ATC labadvanced an air traffic control s

() © 2009 The Psychonomic Society, Inc 118 Air traffic control (ATC) simulations are frequently used for both applied and basic research There is a growing need for ATC simulations, to identify factors that influence the workload and performance of air traffic controllers (Athenes, Averty, Puechmorel, Delahaye, Collet, 2002; Lamoureux, 1999) and to build theories of the representa tions and processes that underlie performance on specific control tasks (Gronlund, Ohrt, Dougherty, Perry, Man n.

© 2009 The Psychonomic Society, Inc 118

Air traffic control (ATC) simulations are frequently used

for both applied and basic research There is a growing

need for ATC simulations, to identify factors that influence

the workload and performance of air traffic controllers

(Athenes, Averty, Puechmorel, Delahaye, & Collet, 2002;

Lamoureux, 1999) and to build theories of the

representa-tions and processes that underlie performance on specific

control tasks (Gronlund, Ohrt, Dougherty, Perry, &

Man-ning, 1998; Rantanen & Nunes, 2005) In addition, ATC

simulations are frequently used to address more basic

is-sues of human cognition, such as the associative learning

mechanisms involved in relative judgment (Loft, Neal, &

Humphreys, 2007), the processes that underlie memory in

the performance of intended actions (Stone, Dismukes, &

Remington, 2001), the effects of time pressure on

process-ing load (Hendy, Liao, & Milgram, 1997), and individual

differences in complex skill acquisition (Ackerman, 1992)

Consequently, ATC simulations are effective tools for

eval-uating the generalizability of broader theories about basic

cognitive processes and capacities, thus explaining human

performance more generally In this article, we describe a

new ATC simulation package called ATC-labAdvanced that

can be used for both applied and basic research In doing

so, we highlight the improvements it offers over currently

available ATC simulators

Existing ATC simulators have typically been developed

so as to have the level of realism and experimental control

required to investigate specific research questions

Real-ism refers to the extent to which experiences encountered

in the simulation occur in the field of interest (DiFonzo, Hantula, & Bordia, 1998; Ehret, Gray, & Kirschenbaum,

2000) Experimental control refers to the degree to which

a simulation can provide control over variables and thus support the conclusion that the effects obtained are due to experimental manipulations (Boring, 1954; Brehmer & Dorner, 1993) To maximize efficiency, existing simula-tors have typically been designed to compromise between the extent to which they can mimic field experience (real-ism) and the experimental control that they can provide High-fidelity ATC simulators typically have high realism but lack experimental control Medium-/low-fidelity sim-ulators can provide this control but often lack realism This trade-off between realism and experimental con-trol presents a problem when both are required For ex-ample, many research groups are developing theories and models designed to predict controller performance in field settings (for a review, see Loft, Sanderson, Neal, & Mooij, 2007) For this type of research, it is crucial to use simu-lations that are representative of the environmental con-text in which experts make decisions (Brunswick, 1956; Simon, 1956) At the same time, experimental control

is required in order to isolate the effects of independent variables on specific ATC control tasks In contrast, the purpose of more basic research may be to test a specific

simulator with realism and control

S ELINA F OTHERGILL

University of Queensland, Brisbane, Queensland, Australia

S HAYNE L OFT

University of Western Australia, Perth, Western Australia, Australia

AND

A NDREW N EAL

University of Queensland, Brisbane, Queensland, Australia

ATC-lab Advanced is a new, publicly available air traffic control (ATC) simulation package that provides both

realism and experimental control ATC-lab Advanced simulations are realistic to the extent that the display features

(including aircraft performance) and the manner in which participants interact with the system are similar to those

used in an operational environment Experimental control allows researchers to standardize air traffic scenarios,

control levels of realism, and isolate specific ATC tasks Importantly, ATC-lab Advanced also provides the

program-ming control required to cost effectively adapt simulations to serve different research purposes without the need

for technical support In addition, ATC-lab Advanced includes a package for training participants and mathematical

spreadsheets for designing air traffic events Preliminary studies have demonstrated that ATC-lab Advanced is a

flex-ible tool for applied and basic research.

2009, 41 (1), 118-127

doi:10.3758/BRM.41.1.118

S Fothergill, selina@psy.uq.edu.au

labAdvanced display resembled the ATC operational envi-ronment in as many ways as possible (Schiff, Arnone, & Cross, 1994) To achieve this, the ATC-labAdvanced display was based on the Australian Air Traffic Management Sys-tem and was developed in close collaboration with subject matter experts

Figure 1 illustrates a generic example of the display used in a high-fidelity ATC-labAdvanced simulation The

sector that the participant controls (the active sector) is

made up of a series of flight paths, waypoints, and airports presented against a light gray background The surround-ing darker gray background represents adjacent and ap-proach sectors (sectors that contain airports) Small green circles symbolize aircraft track symbols, and each aircraft has a data block label that displays the call sign, aircraft type, ground speed, current flight level, and cleared flight level These aircraft track symbols and data blocks can be fully customized ATC-labAdvanced uses nautical miles for distance, knots for ground speed, and feet for altitude Every 5 sec, each aircraft’s position and data block label information is updated Aircraft enter the active sector on inbound flight paths from adjacent sectors or take off from airports in approach sectors They then proceed as denoted

in their flight plan through the series of waypoints and either land at an airport or exit to adjacent sectors on outbound flight paths Aircraft that cruise at flight levels below or above the sector flight level boundary of the active sec-tor (over flights) can also be simulated Importantly, ATC-labAdvanced simulates aircraft performance data (e.g., climb and descent rate, speed rate) accurately for commercial jets, turbo propeller aircraft, and military aircraft As a re-sult, aircraft can transit through sectors in a manner similar

to that for an ATC operational environment

The notification system used to denote transitions in aircraft states can be closely based on ATC operational environments That is, the attributes (e.g., colors, flash-ing) of aircraft track symbols and data block labels can be set to represent different phases of flight, which change dynamically as aircraft move though sectors For exam-ple, an aircraft approaching an active sector from an ad-jacent sector may be set to turn from black to blue when

it reaches a certain distance (e.g., 10 nm) from the active sector As the aircraft travels closer to the active sector, it may be set to flash orange until the controller officially

“accepts” the aircraft, using a specific sequence of ac-tions, at which point it would turn green to denote that it

is under the jurisdiction of that controller When the air-craft is handed off to the adjacent sector or approaches the airport, it would turn black to indicate that it is no longer under the jurisdiction of that controller

Response-system realism The second important

re-quirement for achieving realism was to ensure that partici-pants performing control tasks would be able to interact with the ATC-labAdvanced system as similarly as possible

to how controllers would interact with ATC systems in the field (Schiff et al.,1994) ATC-labAdvanced can be custom-ized to provide simulations of the major control tasks pre-viously identified in cognitive task analyses of ATC (Cox, 1994; Rodgers & Drechsler, 1993) These control tasks

theoretical issue that is prevalent in a range of applied

set-tings in which individuals monitor dynamic multi-item

displays (e.g., military command, radar system operators)

In these circumstances, it may be desirable to have low

correspondence (cf Gray, 2002) between the simulation

and the operational environment, so that the research can

be generalized to other systems (Berkowitz &

Donner-stein, 1982; Mook, 1983) In other circumstances, ATC

simulations may be conducted to assess the effectiveness

of controller team performance or training programs, and

increased experimental control would add little to

improv-ing the outcomes of the research

This highlights a need for an ATC simulation package

in which realism and control can be systematically varied

according to the research question(s) under investigation

In the present article, we present a new ATC simulator

called ATC-labAdvanced that provides this Importantly,

ATC-labAdvanced also provides the programming control

required for researchers to customize the exact levels of

realism and control they require in their simulations The

aim of the present article is to introduce ATC-labAdvanced

and indicate how it can be used for research First, we will

detail the features of ATC-labAdvanced that provide realism,

experimental control, and programming control These

features will then be compared with those of existing

simulators We will then provide examples of applied and

basic research programs that have used ATC-lab Advanced

Next, we will outline the training package available to

fa-miliarize participants with ATC-labAdvanced simulations

Finally, data logging features and system requirements

will be provided

Realism in ATC-lab Advanced

The primary duties for air traffic controllers are to

en-force separation standards between aircraft and ensure that

aircraft reach their destinations in an orderly and

expedi-tious manner One of the more common separation

stan-dards set by the International Civil Aviation Organization

(ICAO) is that aircraft are required to maintain either a

1,000-ft vertical separation or 5 nautical miles horizontal

separation from all other aircraft Consequently, a pair of

aircraft is considered to be in conflict if they will, given

their current speeds, flight levels (altitudes), and bearings,

simultaneously violate vertical and horizontal separation

standards in the future Controllers are required to perform

a range of control activities to ensure the safe and efficient

flow of aircraft When logical, practical, or logistical

con-siderations constrain field experimentation or observation

in an applied work context (DiFonzo et al., 1998; Gray,

2002), high-fidelity simulators can be used to simulate

these tasks Examples include the FAA Academy Training

Simulator (Jones & Endsley, 2000), TRACON

(Acker-man, 1992), the EUROCONTROL Simulation Capability

and Platform for Experimentation, ATCoach (UFA Inc.,

n.d.), and FIRSTplus (Raytheon, 2005) ATC-labAdvanced

simulations can also be designed so that participants

per-form tasks in a manner similar to field controllers

Display realism The first requirement for achieving

realism was to ensure that the components of the

ATC-level by a certain distance) Flight ATC-levels or speeds can be altered by clicking on the data block label where these val-ues are displayed and then choosing new valval-ues from drop-down menus An example of how to change a flight level is illustrated in Figure 2 Similarly, heading changes can be chosen from drop-down menus Headings of aircraft can

be changed by selecting a predetermined heading func-tion on the keyboard, clicking on the aircraft, and dragging

a line to a new destination point Level requirements can

be issued by pressing designated keys and entering into text boxes the distances by which aircraft are required to reach certain flight levels Participants can accept aircraft

by pressing designated keys and clicking on aircraft track

include accepting and handing off aircraft from adjacent

sectors or airports, assigning boundary and cruise

alti-tudes, monitoring air traffic to detect potential conflicts,

resolving conflicts, and traffic sequencing

The intervention methods participants use to modify

air-craft trajectories in ATC-labAdvanced, the way participants

accept and hand off aircraft, and how they use prediction

tools were designed on the basis of structured interviews

with controllers (Fothergill & Neal, 2005, 2006) and

analy-ses of the ATC literature (Callantine, 2002; Späth &

Ey-ferth, 2001) Examples of aircraft intervention methods

include changing flight levels, speeds, or headings and

as-signing flight-level requirements (e.g., reaching a flight

Figure 1 A generic example of a display used in an ATC-lab Advanced simulation The screen shot displays one active and six (four adjacent, two approach) nonactive sectors, various route structures,

and aircraft in their different phases of flight All the aircraft have probe minute vectors to indicate

their position in 1 min’s time; the route for SIA16 is displayed; a scale marker is available in the

top left corner; and a bearing and range line has been attached to VOZ555 To resolve the potential

conflict between VOZ555 and VOZ892, VOZ555 is being vectored away from its planned route The

clock is paused, and the mode display shows that a vector solution is being used

Figure 2 Changing the cleared flight level of an aircraft By clicking on the current cleared flight level, a new cleared flight level can be selected from the menu The new level will be displayed in the aircraft’s label in the next 5-sec update.

generally have low display realism and low system re-sponse realism For example, these simulators do not all have the capability to simulate changes in aircraft altitude,

do not allow participants to interact with the ATC system

in order to modify aircraft trajectory only in limited ways, and do not provide access to prediction tools

This lack of realism does not present a problem when the intent of the research is to test a theoretical idea by mapping the functional relations between variables in a simulation, rather than generalizing to a specific domain ATC-lab, for example, has been successfully used to de-velop general theories (Loft, Humphreys, & Neal, 2004; Loft, Neal, & Humphreys, 2007; Yeo & Neal, 2004) and computational models (Kwantes, Neal, & Loft, 2004) of the processes by which individuals make decisions about the movement of objects on radar displays However, the lack of realism is problematic when one is building theo-ries and models of performance that apply directly to ATC operations (Kopardekar & Magyarits, 2003; Laudeman, Shelden, Branstrom, & Brasil, 1998), since a lack of real-ism poses a substantial threat to the external validity of results For example, a researcher may be interested in examining the processes underlying ATC conflict detec-tion Here, it would be essential that aircraft performance

is accurately simulated so that aircraft transit through sec-tors as they would in the field Controllers must also have access to their regular prediction tools, so they are able to make aircraft trajectory predictions in a way that is similar

to how they would make them in the field

There are many high-fidelity ATC simulators that can provide levels of display realism and response system re-alism that are similar to (or better than) those in ATC-labAdvanced These include but are not limited to the FAA Academy Training Simulator (Jones & Endsley, 2000), the EUROCONTROL Simulation Capability and Platform for Experimentation, FIRSTplus (Raytheon, 2005), and the Total Airport and Airspace Modeler (TAAM) (Jeppesen, 2007) For example, TAAM runs real gate-to-gate traf-fic extracted from the Australian Air Traftraf-fic Management System, and FIRSTplus replicates all the features of mod-ern ATC radar situation displays and can even emulate future operational ATC display types However, as will

be discussed in the sections below, many of these fidelity simulators are not made freely available for re-search, nor do they necessarily provide experimental con-trol or programming concon-trol

Experimental and Programming Control in ATC-lab Advanced

ATC-labAdvanced provides the experimental control re-quired to make definitive conclusions regarding the ef-fects of independent variables on dependent variables Standardized air traffic scenarios can be presented that control extraneous variables and separate

confound-ing variables Programmconfound-ing control refers to the extent

to which the researcher can control what is presented in simulations ATC-labAdvanced provides high programming control over a wide range of task features These task fea-tures include display realism, response system realism, trial presentation, and presentation of rating scales This

symbols Similar to ATC operational environments,

hand-offs can be designed to occur automatically at a set

dis-tance (e.g., 5 nm) beyond the sector boundary

Prediction tools in ATC-labAdvanced include scale

mark-ers, bearing and range lines, probe vectors, route displays,

and history dots These tools are regularly used by

control-lers in the field Scale markers are moved around the screen

to measure distance Bearing and range lines indicate

dis-tance (in nautical miles), bearing (in degrees), and the time

(in minutes) to a future waypoint or another aircraft An

example of how to use the bearing and range line function is

illustrated in Figure 3 Route displays indicate the planned

routes of aircraft, punctuated by the times at which the

air-craft are predicted to reach waypoints, on the basis of their

current nominal trajectory History dots are displayed

be-hind aircraft and represent the routes that aircraft have

trav-eled Probe vectors display the predicted position of aircraft

(in a specified number of minutes) in the horizontal plane,

on the basis of their current nominal trajectory

Realism: Comparison with existing ATC

simu-lators A significant limitation of existing low- and

medium- fidelity ATC simulators is that they lack display

realism and response system realism One prototypical

example is our medium-/low-fidelity predecessor to

ATC-labAdvanced, which we called ATC-lab (Loft, Hill, Neal,

Humphreys, & Yeo, 2004) ATC-lab simulations are

real-istic for participants to the extent that they involve and

af-fect participants and to the extent that participants take the

simulations seriously (DiFonzo et al., 1998) However, a

major limitation of ATC-lab is that it simulates very

selec-tive aspects of ATC ATC-lab has low display realism

be-cause it does not simulate features such as aircraft altitude,

does not use real aircraft performance profiles, does not

present adjacent/approach sectors, and does not provide

any notification system for denoting aircraft transition

states In addition, ATC-lab has low response system

real-ism because it simulates very few control tasks (conflict

detection/resolution only), provides a very limited number

of intervention methods for modifying aircraft trajectory

(speed change only), and provides no prediction tools The

medium-fidelity ATC simulators used by Metzger and

Parasuraman (2001) and Remington, Johnston, Ruthruff,

Gold, and Romera (2000; also see Stone et al., 2001) also

Figure 3 The bearing and range line tool This shows the

dis-tance between the aircraft and the selected end point (in nautical

miles), the bearing (in degrees), and the time that it would take

the aircraft to reach the selected end point (in minutes) based on

its indicated speed.

researchers were required to wait for the developer (for

up to 10 min) to generate starting x- and y-coordinates

Second, it did not allow the calculation of vertical dis-tance, which is essential for ATC-labAdvanced With the mathematical spreadsheets, researchers enter the desired spatial and temporal characteristics of aircraft events, and hard-coded formulae contained in these spreadsheets

pro-vide starting x- and y-coordinates for aircraft in the lateral

plane These spreadsheets are accompanied by a report

documenting the underlying formulae A scenario tester

is also included in the ATC-labAdvanced simulation pack-age, which enables researchers to view (at a faster speed) the air traffic scenarios that are being developed

Programming control over task features Due to

high levels of display realism and system response re-alism, ATC-labAdvanced simulates a much wider array of potential task features than do many existing simulators Furthermore, a significant advantage of ATC-labAdvanced

is that the XML scripting language and code base archi-tecture provide the researcher with programming control over task features First, researchers can control the real-ism of the display, which includes specifying the type of sector (e.g., approach, en route, tower), active and inactive sectors, route structures, position of waypoints, position

of airports, and weather patterns Trials can be constructed

so that different sector maps with different traffic patterns can be presented within the same experiment Researchers can control settings of the aircraft transition notification system, such as the specific color used to denote aircraft transitional states and the positions in sectors where air-craft automatically begin climbing or descending Airair-craft performance can also be modified Second, researchers can control response system realism features, such as the type of prediction tools available to participants and the manner in which they are used, the type of methods that participants can use to modify aircraft trajectory, and the timing/content of instructions and questionnaire items (e.g., workload ratings, motivation ratings) Third,

re-programming control of ATC-labAdvanced is an important

advance, since it allows simulations to be adapted quickly

and cost effectively to serve different research purposes

without the need for technical support

Standardized air traffic scenarios ATC-labAdvanced

experimental scripts are used to specify aircraft events

that occur during experimental trials An example is

illus-trated in Figure 4 These scripts are written using the

Ex-tensible Markup Language (XML) Version 1.0 This is a

free-to-use general purpose markup language, which can

be used as a generic framework for storing any amount

of text or any data whose structure can be represented as

a tree In contrast to the text files used in ATC-lab, XML

scripts can be screened for errors before they are used in

experiments Aircraft details specified in the scripts

in-clude call sign, type, minimum and maximum speed and

flight level, current speed, current flight level, starting

x- and y- coordinates, planned route, position (if any) for

automatic start of climb or descent, and climb and descent

rate The values for aircraft call sign, aircraft type, ground

speed, current flight level (altitude), and cleared flight

level are derived from these scripts and are displayed on

aircraft data blocks When participants intervene during

trials, these values are updated

ATC-labAdvanced provides a set of mathematical

spread-sheets to control the spatial (e.g., minimum separation,

angle of convergence) and temporal (e.g., time to

mini-mum separation) characteristics of aircraft events These

spreadsheets were developed to replace the script

devel-oper provided in the ATC-lab simulation package (Loft,

Hill, et al., 2004) The script developer represented a

substantial improvement over existing medium- and

fidelity simulators because it improved the degree to which

air traffic scenarios could be standardized (see Loft, Hill,

et al., 2004, for a detailed description), and eliminated

the need for manual calculation or trial-by- error

script-ing However, the ATC-lab script developer had two major

limitations First, it was time consuming to use because

Figure 4 Specifications for an aircraft using XML scripting language The aircraft’s type, call sign, starting altitude, starting velocity, starting coordinates, cleared flight level, and flight path are scripted.

In the next section of this article, we will provide ex-amples of applied and basic research programs in which ATC-labAdvanced simulations have been used The degree

of realism and control used in the three research programs were specifically tailored to the research question(s) under investigation, demonstrating the flexibility of ATC-labAdvanced as a tool for cognition research

Illustrative Examples of ATC-lab Advanced Simulations

The three main studies that have used ATC-labAdvanced simulations to date are summarized in Table 1 Fothergill and Neal (2008) used ATC-labAdvanced to examine the ef-fect of workload on the selection of conflict resolution strategies Participating controllers managed traffic in their sector and resolved potential conflicts as efficiently

as possible The purpose was to inform the development

of a computational model that could simulate how con-trollers resolve conflicts in the field (Bolland, Fothergill,

& Humphreys, 2007) The key finding was that control-lers were less likely to implement optimal conflict resolu-tion strategies under a high workload than under a low workload, but only in situations in which these strategies were more difficult to calculate (see Table 1) To obtain applicable results, the simulations were required to be representative of ATC, especially in terms of (1) aircraft performance, (2) sector structure, (3) aircraft transition notification, (4) controller intervention methods, and (5) prediction tool use In order to systematically manipu-late independent variables, a high degree of experimental control was also required to vary configurations of air traffic For example, high-workload scenarios contained configurations that produced more tasks (e.g., conflicts, acceptances and handoffs, aircraft sequencing) than did lower workload scenarios

A recent issue raised in the experimental literature con-cerns how to capture expert performance across different task domains (Ericsson & Williams, 2007) Loft, Bol-land, and Humphreys (2007) recently developed a theory

of expertise for ATC conflict detection ATC-labAdvanced simulations were then used to test a series of predictions from this theory that concerned the factors that affect the likelihood of controllers intervening to ensure separation between aircraft In addition, data were used to test the development of a computational model that simulates how controllers detect conflicts in the field (Loft, Bol-land, & Humphreys, 2007) Thus, it was essential for ATC- labAdvanced to simulate the environmental context in which controllers make conflict detection decisions In particular, it was critical that controllers have access to their regular prediction tools, such as range and bearing lines, in order to ensure that they acquire aircraft trajec-tory information in a realistic manner

However, in contrast to Fothergill and Neal (2008), ATC-labAdvanced was programmed in such a way that controllers performed only conflict detection By using the program-ming control available in ATC-labAdvanced to remove other ATC control tasks, Loft, Bolland, and Humphreys (2007) isolated conflict detection by eliminating visual search requirements and competing demands on attention (see

searchers can control general features, such as the order of

presentation of trials, the timing and length of task breaks,

and when scenarios are paused

Control: Comparison with existing ATC

simula-tors There are a handful of ATC simulators that provide

some level of experimental control For example, both

ATC-lab (Loft, Hill, et al., 2004) and TRACON

(Acker-man, 1992) can present standardized air traffic scenarios

However, in comparison with ATC-labAdvanced, they

pro-vide little programming control Ackerman noted that in

order to adapt TRACON to the study of skill acquisition,

the features of TRACON simulations needed to be

con-siderably modified, which resulted in high programming

costs This is the case with the original ATC-lab (Loft,

Hill, et al., 2004) as well As a result, researchers using

simulators such as ATC-lab or TRACON would need to

hire a technical specialist to implement changes to

simu-lation features In addition, as was discussed previously,

many of these simulators have low realism

Despite high realism, a significant limitation of many

existing high-fidelity ATC simulators (such as the FAA

Academy Training Simulator) is that they lack the

experi-mental control required to make definitive conclusions

re-garding the effects of independent variables on dependent

variables (see Loft, Hill, et al., 2004) Furthermore, many

of these simulators and other high-fidelity simulators that

do provide better experimental control are not made freely

available for research (e.g., EUROCONTROL Simulation

Capability and Platform for Experimentation; ATCoach)

There are at least two ways in which experimental

con-trol is restricted in some high-fidelity simulators First,

although general task conditions, such as the number of

aircraft, type and mix of aircraft, and flight paths, can be

controlled, little control is provided over the spatial and

temporal properties of aircraft events A lack of

standard-ization in air traffic scenarios makes it difficult to control

extraneous variables or to separate confounding variables

This can present a problem, such as when the effects of

task demands on the time taken to complete specific

con-trol tasks are assessed Task demands may include average

distance between aircraft, number of aircraft in altitude

transition, and number of potential conflicts Without

control, researchers would be forced to extract values for

task demands from historic flight data in ATC

simula-tions and correlate those values with performance on a

post hoc basis (e.g., Laudeman et al., 1998) This method

would make it difficult to determine how unique factors

and combinations of factors influence performance

dif-ferentially (Loft, Sanderson, et al., 2007)

Second, the programming architecture underlying

many existing high-fidelity simulators is typically based

on an all-or-none philosophy, in that it does not provide

substantial experimental control over what is displayed

(e.g., altitude, maps), what specific ATC control tasks are

conducted (e.g., accepting aircraft, conflict detection), or

the manner in which participants interact with the ATC

system (e.g., intervention methods, prediction tools) A

consequence of this is that it is difficult to test predictions

about processing mechanisms underlying performance on

control tasks or to test specific theoretical questions

margins around the projected trajectory of aircraft as a function of experience could closely predict these inter-vention decisions (see Table 1)

In addition to these applied research programs, ATC-labAdvanced has been used when more basic research has

been conducted Prospective memory refers to

remember-ing to perform an action in the future and is traditionally studied using verbal task paradigms (Einstein & McDan-iel, 1990) In the real world, highly practiced tasks make

up much of the work of experts, meaning that in order

to execute intentions, people must remember to deviate from routine (Dismukes, 2008) In addition, prospec-tive memory demands often occur in visuospatial, rather than verbal, contexts Exploring prospective memory in the context of routine visuospatial tasks is thus of both

Remington et al., 2000) Experimental control was also

required in order to systematically vary factors such as

(1) the minimum separation of aircraft pairs, (2) the angles

of intersection, and (3) the times to minimum separation

For example, for vertical problems, one aircraft was

cruis-ing and the other climbcruis-ing, with lateral separation set at

0 nm On the basis of current speeds and climb rates, the

vertical separation distance when the aircraft violated

lat-eral separation (5 nm) varied from 0 ft to 4,000 ft As

is illustrated in Figure 5, one of the key findings was that

the probability of controller intervention decreased with

increases in the minimum lateral separation of the aircraft

Furthermore, expert controllers were significantly more

likely to intervene than were trainees A computational

model that assumed that controllers place different safety

Table 1 Summary of the Three Main Studies That Have Used ATC-lab Advanced Simulations

Fothergill

& Neal

(2008)

16 endorsed air

traffic controllers

1 What is the effect of workload on conflict reso-lution decisions?

2 Can we computation-ally model conflict resolution heuristics as a function of workload?

1 Workload level

of scenario (high

vs low)

2 Difficulty of calculating the op-timal solution (dif-ficult vs easy)

1 Conflict resolu-tion strategy

1 When the optimal solution was difficult to calculate, controllers were less likely

to select the optimal solution under high workload than under low workload *

2 When the optimal solution was easy to calculate, control-lers were likely to select the optimal solution under both levels of workload *

3 These results can be incor-porated into the development

of a computational model that simulates how controllers re-solve conflicts in the field Loft, Bolland,

& Humphreys

(2007)

13 endorsed air

traffic controllers

and 7 trainee

con-trollers (1 year

training)

1 What aircraft geometry factors affect the proba-bility that controllers will intervene to ensure sepa-ration between aircraft?

1 Distance of minimum lat-eral separation (0 nm–20 nm)

2 Controller ex-perience (experts

vs trainees)

1 Probability

of controller intervention

1 Controllers were more likely to intervene with in-creases in minimum lateral separation.

2 Experts were more likely to intervene than trainees.

3 A computational model that assumes controllers place safety margins around the projected trajectory of aircraft can account for both expert and trainee intervention decisions.

2 Will intervention deci-sions differ as a function

of controller experience?

3 Can the psychologi-cal processes underlying these intervention deci-sions be captured by a computational model?

Loft, Campbell,

& Remington

(2008)

32

undergradu-ate psychology

students

1 Will participants find it more difficult to remem-ber to deviate from strong routines, as compared with weak routines?

Routine strength 1 Probability of

performing a rou-tine action instead

of an intended action

1 Participants were more likely to forget to deviate from strong routines, as com-pared with weak routines.

2 No effect of routine strength on ongoing task performance.

2 Will ongoing task performance decrease when participants have to remember to deviate from strong routines, as com-pared with weak routines?

2 Ongoing task performance;

aircraft accep-tance and conflict detection

* Since the dependent variable in this study was qualitative (solution type), categorical difference tests (McNemar tests) were used to determine whether participants switched their conflict resolution strategy preferences under different levels of workload and as a function of the difficulty of calculating the optimal solution

ules to the training program The amount of emphasis on each training module will depend on the realism of the simulation and the expertise of the participants (e.g., con-trollers, university students) The first module provides

a general overview of the task The second module de-scribes the human–machine interface, which includes the general display, maps, and aircraft flight strips For the third module, participants are instructed on and practice how to use the prediction tools For the fourth module, participants are instructed on and practice how to accept and hand off aircraft, how to assign cruise or boundary levels, and where the top of descent points are on sector maps The fifth module instructs participants on how to answer questions that may appear during the experiment For the sixth and final training module, participants are instructed on and practice how to intervene to modify aircraft trajectories The duration of the ATC-labAdvanced training is approximately 30 min, although there is some variance with respect to how long it takes participants to familiarize themselves with the intervention methods and prediction tools

The contents of data log files recorded at the end of ex-perimental sessions vary according to the type of experi-ment Nevertheless, these files generally collect two types

of data The first type consists of the details of the air traf-fic scenarios that were presented on each trial, including the type, timings, and durations of aircraft events Partici-pants’ actions are the second source of data These actions include the timing of interventions to aircraft trajectories, subjective ratings, timing of aircraft acceptances and handoffs, and the timing and type of prediction tool use ATC-labAdvanced also records all mouse movements made

by participants in x-, y-coordinates, allowing researchers

to make inferences regarding participant attention Log files generated for each participant can be imported into statistical packages such as Microsoft Excel and SPSS ATC-labAdvanced was written using Qt Widget Library, owned by Troltech Microsoft Visual C6 compiler was used to build the source code ATC-labAdvanced can be run

on desktops or laptop computers that run Microsoft Win-dows No additional software or hardware is required The program will update and display each aircraft’s position, speed, and level in the sector once every 5 sec, on the basis of the aircraft’s current speed, average climb/descent rates, and heading These values are preset in a simulation

script that specifies the series of x-, y-coordinates through

which the aircraft will travel at various flight levels and speeds In simulations in which participants are asked to resolve potential conflicts and assign boundary and cruise altitudes, participants may change these parameters dur-ing a trial

Conclusions

ATC simulators are frequently used in a variety of ap-plied and basic research programs Existing ATC simula-tors typically compromise between the extent to which they can mimic field experience (realism) and the experi-mental control that they can provide In addition, very few ATC simulators are made publicly available to

research-practical and theoretical importance, and ATC-labAdvanced

provides a useful platform for conducting such

investiga-tions Loft, Campbell, and Remington (2008) used

ATC-labAdvanced to investigate individuals’ ability to remember

to deviate from routine Participants accepted aircraft into

their sector and intervened to prevent the occurrence of

conflicts by changing the flight levels of aircraft Routine

strength was manipulated by varying the number of times

the participants performed a specific sequence of actions

when accepting aircraft At test, prospective memory

in-structions asked the participants to substitute a different

key for the standard key when accepting aircraft The

par-ticipants were more likely to forget to deviate from their

strong routines (M  17), as compared with weak ones

(M  08) Although experimental control was required

to present standardized air traffic scenarios, the realism

of the simulation was minimized in order to allow

partici-pating first-year psychology students to quickly become

highly practiced on a small number of ATC control tasks

ATC-labAdvanced also has the potential to be more broadly

used in basic and applied experimental research contexts

For example, we are currently using ATC-labAdvanced to

ex-amine the motivational processes responsible for the

regu-lation of task-directed effort, using a variety of behavioral,

physiological, and self-report measures The simulation is

suited to the analysis of psychological phenomena at both

the within- and between-persons levels of analysis, using

both experimental and correlational methods (e.g., growth

curve modeling; Bliese & Ployhart, 2002) Other types of

phenomena that can be examined include the effects of

fatigue, alcohol, and caffeine on attention, reaction time,

and decision-making processes

Training Manual, Data Logging,

and System Requirements

The ATC-labAdvanced simulation package includes a

training manual and practice scenarios There are six

mod-0

.2

.4

.6

.8

1

Minimum Lateral Separation (nm)

Experts Trainees

Figure 5 The probability of intervention by controllers across

the minimum lateral separation of aircraft pairs

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anal-ysis of Australian air traffic control Australian Journal of Psychology

Supplement, 57, 109-110.

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control: How contextual factors influence conflict resolution choices

Australian Journal of Psychology Supplement, 59, 3.

Fothergill, S., & Neal, A (2008) An evaluation of the effect of

workload on conflict decision making in air traffic control Australian

Journal of Psychology Supplement, 60, 4.

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mi-croworlds in basic and applied cognitive research Cognitive Science

Quarterly, 2, 205-227.

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Jour-nal of Experimental Psychology: Applied, 4, 263-280.

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errors in complex environments Human Factors, 42, 367-378.

Kopardekar, P., & Magyarits, S (2003, June) Measurement and

pre-diction of dynamic density Paper presented at the 5th USA/Europe

ATM Research and Development Seminar, Budapest.

Kwantes, P J., Neal, A., & Loft, S (2004) Developing a formal model of human memory in a simulated air traffic control conflict

detection task In Proceedings of the Human Factors and

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Human Factors & Ergonomics Society.

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Dynamic density: An air traffic management metric

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In R Jensen (Ed.), Proceedings of the 14th International Symposium

on Aviation Psychology (pp 409-414) Dayton, OH: Association for

Aviation Psychology.

Loft, S., Campbell, L & Remington, R W (2008, March) Failure to

deviate from routine and task interference in an air traffic control task

Paper presented at the 34th Australasian Experimental Psychology

Conference, Perth, Australia.

Loft, S., Hill, A., Neal, A., Humphreys, M., & Yeo, G (2004)

ATC-lab: An air traffic control simulator for the laboratory Behavior

Re-search Methods, Instruments, & Computers, 36, 331-338.

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Raytheon (2005) FIRSTplus tower and radar simulator Retrieved

ers who wish to use or adapt them The present article

has presented a new, publicly available ATC simulation

package called ATC-labAdvanced.1 The realism and

experi-mental control provided by ATC-labAdvanced represents an

advance over many currently available simulators In

ad-dition, ATC-labAdvanced has the programming control to

allow systematic variation of realism and control in order

to investigate specific research questions of interest in a

cost-effective manner

AUTHOR NOTE

This research was supported in part by Linkage Grant LP0453978 from

the Australian Research Council The authors thank Phillip Waller for his

C programming of the ATC-lab Advanced program Thanks also go to

Peter Lindsay for his contribution to the formulae that underlie the 2-D

(lateral) dynamics of ATC-lab Advanced Please contact Peter (p.lindsay@

uq.edu.au) for further information regarding how these formulae were

derived Correspondence concerning this article should be addressed to

S Fothergill, School of Psychology, University of Queensland, Brisbane

4072, QSLD, Australia (e-mail: selina@psy.uq.edu.au).

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NOTE

1 Research groups interested in using ATC-lab Advanced for noncom-mercial purposes can download the program from www.psy.uq.edu.au/ directory/index.html?id=25 The following materials will be available for download: the ATC-lab Advanced base code; an example XML script based on a representative sample of the published studies; instructions on how to use the programming control features of the XML scripts; math-ematical formulae, spreadsheets, and instructions; the training modules and instructions; and the practice scenarios Questions regarding any of these materials can be directed to S Fothergill (selina@psy.uq.edu.au)

at the University of Queensland.

(Manuscript received January 14, 2008;

revision accepted for publication September 26, 2008.)

April 8, 2008, from www.ray.ca/external/home.nsf /(Webpages)/

Products_FIRSTplus? OpenDocument.

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anal-ysis of Australian air traffic control Australian Journal of Psychology

Supplement,... and SPSS ATC- labAdvanced was written using Qt Widget Library, owned by Troltech Microsoft Visual C6 compiler was used to build the source code ATC- labAdvanced can