaze behaviors that might be important when performing various flight tasks and when monitoring automated processes, and that can differentiate between expert and novice pilots. However, several concerns arose from the review. Among these concerns are the unexamined role of peripheral vision, the scarcity of studies on in-flight emergencies, and the lack of interventional studies. Conclusion: Specific gaze patterns appear to be related to improved flight performance. Future studies should address the methodological concerns mentioned to better clarify the relationship between gaze behavior and flying performance.
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Introduction: Attention plays a central role in cognitive processing; ineffective attention may induce accidents in flight operations. The objective of the current research was to examine military pilots' attention distributions between chasing a moving target and a stationary target. Method: In the current research, 37 mission-ready F-16 pilots participated. Subjects' eye movements were collected by a portable head-mounted eye-tracker during tactical training in a flight simulator. The scenarios of chasing a moving target (air-to-air) and a stationary target (air-to-surface) consist of three operational phases: searching, aiming, and lock-on to the targets. Results: The findings demonstrated significant differences in pilots' percentage of fixation during the searching phase between air-to-air (M = 37.57, SD = 5.72) and air-to-surface (M = 33.54, SD = 4.68). Fixation duration can indicate pilots' sustained attention to the trajectory of a dynamic target during air combat maneuvers. Aiming at the stationary target resulted in larger pupil size (M = 27,105, SD = 6565), reflecting higher cognitive loading than aiming at the dynamic target (M = 23,864, SD = 8762). Discussion: Pilots' visual behavior is not only closely related to attention distribution, but also significantly associated with task characteristics. Military pilots demonstrated various visual scan patterns for searching and aiming at different types of targets based on the research settings of a flight simulator. The findings will facilitate system designers' understanding of military pilots' cognitive processes during tactical operations. They will assist human-centered interface design to improve pilots' situational awareness. The application of an eye-tracking device integrated with a flight simulator is a feasible and cost-effective intervention to improve the efficiency and safety of tactical training.Li W-C, Yu C-S, Braithwaite G, Greaves M. Pilots' attention distributions between chasing a moving target and a stationary target. Aerosp Med Hum Perform. 2016; 87(12):989-995.
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Introduction This chapter describes two related models of visual attention. SEEV (salience effort expectancy value) predicts how scanning is driven by the four components in the monitoring and control of complex workplaces like the aircraft cockpit, driving cab, or operating room. It distinguishes between ideal scanning, driven just by expectancy and value, and actual scanning, driven also by the attention capture of salient displays, and the inhibiting factor of the effort of scanning long distances. Data validating the model, and a computational example are presented. NSEEV (Noticing SEEV) predicts the latency and accuracy of noticing discrete events within the workplace, in the context of the ongoing scanning predicted by SEEV. Thus SEEV predicts the distribution of fixations at the time when an event to be noticed occurs, and hence the retinal eccentricity of that event. Event eccentricity, along with event salience and expectancy, predict how long it will take the event to be noticed. Validation data from NSEEV are also presented, and practical applications of both models are described. The SEEV Model High in the skies over Brazil, pilots in a small corporate jet failed to notice that a visual indicator in their cockpit had changed status, informing that their plane was no longer broadcasting its position to others in the sky.
Numerous studies in high-risk work settings reveal the great amount of human related causes in accident and incidents occurrences (e.g., Flin, 2008; Helmreich, 2000; Reason, 1990). Moreover, Endsley (1999) points out that about 88% of all accident causes can be traced back to situation awareness (L1 perception [76.3%], L2 comprehension [20.3%], and L3 anticipation [3.4%]). As there is no common and standardized training content on scanning behavior and situation awareness in today’s flight school education in Switzerland, an effective training concept is necessary a) to support pilots in achieving and maintaining appropriate scanning skills; b) to develop personal mechanisms in handling situation awareness and c) to regain situation awareness after an unexpected event (e.g., LOC-I). The following paper aims at demonstrating a new training design for general aviation pilots. The training involves theoretical information about scanning techniques and situation awareness and is combined with practical exercises - either in a flight simulator or in real flight sessions. During the practical training session, the pilot wears an eye tracking device, which provides the flight instructor (trainer) with immediate information on the pilot’s scanning patterns and state of situation awareness.
Background: This study examined whether subjective measurements of in-flight sleep could be a reliable alternative to actigraphic measurements for monitoring pilot fatigue in a large-scale survey. Methods: Pilots (3-pilot crews) completed a 1-page survey on outbound and inbound long-haul flights crossing 1-7 time zones (N = 586 surveys) between 53 city pairs with 1-d layovers. Across each flight, pilots documented flight start and end times, break times, and in-flight sleep duration and quality if they attempted sleep. They also rated their fatigue (Samn-Perelli Crew Status Check) and sleepiness (Karolinska Sleepiness Scale) at top of descent (TOD). Mixed model ANCOVA was used to identify independent factors associated with sleep duration, quality, and TOD measures. Domicile time was used as a surrogate measure of circadian phase. Results: Sleep duration increased by 10.2 min for every 1-h increase in flight duration. Sleep duration and quality varied by break start time, with significantly more sleep obtained during breaks starting between (domicile) 22:00-01:59 and 02:00-05:59 compared to earlier breaks. Pilots were more fatigued and sleepy at TOD on flights arriving between 02:00-05:59 and 06:00-09:59 domicile time compared to other flights. With every 1-h increase in sleep duration, sleepiness ratings at TOD decreased by 0.6 points and fatigue ratings decreased by 0.4 points. Discussion: The present findings are consistent with previous actigraphic studies, suggesting that self-reported sleep duration is a reliable alternative to actigraphic sleep in this type of study, with use of validated measures, sufficiently large sample sizes, and where fatigue risk is expected to be low. van den Berg MJ, Wu LJ, Gander PH. Subjective measurements of in-flight sleep, circadian variation, and their relationship with fatigue. Aerosp Med Hum Perform. 2016; 87(10):869-875.
Fatigue is a major contributing factor to operational errors. Therefore, the validation of objective and sensitive indices to detect fatigue is critical to prevent accidents and catastrophes. Whereas tests based on saccadic velocity (SV) have become popular, their sensitivity in the military is not yet clear, since most research has been conducted in laboratory settings using not fully validated instruments. Field studies remain scarce, especially in extreme conditions such as real flights. Here, we investigated the effects of real, long flights on SV. We assessed five newly commissioned military helicopter pilots during their aviation training. Pilots flew Sikorsky S-76C helicopters, under instrumental flight rules, for more than two hours (ca. 150 min). Eye movements were recorded before and after the flight with an eye tracker using a standard guided-saccade task. We also collected subjective ratings of fatigue. SV significantly decreased from the Pre-Flight to the Post-Flight session in all pilots by around 3% (range: 1-4%). Subjective ratings showed the same tendency. We provide conclusive evidence about the high sensitivity of fatigue tests based on SV in real flight conditions, even in small samples. This result might offer military medical departments a valid and useful biomarker of warfighter physiological state.
Technological advances since the early days of flight have significantly transformed the aircraft cockpit and have altered the relationships among the human pilot, the aircraft, and the environment. Consistent with technological advances in aviation, the role of the pilot has evolved from one characterized by sensory, perceptual, memory, and motor skills to one characterized primarily by cognitive skills. This chapter aims: (1) to trace the technological evolution of the aircraft cockpit and of the flying task; (2) to describe issues inherent in the naturalistic side of the hybrid ecology, the need for correspondence, or accuracy in perception and judgment, in dealing with external environmental factors such as ambiguity and probabilism of cues, and the dangers of errors in correspondence; (3) to describe issues inherent in the electronic side of the hybrid ecology, including the need for analytical and consistent use of information, the need for coherence, or rational use of data and information in dealing with the internal, electronic environment, and the dangers of coherence errors; and (4) to discuss the integration of the two sides of the hybrid ecology and challenges for the design of Next Generation (NextGen) aircraft.
Research has shown no consistent findings about how scanning techniques differ between experienced and inexperienced helicopter pilots depending on mission demands. To explore this question, 33 military pilots performed two different landing maneuvers in a flight simulator. The data included scanning data (eye tracking) as well as performance, workload and a self-assessment of scanning techniques (interviews). Fifty-four percent of scanning-related differences between pilots resulted from the factor combination of expertise and mission demands. A comparison of eye tracking and interview data revealed that pilots were not always clearly aware of their actual scanning techniques. Eye tracking as a feedback tool for pilots offers a new opportunity to substantiate their training as well as research interests within the German Armed Forces.