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Statistical analysis of air traffic controllers’ eyemovements

Yanjun Wang 1,2,∗, Wei Cong 1,2, Bin Dong1,2, Fan Wu1,2, and Minghua Hu1,2

1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China2National Key Laboratory of Air Traffic Flow Management, Nanjing 210016, China

Abstract—Eye movements are important indicators of infor-mation seeking behavior, and provide an insight into informa-tion about interests, goals, plans and cognitive strategies. Theunderstanding of eye movements is thus of great importance tostudy the behaviors of human who are responsible for the safetyand efficiency of a complex system. In air traffic management,much previous research has focused on the investigations onpilots’ eye movements. Little has been done on the study ofcontrollers’ eye movements. Here, we present statistical analysisof controllers’ eye movements that are recorded during real-timesimulations. Specifically, we examine two commonly investigatedoculomotor behaviors, fixation and saccades, to study effect ofworking experience on eye movements. By comparing the statis-tical properties of defined metrics and by applying MultifractalDetrended Fluctuation Analysis method to the time series data,we show that working experience do have notable effects on eyemovements patterns. Both fixation and saccades are differentbetween qualified controllers and novices. Qualified controllerscan use more efficient searching strategies than novices. Thesefindings may help to enhance the quality of controller training.More importantly, they may shed lights on understanding ofmechanisms of information seeking of human when executecomplex tasks.

Index Terms—air traffic control, eye movements, fixation,saccades, information seeking

I. INTRODUCTION

Over the past decades, a considerable amount of researchhas been devoting to the human-operator topics in the man-machine systems, or human-driven complex systems [1, 2].Efforts have been giving either to improve the human perfor-mance estimation and human errors effects on system relia-bility and system effectiveness, or to develop intelligent toolsto support human operation [3, 4, 5]. Despite the advancedtechnologies and operational concepts being applied into airtraffic management (ATM) system, it is recognized that airtraffic controllers are continuing to play critical roles in thesystem [6, 7, 8, 9]. Much previous research has emphasizedthe study of air traffic controller’s communications, cognitiveactivities and mental workload [10, 11, 12]. Little attention hasbeen paid to their eye movements activities. Since ATM systemis in the process of transforming, the roles, responsibilities, andrequirements from both human and automation are changing[9]. For instance, a main task for controller in future system isto monitor automations function properly, and controller can

Corresponding author: ywang@nuaa.edu.cn

quickly resume control if autonomous operation fails or de-grades [13, 9]. Given the unique characteristics of controller’swork, the understanding of controller’s eye movements hasbeen a question of interest in both ATM domain and otherfields.

Eye movements have been the subjects of rapidly growingresearch interest within various disciplines, including psychol-ogy, ergonomics, and computer science etc. In [14], Kowlerpresents a broad review on eye movements, emphasizing onthree oculomotor behaviors, gaze control, smooth pursuit, andsaccades, and their interactions with vision. Research focus-es from human-computer perspective are using eye trackingeither as a means of studying the usability of computerinterface, or as a means of interacting with the computer[15].For example, eye-tracking study was carried out by MITRECAASD to evaluate their newly developed automation con-cept, Relative Position Indicator (RPI)[16]. Recently, a spateof work has suggested that eye movements measures canbe used to predict human performance [17, 18]. Ahlstromand Friedman-Berg performed an investigation to examine thecorrelations between controller’s eye movements and cognitiveworkload. They found that eye movement measures provide amore sensitive measure of workload as observed in numerousbehavioral studies [19]. Meanwhile, eye blink information andsaccadic velocity are reported to be used as an indicator ofarousal levels in naturalistic tasks [20, 21].

An important reason why eye movements have gained somuch attention is that eye movements can provide an insightinto information about interests, goals, plans and cognitivestrategies. An example of using eye movements as a tool toinfer underlying and hidden cognitive strategies was givenin [22]. Jiang et al. proposed a model to predict humanimplicit intention on the basis of salient features of eyemovements, including fixation length, fixation count and pupilsize variation. Although Jiang et al. claimed that the proposedmodel shows plausible performance, there is still a lot workto be done to recognize human intention while performingcomplex tasks.

Investigations on information searching behavior have beenfacilitated in recent years by analyzing eye movements data,as eye movements are the natural indicators of informationsearching by the brain. In the past few years, substantial at-tention has been devoted to the topic of task-directed search forinformation through eye movements [23, 24, 25, 26, 27]. Theresearch on underlying mechanisms of information-seeking

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was highlighted in an important study by Gottlieb et al., whoreviewed research work on this topics from three separatefields, machine learning, eye movements in natural behavior,and psychology and neuroscience [27]. One line of informationseeking research is focusing on the scanning patterns whenhuman tracks multiple targets or solves visual search problems.The Federal Aviation Administration has conducted a researchprogram to investigate air traffic control specialist information-scanning activity [28]. Eye movements data which includesfixations, saccades, blinks, and pupil information were exam-ined, and scanning patterns were identified. Controllers tendto focus on the areas of highest traffic density, whereas suchbehaviors may lead to the late identification of intrusionswhich may result in unsafety events. Similar behavior wasreported in the study by Fehd and Seiffert, who examinedthe eye movements when human trace multiple targets [29].A central location was found to be the focus which couldbe explained by the strategy of grouping targets into a singleobject [29]. van Meeuwen et al. compared the visual problemsolving strategies in three levels of air traffic control exper-tise, namely novice, intermediate, and expert. They used stillpictures of realistic radar situations which represent differentlevels of traffic complexity, and asked controllers to providethe optimal order of arrival of all depicted aircraft. Threeproblem solving strategies were analyzed, which include atten-tion focusing, chunking, and means-end analysis. Recent work,Kang and Landry proposed an algorithm based on MaximumTransition-based Agglomerative Hierarchical Clustering (M-TAHC) to controller’s visual scanning, and aircraft selectionbehaviors[30, 31].

In this article, controllers’ eye movements data are col-lected and analyzed to provide an initial understanding ofrelationships between working experience and eye movementspatterns. Specifically, we want to (i) study the effect ofworking experience on fixation and saccades; (ii) comparethe probability distributions of eye movements metrics; (iii)explore multifractal characteristics of fixation time series. Therest of the paper is organized as following: Sec. II presentsa brief introduction about data collection and experiments.In Sec. III, we examine the correlations between workingexperience and eye movement metrics from several aspects,such as probability distribution, multifractal characteristics etc.The conclusion remarks are presented in Sec. IV.

II. EXPERIMENTS AND DATA

A. Participants

A total of 25 personnel (22 males, 3 females) aged 21-40years (M = 28.4, SD = 20.08) volunteered to participate inthis investigation. Participants consist of 4 students majoring inair traffic control from Nanjing University of Aeronautics andAstronautics (NUAA) and 21 air traffic controllers from AirTraffic Management Bureau (ATMB) of Zhejiang Province.Based on working experience and personal competency, con-trollers are qualified into five classes. As one controller hasnot obtained ATC license, he/she is treated as novice. Table Ipresents the number of participants in each class.

Level-two controllers have worked for at least twelve years(years of work experience: M = 14.0, SD = 3.5). Level-Three controllers have worked for at least eight years (years ofwork experience: M = 9.3, SD = 1.0). Level-Four controllershave worked for at least six years (years of work experience:M = 6.5, SD = 0.6). Level-Five controllers have workedfor at least two years (years of work experience: M = 2.8,SD = 1.1). Novices do not have any working experienceand they are always trained in the simulator (years of workexperience: M = 0.0, SD = 0.0).

B. Equipment

To record controllers’ eye movements, an eye contact-lesstracking system, faceLAB 5.0 was utilized. The faceLAB 5.0provides a very high quality tracking without interfering withthe user environment. Due to the high tracking quality andthe automatic software, faceLAB 5.0 has been widely usedin commercial, clinical and research applications, for analysispurposes.

The following are some of definitions and features that canbe measured by this device:

• Head-Pose: faceLAB can determine the position andorientation of a subject’s head in 3D coordinates, whichis known as the head-pose.

• Gaze: faceLAB outputs separate gaze rays for the leftand right eye. Each gaze ray is made up of an originpoint and a unit vector.

• Saccades: The ballistic movement of the eye from onefixation to the next is defined as a saccade. The fast eyemovement can be up to 700o/s. In faceLAB, a saccadeis defined as a fast motion of the eye to change the gazepoint between fixation points.

• Eye Closure: The eye closure of each individual eye ismeasured as a percentage relating to the coverage of theiris.

• Eye Blinks: The blink measurement in faceLAB is abinary signal (true or false), which reports the occurrenceof blink events. They are defined as a rapid eye closurefollowed quickly by a rapid eye opening.

• Pupillometry: If tracking the eyes using the pupil con-tour, faceLAB is able to measure the diameter of eachpupil. The diameter of the pupil is measured in metres.

• PERCLOS: The PERCLOS fatigue measure is regardedas the most reliable single visible indicator for fatigue. Itis based on the eye closure of the subject, in particularthe percentage of extended periods with practically closedeyes in a time window of fixed size.

During the visual stimulus, the eye tracking system tracksthe human’s eye and evaluates the features such as fixationlength, fixation count, pupil size and visual scan path etc. ofthe participant.

C. Experiments

In order to investigate eye movements in active complextasks, we used radar control simulation systems to perfor-m high-fidelity simulation exercises rather than using still

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TABLE I: The number of controllers in five classes

Level-Two Level-Three Level-Four Level-Five Novices

Male 3 3 3 9 4Female 0 1 1 0 1

pictures. Both the functions and the interface at controller’sposition are exactly the same as their work station in ATMBof Zhejiang, thus participants do not need time to familiar withthe simulation system.

We select Hangzhou Approach Sector 03 (ZSHCAP03)for simulated airspace environment. As shown in Figure 1,the horizontal boundary of ZSHCAP03 is the polygon areasurrounded by the lines: Xuancheng-Pingqiao-Changganglin-Nanxun-P2-Shangyu-Zhangcun-Tonglu-Xuancheng, whilevertical range is QNE 3000m (at or below) ( For QNHarea, the vertical range is at or below 3000m on QNH ).ZSHCAP03 is mainly responsible for sequencing arrivalflights before handing to tower, and for directing departureflights and few overflying flights.

Three levels of traffic scenarios are prepared based on thereal schedule, namely easy, normal, and hard. However, thedifference in traffic scenarios is out of concern of current study.We only analyze the data collected with normal traffic.

The faceLAB cameras are put in the front of controller’skeyboard under the middle of main radar screen. A precisemodel is built for each participant, and calibration is madebefore simulation starts. All participants were required to havenormal utilization of both arms and legs and permitted to weareyeglasses for vision correction, as they were doing normalcontrol job. Brief introduction of the purpose of the study andthe traffic scenario was given before each simulation run. Allthe participants have signed the Informed Consent Statementwhich allows to use eye movements data.

Jianqiao Xiaoshan

Airport

Fig. 1: The structure of simulated airspace. Gray lines aresector boundaries, while black lines are published arrival anddeparture routes to Hangzhou Xiaoshan International Airport.Red arrows indicate major arrival flows, while green arrowsshow departure flows from the airport.

III. RESULTS

A. Fixation identification (I-VT)Although faceLAB can determine fixation from eye move-

ment data, it is recommended to develop or employ algorithmto extract fixation points from the raw data. Here we usethe Velocity-threshold fixation identification (I-VT) algorithm,which is the simplest one and has been widely used in lots ofeye-tracking research [32]. Briefly, I-VT uses angular velocityto distinguish fixation and saccade points. A crucial parameter,the velocity threshold, must be designated in advance, forexample, set velocity threshold to 125o/s. The original timeseries data is sorted according to recorded time. Then, I-VT begins by calculating point-to-point angular velocities foreach point. The following rules will be applied to distinguishfixation and saccade points: If the point’s angular velocityis less than the threshold, it is identified as fixation point;otherwise it is identified as saccadic point. The process thencollapses consecutive fixation points into fixation groups anddiscards saccade points.

Finally, I-VT outputs each fixation group represented by< x, y, t, d >, where x and y are the center of the points ina group , t represents the time of the first point, and d givesthe duration of the fixation group.

An example of fixation trajectory is plotted in Fig. 2. Ascan be seen from the figure, the pattern of eye tracks clearlyfollows the pattern of route structure shown in Fig. 1.

Fig. 2: The fixation trajectory of a controller on the radarscreen. The arrows indicate the direction of movements.

B. Effect of working experience on fixation and saccadesTo study the effect of working experience on fixation, we

first examine the statistical properties of two commonly used

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metrics, Area of Interest (AOI) and fixation duration. Originaldata was divided into five groups based on participants’working experience as described in Sec. II-A. Given the meannumber of AOIs and mean fixation duration plotted in Fig. 3,one can tell that there are significant difference between thefollowing three categories, namely Cat. I: Level-Two, Cat. II:Level-Three, Level-Four, and Level-Five, and Cat. III: Novice.

As the most experienced controllers among all participants,Level-Two controllers have worked in ATC for more than12 years, so they are quite familiar with controlled airspace.However, most of them spend much time on administrativework in the bureau. Normally they work as coordinate con-troller (or data controller), rather than as executive controller(or radar controller). As a result, their eye movements patternis different from the others. In Fig. 3, the average numberof AOIs of Level-Two (M ≈ 1126) is much fewer thanthe other four groups (M ≈ 1521), but the average fixationduration (1.66s) is much longer than the others. Due to longtime away from control work, Level-Two controllers may bereluctant to monitor airspace in all directions, only focus oninterested areas. This result may indicate misallocation ofattention resource.

In contrast, controllers in Cat. II have working experienceof 2-10 years, and they are the main staff who are responsiblefor controlling flights. The average number of AOIs increasesgradually from Level-Five to Level-Three within Cat. II group,whereas the mean fixation duration decreases slightly. It tookabout an average of 1.1 seconds for these three levels ofcontrollers to observe the interesting and necessary flight tar-gets, which suggests that controllers are capable of allocatingattention more reasonably.

Novices are at training stage, and they do not have anypractical experience. As shown in the figure, their eye move-ments pattern is very special. The mean fixation duration ofnovices only lasts 0.2 seconds which is far below the others,although the number of AOIs approximated to the ones in Cat.II groups. This pattern is consistent with realities. Novicesare comparatively new to the airspace environment. Variouscritical factors can influence their cognitive activities, suchas traffic distribution, airspace structure etc. Compared withsenior controllers, their capability of managing traffic is stillunder development. They could not select correct targets andallocate proper attention to flights or points.

The comparisons on the standard deviations of numberof AOIs reveal the effect of working experience on fixationfrom another perspective. Smallest variation is found in Level-Two controllers, while largest fluctuation is found in novices.This interesting finding indicates that seasoned controllers usesimilar information seeking strategies which are more optimalthan the ones used by beginners.

A heat map of fixation can be created from the positions offixation points, showing the probability of fixation in a givenarea. The hot zones, zones with higher density designate wherecontrollers focused their gaze with a higher frequency. InFig. 4 we show the typical heat maps of four controllers withfour levels. The airspace structure can be easily captured bythe hot zones of the images. The location in the center aroundairport is identified to be the focus, suggesting controllers

adopt an optimal viewing position which is in agreement withprevious studies [28, 29]. However, the spatial pattern of eyefixations of novices is different from the others. The fixationpoints of novices are scattered across a large range of airspace,suggesting the inefficiency mechanisms of information search-ing.

0

1000

2000

3000

NoviceLevel-FiveLevel-FourLevel-Three Mea

n nu

mbe

r of A

rea

of In

tere

st(A

OI)

(num

ber) Mean number of Area of Interest(AOI)

Mean Fixation Duration

Level-Two0

3

6

9

Mea

n Fi

xatio

n D

urat

ion

(s)

Fig. 3: Average number of Area of Interest (AOI) and fixationduration. Error bars show standard deviation of associatedgroup data.

(a) Level-Two (b) Level-Three

(c) Level-Four (d) Level-Five

Fig. 4: The heat maps of fixation of four controllers.

To investigate the saccades behavior of controller, we calcu-late the saccadic velocity of every eye movement data (Fig. 5).Saccadic velocities of licensed controllers are quite similar.The mean saccadic velocity of novices is higher than of theother four groups. Saccadic velocity reflects the capability ofcontrollers in response to environment change and reflects theirability to track moving targets. After professional training andpractical work, licensed controllers have gained special ability

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to manage traffic. When facing with various kinds of situation,skilled controllers could quickly focus on the key targets andmonitor the whole situation. Although the average saccadicvelocity of novices is the fastest among the others, this doesn’tmean that novices could search critical targets quickly. On thecontrary, they often select targets randomly and are easier tobe distracted by disturbance due to the fact that they are notfamiliar with control work. All of these would lead to highersaccadic velocity.

Power law distribution has been found in many kinds ofhuman activities, and inter-communication times of controllersare also proved to be heavy-tailed. It is expected that eyemovements behaviors show similar feature. We plot the prob-ability distributions of three eye movements metrics, fixationcount during a gaze, fixation duration and saccadic velocity(see Fig. 6, Fig. 7, and Fig. 8 respectively). As displayedin the figures, distributions of the number of fixation anddistributions of fixation duration are roughly similar, withlicensed controllers’ data exhibiting exponential decay in thebeginning and power-law pattern at the tail, whereas the dataof novices approximated to power-law distributions. Comparedwith the number of fixation and fixation duration, differentlevels of controllers’ saccadic velocity distributions are moreconcentrated. All of data shows evident characteristics ofpower-law feature.

0

200

400

NoviceLevel-FiveLevel-FourLevel-Three

Mea

n sa

ccad

ic v

eloc

ity (

/s)

Mean saccadic velocity

Level-Two

Fig. 5: Means and standard deviations of saccadic velocity.

C. Multifractal characteristics of eye movements data

To further investigate the fundamental properties of eyemovements, we turn to the classical time series analy-sis method, Multifractal detrended fluctuation analysis (MF-DFA).

1) The Multifractal detrended fluctuation analysis (MF-DFA) algorithm: To analyze non-stationary time series, Kan-telhardt et al. developed Multifractal Detrended FluctuationAnalysis (MF-DFA) method which has been widely used invarious fields [33]. Here we briefly depict the five steps ofusing MF-DFA to analyze a given time series X = {xk, k =1, 2, ..., N}.

Fig. 6: Distribution of fixation count.

Fig. 7: Distribution of fixation duration.

Fig. 8: Distribution of saccadic velocity.

Step 1. Define the sequence of summary displacements asfollows:

Y (i) =i∑

k=1

(xk − x), i = 1, 2, ..., N (1)

where

x =1

N

N∑k=1

xk.

Step 2. Divide Y (i) into Ns = int(N/s) non-overlappingsegments of length s. To make sure that no information islost during the process of dividing, the analogous procedure

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is repeated starting from the opposite end. Therefore, we willhave 2Ns segments.

Step 3. For all the s data points in each segment v(v =1, 2, ..., 2Ns), the least-square fitting is employed to a k −order polynomial

yv(i) = a1ik + a2i

k−1 + · · ·+ aki+ ak+1 (2)

Step 4. Calculate the variances F 2(s, v). For each segmentv = 1, 2, ..., Ns, F 2(s, v) is calculated as

F 2(s, v) =1

s

s∑i=1

Y [(v − 1)s+ i]− yv(i)2 (3)

While for segments v = Ns + 1, Ns + 2, ..., 2Ns, F 2(s, v)is calculated as

F 2(s, v) =1

s

s∑i=1

Y [N − (v −Ns)s+ i]− yv(i)2 (4)

Step 5. The fluctuation function Fq(s) for a given realnumber q = 0 is defined as

Fq(s) =

{1

2Ns

2Ns∑v=1

[F 2(s, v)]q/2

}1/q

(5)

If q = 0, Fo(s) is given as

F0(s) = exp

{1

4Ns

2Ns∑v=1

ln[F 2(s, v)]

}∼ sh(0) (6)

Fq(s) is the function of data length s and fractal order q, andFq(s) ∝ sh(q). Here h(q) is called generalized Hurst exponent.When q = 2, F2(s) is the standard Detrended FluctuationAnalysis (DFA). The obtained h(2) can tell the time series Xwhether appear to be long-memory processes or 1/f noise.

• h(2) < 0.5: anti-correlated• h(2) ≈ 0.5: uncorrelated, white noise• h(2) > 0.5: correlated• h(2) ≈ 1: 1/f noise, pink noise• h(2) > 1: non-stationary, random walk like, unbounded• h(2) ≈ 1.5: Brownian noiseThe global scaling exponent τ(q) is defined based on

generalized Hurst exponent h(q)

τ(q) = qh(q)− 1 (7)

Based on Legendre transformation, we have{α = τ ′(q)

f(α) = qα− τ(q)(8)

Here, α is called singularity strength or Holder exponent,and f(α) is the spectrum of singularities which tells thedimension of subset of the series. Given the Equation 7 andEquation 8, it can be obtained

α = h(q) + qh(q) = h(q) + qdh(q)

dq(9)

f(α) = q[α− h(q)] + 1 (10)

2) The MF-DFA results: Figure 9 presents the fluctuationfunctions Fq(s) of scale s for all fixation duration data ofLevel-Three controllers. In Figure 10 we show the relation-ships between h(q) and q. From the plot we notice that thereis a clear trend that q-order h(q) decreases when q increasesfrom -10 to +10. Note that the h(q) of fixation is not aconstant number, rather it changes with the order q, whichindicates that there exists significant fractal characteristics incontrollers’ fixation dynamics. However, the generalized Hurstexponents lie between 0.7 and 0.8 for all levels of controllerswhen q = 2. This result indicates all the fixation data ispositively long-range correlated, and has long-range power-law relations. The fixation data is sensitive to initial condition,showing a classical fractal time series. Long-range correlatedphenomena suggest that fixation activity appears to be long-memory processes meaning that the past activity may influencethe present and future activity. Note that h(q) of novice is lessthan 0.5 when q > 6, suggesting the negative correlations offixation.

101

102

103

104

10−3

10−2

10−1

100

101

102

103

s

Fq(s

)

q=−10q=−5q=−2q=−1q=0q=1q=2q=5q=10

Fig. 9: Fluctuation function Fq(s) vs. scale s for nine valuesof q. Data of controllers with Level-Three was used.

−10 −5 0 5 100.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

q

h(q)

Level 2Level 3Level 4Level 5Novice

Fig. 10: Generalized Hurst exponents h(q) vs. order q for fivelevels of controllers

To compare the multifractal characteristics of fixation indifferent levels of controllers, we plot the multifractal singu-

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larity spectrum in Figure 11. From the multifractal theories,we know that the singularity spectra f(α) at the minimumvalue of α indicates the maximum fluctuation of the system.The smaller α, the more fluctuation exists. It can be seenfrom the figure that novices have the smallest α ≈ 0.37,whereas the others are bigger than 0.48. This result suggeststhere are more fluctuations in novices fixation activities. Therange of singularities (∆α) measuring the difference betweenthe maximum fluctuations and minimum fluctuation of thesystem, can be used to capture the strength of multifractalcharacteristics of the system.

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6−0.2

0

0.2

0.4

0.6

0.8

1

1.2

α

f(α)

Level 2Level 3Level 4Level 5Novice

Fig. 11: The singularity spectrum of different levels of con-trollers

IV. CONCLUSIONS

A. Contributions

Eye movements can be used as a clue to what human isperceiving, where the attention is allocated. Here, we presentour study on air traffic controllers’ eye movements, empha-sizing two oculomotor activities: fixation/gaze and saccades.Special attention is given on the effect of working experienceon eye movements, aiming to uncover the underlying strategiesthrough the analysis of statistical patterns of eye movementsamong controllers. Rather than using still pictures, we collecteye movements data during real-time simulations. Based onI-VT algorithm, fixation points are extracted from the rawdata. Eye movements metrics such as number of AOIs, fixationdurations, and saccadic velocity have been carefully analyzed.It is found that a qualified controller uses an efficient searchstrategy by fixations on core targets. A further study is neededto enhance the visual search behavior of trainees in air trafficcontrol. Probability distributions of eye movement metricsexhibit heavy-tailed feature. Detailed distribution shapes stillcan help us discriminate novices and experienced controllers.Finally, we use MF-DFA method to confirm that fixation timeseries has multifractal characteristics. Multifractal singularityspectrum demonstrates that there are more fluctuations innovices fixation activities.

In conclusion, working experience has a notable effect oncontrollers’ eye movement patterns. Area of Interest, fixationduration and saccadic velocity are demonstrated to be the

reasonable indicators to judge whether a controller undertraining is qualified.

B. Limitations and future research

One advantage of using faceLAB to record eye movementsdata is that controllers are not required to wear any equipmentwhich allows them to control traffic as usual. However, theirconventional working environment is changed because of theequipment being set up between them and radar screen. Sucharrangements can cause physiological stress which may affectcontroller’s activities. Out of safety concerns, human-in-the-loop simulations are carried out to collect eye movementsdata of controllers rather than collecting data during fieldoperations. Although our simulation facilities provide a high-fidelity simulated environment, there are still drawbacks thatmight not be avoided: (i) Unlike field operations, controllerscannot feel personally on the scene during exercises with thesimulators. For controllers, simulation means training whichallows mistakes. Therefore, they will be more relaxed duringsimulation than daily work. The environmental and phycolog-ical differences might result in the changes in their behavior;(ii) In our simulations, controller is focusing on the controlledsectors. Handing in/out flights is not required to monitor trafficin adjacent sectors or to coordinate with other controllers.However, in filed operations, controller has to keep an eye onthe traffic situations in adjacent sectors, and coordinates withother controllers about traffic constraints. And that means alot of work. To avoid, a large-scale simulation consisting ofseveral sectors is suggested.

Given the importance of information seeking for air trafficcontrollers, and the unique characteristics of their work, furtherstudies on eye movements from various perspectives aresuggested. The joint research carried out by psychologists,computer engineers, data scientists, and other researchersfrom related fields, will advance our understanding of humancomplex systems.

V. ACKNOWLEDGEMENTS

We thank air traffic controllers from Air Traffic Manage-ment Bureau (ATMB) of Zhejiang Province, and studentsfrom Nanjing University of Aeronautics and Astronautics forparticipating in the experiments. We thank Yuan Wang andYinxin Liu for help with collecting eye-tracking data. Thisresearch was partially supported by the National Natural Sci-ence Foundation of China (grant number: 61304190), the Fun-damental Research Funds for the Central Universities (grantnumber: NJ20150030), and by the Natural Science Foundationof Jiangsu Province of China (grant number: BK20130818)

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