From Laboratory to Cockpit: Evaluating the Predictive Value of
Cognitive Tasks on Flight Simulator Performance
S
´
ebastien Scannella
a
and Quentin Chenot
b
F
´
ed
´
eration ENAC ISAE-SUPAERO ONERA, Universit
´
e de Toulouse, France
Keywords:
Neuroergonomics, Flight Simulator, Executive Functions, Complex Tasks.
Abstract:
Comprehending the cognitive mechanisms underpinning success in demanding daily tasks is imperative for
the human factors field. It not only necessitates a foundational grasp of the human brain but also furnishes
invaluable insights for managing and averting human errors. This is particularly true in critical systems such
as airplane, surgery, and more recently with autonomous vehicles. In order to provide additional information
of the link between flying activity and high level cognitive functions, we investigated the relations between
common executive function task performance, more complex and ecological task performance and flight sim-
ulator performance. Our results suggest that the unitary nature and lack of real-life legitimacy of common
laboratory executive function tasks limit their ability to explain flight performance in the simulator —except
for set shifting. Conversely, more ecological and dynamic tasks that engage executive functions tend to explain
a larger variance in flying activity. Further research is planned to refine these predictive models and understand
the underlying cognitive mechanisms.
1 INTRODUCTION
Understanding the cognitive processes that contribute
to performance in complex, real-world tasks is impor-
tant in the human factors’ domain. Beyond the need
for fundamental understanding of the human brain,
it provides additional insight into handling and pre-
venting human errors (Leiden et al., 2001; Koech-
lin, 2014). This extends to professionals operating in
high-pressure environments, such as airplane pilots or
surgeons—to name a few—where human lives are at
risk. Of particular interest, executive functions (EFs)
have been shown to be the most important functions
to achieve efficient and adaptable behavior that is re-
quired in such tasks (Causse et al., 2011; Smit et al.,
2021; Panganiban and Matthews, 2014). However,
our understanding of executive functioning in real op-
erating settings is constrained by the experimental set-
tings.
On the one hand, complex cognitive processes
have been investigated in numerous studies under
controlled laboratory conditions for planning, inhi-
bition, working memory, or switching (Cristofori
et al., 2019; Logue and Gould, 2014; Sorel and Pen-
a
https://orcid.org/0000-0001-9547-8303
b
https://orcid.org/0000-0001-6439-9952
nequin, 2008; St Clair-Thompson and Gathercole,
2006; Friedman and Miyake, 2017; Miyake et al.,
2000). Although they have brought precious knowl-
edge about how the brain can efficiently provide such
high-level behaviors, they present at least two limits.
First, they often lack ecological validity because they
are designed to study an isolated process on a simpli-
fied computer task. Most of the time stimuli are pre-
sented in blocs of conditions, and\or one at a time,
which is never encountered in such a way in real life.
In addition, due to the isolation of one process over
the others, there is a limitation in the dynamics and
interactions among them, which consequently limits
the complexity. On the other hand, real life complex
task performance is also a significant research field
(Smit et al., 2021). Aircraft pilots, for instance, have
been involved in studies aiming at understanding au-
ditory attentional processes (Dehais et al., 2019a) or
more widely mental state (Gateau et al., 2018) in real
aircrafts. These studies, however, are less numerous
than laboratory ones, due to several difficulties. The
operational setting is often expensive and difficult to
access to (e.g., airplanes, airfield, control tower, etc.).
The measurement tools to assess cognitive processes
are exposed to internal (participants’ movements) and
external (light, electromagnetic fields) interferences.
Finally, what determines the quality of measurement
Scannella, S. and Chenot, Q.
From Laboratory to Cockpit: Evaluating the Predictive Value of Cognitive Tasks on Flight Simulator Performance.
DOI: 10.5220/0012918500004562
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 2nd International Conference on Cognitive Aircraft Systems (ICCAS 2024), pages 13-20
ISBN: 978-989-758-724-5
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
13

in real-life situations is also its biggest flaw, namely,
the lack of reproducibility and the presence of numer-
ous confounding factors.
To get the best of the two worlds, researchers
have developed complex tasks in simulators or with
pseudo-ecological environments (Causse et al., 2011;
Dehais et al., 2019b; Kennedy et al., 2010; Scan-
nella et al., 2018; Yesavage et al., 2011). The Multi-
Attribute Task Battery (MATB-II) for instance, is a
computer-based task designed to evaluate operator
performance and workload (Santiago-Espada et al.,
2011). The main interest of this battery lays in its
multitasking aspects. Unlike unitary process evalua-
tion, MATB requires the simultaneous performance
of monitoring, dynamic resource management, and
tracking tasks involving working memory, inhibition,
and switching. However, it can be argued that the sub-
tasks of the MATB are interdependent solely in rela-
tion to time-sharing. Allocating cognitive resources
to one subtask diminishes the resources available for
the others; a bad performance on one of them, how-
ever, do not directly affect the state of the others.
In an attempt to design a pseudo-ecological task to
study complex skill acquisition, Man
´
e and Donchin
(Man
´
e and Donchin, 1989) have created the Space
Fortress (SF) video game. In this 2D game, the par-
ticipant must control a ship and shoot at a central
fortress to destroy it. Simultaneously, the participant
must earn points and missiles by accomplishing sev-
eral subtasks, like identifying a predefined sequence
of special characters or mines types. According to the
authors, SF has been designed to rely on skills such
as memory, attention, dual-tasking ability, and psy-
chomotor control and speed, although these relation-
ships have not been rigorously tested yet. Latter, this
video game has shown successful transfer learning
to aircraft pilot trainees’ performance (Gopher et al.,
1994).
Regarding flight performance per se, in the review
of Smit et al., (Smit et al., 2021), the authors have
shown that multiple EFs together with other cognitive
abilities are associated with most of the measures of
flying, navigating, and communicating, and further-
more can predict flight performance. This suggests a
general involvement of cognition (including EFs) in
flight management. However, these results do not al-
low for the identification of critical EFs for handling
a plane, except for the working memory (Durantin
et al., 2016; Causse et al., 2011).
The present study sought to bridge the gap be-
tween laboratory cognitive assessments and real-
world task performance by examining the predictive
value of a range of cognitive tasks on flight sim-
ulator performance. Laboratory executive function
tests and pseudo-ecological tasks were performed by
pilots before assessing their performance on differ-
ent flight scenarios in a flight simulator, including
standard and complex airfield patterns (system fail-
ures, bad weather, etc.). Because of the complexity
of the flight scenario, we hypothesized that pseudo-
ecological tasks (SF and MATB) would be higher cor-
relates of simulator performance than the EF labora-
tory tasks.
2 METHODS
2.1 Participants
Thirty Private Pilots (4 women, mean age = 22y, mean
flight hours = 59.4) holding a Private Pilot License
(PPL) or in the process of obtaining it, were included.
The study has been approved by the local ethic com-
mittee of EUROMOV, Montpellier University, IRB-
EM: 2203C, in 2022.
2.2 Experimental Protocol
Participants were involved in a three-session protocol
across three days. In the first session they practiced
the SF and MATB tasks. In the second session they
underwent the executive function battery. In the last
session they flew the four flight scenarios in the sim-
ulator.
2.3 Executive Function Tasks
Participants first underwent a battery of nine execu-
tive function tasks that has been created according
to the literature (Friedman and Miyake, 2017). It
included three inhibition tasks, three updating tasks,
and three switching tasks. Note that the task codes
come from the millisecond test library (https://www.
millisecond.com/download/library/) and were admin-
istrated through the Inquisit software (V.6). The tasks
have been translated in French for the purposes of this
experiment and the modified code files are available
on https://osf.io/fm58p/.
2.3.1 Inhibition
Antisaccade. During this task, the participant must
focus on a fixation cross in the center of the screen. A
yellow square flashes (i.e., a visual cue) on either the
right or the left side of the cross. After the flash, an ar-
row appears on the opposite side of the flash, pointing
either left, right or up. The participant must respond
to which direction the arrow is pointing through the
ICCAS 2024 - International Conference on Cognitive Aircraft Systems
14

arrow keys on the keyboard. Note that we did not
measure saccades with an eye-tracker. The dependent
variable on this task is the proportion of correct re-
sponses.
Stop Signal. In this task, the participant must focus
on a fixation cross in the center of the screen. Then,
an arrow appears, pointing either left or right. The
participant has to press the corresponding arrow key.
However, in some trials, the participant hears an audi-
tory signal which indicates that she\he has to inhibit
her\his response. This task is extracted from Ver-
bruggen et al. (Verbruggen et al., 2019) and default
parameters were used. The dependent variable on this
task was the stop signal RT (response time).
Stroop. In this task, the participant saw colored stim-
uli (figures and words). Their goal was to indicate
the color of the stimuli. This test is adapted for com-
puters, and keyboard keys are associated with colors
(Scarpina and Tagini, 2017). There were congruent
(e.g., the word RED written in red) and incongruent
(e.g., the word RED written in blue) trials. The de-
pendent variable on this task was the RT difference
between congruent and incongruent trials.
2.3.2 Updating
Keep Track. During this task, the participant must
memorize and update words that are specific to cer-
tain categories (amongst 6 in total). The words are
presented one by one in the center of the screen. The
trials on this task include to keep-track of 3 to 4
words simultaneously and are randomized. The de-
pendent variable was the proportion of correctly re-
called words.
Letter Memory. In this task, the participant views a
series of letters that appear one at a time at the cen-
ter of the screen. Their goal is to memorize the four
last letters. This task is modified from Friedman et
al. (Friedman et al., 2008) in which participants have
only to memorize the three last letters. This modifi-
cation choice was done according to a pilot study that
revealed a ceiling effect with only 3 letters to mem-
orize. The dependent variable was the proportion of
correctly recalled letters.
Dual N-back. In this task, the participant needs to
follow a sequence of stimuli in two modalities at the
same time (visual and auditory). N-value was set to
two (i.e., 2-back task). The participant must deter-
mine whether the position of the square (visual) was
the same as the one observed two trials before in a 3
x 3 grid; and simultaneously determine whether the
heard letter is the same as the one presented two tri-
als before. Note that this is the only task that differs
from Friedman et al. (Friedman et al., 2008) study
in which they choose a spatial n-back. The reason is
that the spatial n-back is not available on millisecond
test library, and the dual n-back is the closest that we
found in this library. The dual n-back task from the
present study comes from Jaeggi et al. (Jaeggi et al.,
2010) and we used identical parameters with only the
2-back. The dependent variable was the proportion of
correct responses (yes and no).
2.3.3 Switching
Number Letter. During this task, the participant sees
a 2 x 2 matrix on the computer screen. A pair of char-
acters (ex: ’7C’) is presented and the participants have
to respond either on the letter (consonant vs. vowel)
or the digit (odd vs. even) depending on the posi-
tion of the characters on the matrix that will randomly
change. The dependent variable was the difference of
RT between switching trials and non-switching trials.
Color Shape. In this task, red or green circles or tri-
angles are presented to the participant. The goal is to
respond to the type of stimuli depending on the cue (S
for Shape vs. C for Color). The trials were random-
ized and the dependent variable on this task was the
difference of RT between switching trials and non-
switching trials.
Category Switch. During this task, the participants
are asked to categorize a word in terms of (a) living
criterion (living vs. non-living) or (b) size criterion
(smaller vs. larger than a basketball). A cue deter-
mined which categorization needs to be performed,
with a heart associated to the living criterion, and a
cross associated to the size criterion. The trials were
randomized and the dependent variable on this task
was the difference of RT between switching trials and
non-switching trials.
2.4 Pseudo-Ecological Tasks
2.4.1 MATB II
In the Multi-Attribute Task Battery II (MATB-II, see
https://github.com/VrdrKv/MATB/blob/master), the
participant must manage a computer-based system
featuring tasks intended to simulate those performed
during aircraft piloting. The main objective is to
maximize performance on several tasks on the same
screen. We used a four-task version (see Figure 1.b)
that included: (1) managing visual alarms (Monitor-
ing); (2) managing target tracking using a joystick
(Tracking); (3) managing fuel pumps (Fuel Man-
agement); and (4) managing radio communications
(Communication). In the Monitoring task, sliders are
moving randomly between two extreme values along
with two boolean generic alarms (red or green). The
participant must press the corresponding button (from
From Laboratory to Cockpit: Evaluating the Predictive Value of Cognitive Tasks on Flight Simulator Performance
15

F1 to F6) whenever one slider is stuck or when an
alarm switches from green to red. In the tracking task,
the participant must use the joystick to keep a moving
reticle as close as possible to the center of the tar-
get. In the Fuel Management task, participants have
to open or close 8 different pumps to optimize the to-
tal amount of fuel in two main tanks. Finally, in the
Communication task, participant have to set virtual ra-
dio frequencies according to simulated air-traffic con-
trol tower auditory messages.
The dependent variable was the cognitive-motor
performance of the best session as measured by a
global mean z-score including the reaction time in the
Monitoring task, the mean distance from the center in
the Tracking task and the mean distance from the op-
timal level in the two main tanks of the Fuel Manage-
ment task. Due to the low number of relevant Com-
munication events, this task has not been taken into
account in the scoring.
Figure 1: Complex tasks a. Space Fortress, b. MATB-II and
c. ISAE-SUPAERO flight simulator.
2.4.2 Space Fortress
For the purpose of this study, a Python-based
(ver. 2.7) version of SF was chosen from the
github.com/CogWorks website (see Figure 1.a). The
main goal of the game is to control a spaceship in
space with no gravity (first task). The second goal
is to destroy the fortress (second task). In addition,
the player has to memorize three specific letters that
will help him to identify and destroy different types of
mines (type-1 and type-2) that regularly appear in the
game (third task). Simultaneously, the player must
keep focusing on sequences of symbols (e.g. # and
$) that appear continuously throughout the game in
order to capture bonuses (fourth task). To obtain the
higher possible score, the participant has to perform
the four sub-tasks in parallel. To this goal, partici-
pants were given the following instruction: ”You will
maximize your score by performing all sub-tasks to
the best of your abilities”. A detailed description of
rules is available in our previous study (Chenot et al.,
2022).
During the first session, participants practiced the
game. This practice started with reading a docu-
ment that described the task (environment and rules).
After reading, participants were asked to play the
SF game for approximately 12 minutes (4 × 3 min
games). They practiced one task at a time following
the variable-training methods used in previous stud-
ies (Boot et al., 2010). More precisely, participants
were asked: to control the ship and only focus on
destroying the fortress (game 1); to capture bonuses
only (game 2); to destroy mines only (game 3); and
finally to perform all tasks together (game 4). Note
that participants were informed about the points’ dis-
tribution prior to play. The experimenter made sure
that participants understood and had experienced all
the rules of the SF game before starting the evalua-
tion. After practice, the participant performed the ex-
perimental session which constituted of ten SF games
(3 minutes each). They performed five ”monotask”
games (only flying the ship and destroy the fortress)
and five ”multitask” games (all tasks together) alter-
nating between one another.
The dependent variable was the z-score of the best
game total score, corresponding to the sum of points
including all sub-tasks.
2.5 Flight Simulator
In the flight simulator session, pilots were installed on
the left seat of the ISAE-SUPAERO flight simulator
Pegase which simulates an Airbus A320 (see Figure
1.c). Four flight scenarios were created according to
the flight rules (Visual Flight Rules; VFR vs. Instru-
ment Flight Rules; IFR) and the difficulty (Low vs.
High). The four scenarios consisted in flight traffic
patterns in the vicinity of Toulouse-Blagnac airport
and the difficulty was manipulated according to vis-
ibility, landing type and failure (see Table 1). Flight
Simulator performance was evaluated with a compos-
ite score taking into account compliance with flight
parameters, as well as approach and landing quality.
Each z-scored metric was assigned a specific weight
in the final composite score to reflect its relative im-
portance in flight operations. See Table 2 for a de-
tailed description of the global flight performance cal-
culation.
ICCAS 2024 - International Conference on Cognitive Aircraft Systems
16

Table 1: Flight Simulator Scenarios.
Type Diff Visibility Land Failure
VFR
Low Clear Visual None
High Cloudy Visual Alt+Speed
IFR
Low Clear ILS None
High Fog+Night ILS Engine
VFR: Visual Flight Rules. IFR: Instruments Flight Rules.
ILS: Instrument Landing System. Alt: Altitude.
Table 2: Flight Simulator Performance Scoring.
Parameters weights Metric Flight Phase
Altitude: 15%
Area
under
curve
Crosswind
Downwing
Base Leg
Speed: 15%
Area
under
curve
Crosswind
Downwing
Base Leg
Time: 15%
Time
Difference
Crosswind
Downwing
Base Leg
Landing: 40%
Max G’s Landing
Euclidian
Distance
Landing
Approach: 15%
Flight
Path
Deviation
Approach
2.5.1 Statistical Analyses
In order to test the hypothesis that complex task per-
formance (SF and MATB-II) would be a better cor-
relate of the flight performance compared to execu-
tive function scores, correlation analyses have been
carried-out using the global executive function score
(mean z-score of the nine task performance), SF score
(z-score in the best game), MATB-II score (mean z-
score of three subtasks in the best game) and flight
simulator performance score (mean weighted z-score
of altitude, speed, heading, approach and landing met-
rics) of the four scenarios. Two additional correla-
tion coefficient analyses have been done in order to
statistically validate the best flight simulator perfor-
mance predictive variable. All scores have been z-
scored and analyses have been carried out using R
studio (V4.2.2). The main hypotheses were tested by
the following correlations:
• Executive Function tasks Flight simulator perfor-
mance (EF and flight simulator composite score).
• Pseudo-ecological tasks versus Flight simulator
(a. SF and flight simulator composite score; b.
MATB and flight simulator composite score).
• Correlation coefficient comparisons between
these three correlations.
Additional correlations analyses were also performed:
• Executive functions versus pseudo ecological
tasks (a. EF and MATB; b. EF and SF);
• Between the two pseudo ecological tasks (EF ver-
sus SF).
Finally, a complementary analysis between individual
EF tasks and simulator performance (Inhibition, Up-
dating and shifting) was done.
3 RESULTS
Main hypothesis results are summarized in Figure 2.a,
b and c. Concerning correlations with the flight simu-
lator performance, SF was the higher predictive vari-
able, albeit not reaching statistical significance (r =
0.35; p = 0.06), while executive functions and MATB
scores did not significantly correlate with simulator
score (r = 0.2; p = 0.29 and r = −0.035; p = 0.86,
respectively). A first correlation coefficient compar-
ison (R cocor package) revealed that SF was a bet-
ter predictor of the flight simulator performance than
the MATB (one-tailed z = 2.01, p < 0.05). A second
correlation coefficient however did not reveal a bet-
ter predictive value of SF compared to EF (one-tailed
z = 0.75, p = 0.23).
As shown in Figure 2.f, despite the difference be-
tween MATB and SF in correlating with the flight
simulator performance, performance in these two
tasks was highly correlated with each other (r = 0.59;
p < 0.001). Similarly, our analysis revealed moderate
correlation (r = 0.3; p = 0.09; in the best case) be-
tween EFs and complex tasks (SF and MATB). Look-
ing at the relation between each three executive func-
tions and the flight simulator performance (see Figure
2.g, h and i), we found a significant predictive value
of the shifting performance (r = 0.39; p < 0.05) but
not for the Inhibition or updating scores (r = 0.11;
p = 0.57 and r = 0.00; p = 0.99 respectively).
4 DISCUSSION
Although based on a limited sample, this study un-
derscores the complex relationship between labora-
tory cognitive tasks and ”close-to-real-world” perfor-
mance. It suggests that Space Fortress—the more
ecological task—is the closest to the flying one, as
attested by its relative predictive power for flight sim-
ulator performance. This task has been first developed
From Laboratory to Cockpit: Evaluating the Predictive Value of Cognitive Tasks on Flight Simulator Performance
17

Figure 2: Correlation results for the main hypotheses (a, b and c) and additional comparisons (from d to i). All scores are
z-scores. Solid lines stand for the linear fits.
by psychologists to study complex skill acquisition
(Man
´
e and Donchin, 1989) and latter showed trans-
fer learning to real-life flying activity (Gopher et al.,
1994). As a consequence, our hypothesis was that this
task would be a better correlate to the flying activity
in the flight simulator compared to more abstract EF
tasks. Indeed, it involves visuo-motor coordination,
working memory, inhibition and alternating between
four different, dynamic, and interconnected subtasks.
All these cognitive processes may be needed to han-
dle a flight in complex situations.
A second interesting results is the unexpected
non-predictable value of the MATB for the simula-
tor performance (close to 0% of explained variance),
despite its high correlation level with Space Fortress
(around 35% of explained variance). These results
suggest that these two complex tasks exhibit both
overlaps and notable differences. Like SF, the MATB
involves visual tracking, working memory and shift-
ing from one subtask to another. A major difference,
however, is that MATB subtasks are not intercon-
nected; meaning that poor performance on one sub-
task does not affect other subtasks but only the global
score. In SF, missing bonuses or ignoring mines type
prevent from getting points and ammo to shoot at the
Fortress. For instance, the fortress and mine subtasks
cannot be achieved without a proper performance on
the bonus subtask.
Similarly, the global score obtained in executive
functioning in our sample of pilots poorly predicted
the flight performance. This may appear contradic-
tory with previous studies showing that reasoning and
ICCAS 2024 - International Conference on Cognitive Aircraft Systems
18

working memory, as attested by simplified computer-
based tasks, can significantly predict flight simulator
performance (Causse et al., 2011; Smit et al., 2021).
Looking more closely at the subscores of the three ex-
ecutive functions (i.e, inhibition, updating, shifting)
we found that only shifting could explain part of the
simulator performance. One explanation could stand
in the fact that working memory in flight management
has been mostly shown to be used for radio communi-
cation (Morrow et al., 2003; Smit et al., 2021), which
was absent in our simulator task.
4.1 Limits
It should be noted that this protocol is part of a larger
study on cognitive training of airplane pilots. The
present article focuses on the first flight simulator
session only, and subsequent results from this larger
study may be addressed in a future publication. The
sample size of pilots is not large enough to prevent
from false positive\negative correlation effects or to
test more advanced prediction models (e.g., multi-
ple regression models). Finally, in this first approach
study, neither the flight experience, nor the video
game experience have been taking into account, al-
though these could be covariates that explain a part
of variance in complex task or flight simulator perfor-
mance.
4.2 Conclusion
As a conclusion, our results suggest that the abil-
ity to handle the different subtasks in our most dif-
ficult flight simulator scenario seems to rely on being
able to handle interconnected tasks by switching ef-
ficiently between them and taking into account their
interdependence rather than just relying on working
memory, inhibition or unrelated multitasking compo-
nents.
Further research is planned to refine these predic-
tive models with a higher statistical power and more
exhaustive flight simulator performance assessment.
It will allow for more sophisticated models taking
into account pilot’s demographics and performance in
subtasks of the flying activity.
ACKNOWLEDGEMENTS
The authors wish to thank Stephane Perrey for his
help in obtaining the local committee approval. This
study is part of a research project funded by the
French procurement agency (DGA).
REFERENCES
Boot, W. R., Basak, C., Erickson, K. I., Neider, M., Simons,
D. J., Fabiani, M., Gratton, G., Voss, M. W., Prakash,
R., Lee, H., et al. (2010). Transfer of skill engendered
by complex task training under conditions of variable
priority. Acta psychologica, 135(3):349–357.
Causse, M., Dehais, F., and Pastor, J. (2011). Executive
functions and pilot characteristics predict flight simu-
lator performance in general aviation pilots. The Inter-
national Journal of Aviation Psychology, 21(3):217–
234.
Chenot, Q., Hamery, C., Lepron, E., Besson, P., De Boisse-
zon, X., Perrey, S., and Scannella, S. (2022). Perfor-
mance after training in a complex cognitive task is en-
hanced by high-definition transcranial random noise
stimulation. Scientific Reports, 12(1):4618.
Cristofori, I., Cohen-Zimerman, S., and Grafman, J. (2019).
Executive functions. Handbook of clinical neurology,
163:197–219.
Dehais, F., Dupr
`
es, A., Blum, S., Drougard, N., Scannella,
S., Roy, R. N., and Lotte, F. (2019a). Monitoring pi-
lot’s mental workload using erps and spectral power
with a six-dry-electrode eeg system in real flight con-
ditions. Sensors, 19(6):1324.
Dehais, F., Roy, R. N., and Scannella, S. (2019b). Inat-
tentional deafness to auditory alarms: Inter-individual
differences, electrophysiological signature and sin-
gle trial classification. Behavioural brain research,
360:51–59.
Durantin, G., Scannella, S., Gateau, T., Delorme, A., and
Dehais, F. (2016). Processing functional near in-
frared spectroscopy signal with a kalman filter to as-
sess working memory during simulated flight. Fron-
tiers in human neuroscience, 9:707.
Friedman, N. P. and Miyake, A. (2017). Unity and diver-
sity of executive functions: Individual differences as a
window on cognitive structure. Cortex, 86:186–204.
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C.,
Corley, R. P., and Hewitt, J. K. (2008). Individual
differences in executive functions are almost entirely
genetic in origin. Journal of experimental psychology:
General, 137(2):201.
Gateau, T., Ayaz, H., and Dehais, F. (2018). In silico vs.
over the clouds: on-the-fly mental state estimation of
aircraft pilots, using a functional near infrared spec-
troscopy based passive-bci. Frontiers in human neu-
roscience, 12:187.
Gopher, D., Well, M., and Bareket, T. (1994). Transfer of
skill from a computer game trainer to flight. Human
factors, 36(3):387–405.
Jaeggi, S. M., Buschkuehl, M., Perrig, W. J., and Meier, B.
(2010). The concurrent validity of the n-back task as a
working memory measure. Memory, 18(4):394–412.
Kennedy, Q., Taylor, J. L., Reade, G., and Yesavage, J. A.
(2010). Age and expertise effects in aviation decision
making and flight control in a flight simulator. Avia-
tion, space, and environmental medicine, 81(5):489–
497.
From Laboratory to Cockpit: Evaluating the Predictive Value of Cognitive Tasks on Flight Simulator Performance
19

Koechlin, E. (2014). An evolutionary computational theory
of prefrontal executive function in decision-making.
Philosophical Transactions of the Royal Society B: Bi-
ological Sciences, 369(1655):20130474.
Leiden, K., Laughery, K. R., Keller, J., French, J., Warwick,
W., and Wood, S. D. (2001). A review of human per-
formance models for the prediction of human error.
Ann Arbor, 1001:48105.
Logue, S. F. and Gould, T. J. (2014). The neural and genetic
basis of executive function: attention, cognitive flex-
ibility, and response inhibition. Pharmacology Bio-
chemistry and Behavior, 123:45–54.
Man
´
e, A. and Donchin, E. (1989). The space fortress game.
Acta psychologica, 71(1-3):17–22.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H.,
Howerter, A., and Wager, T. D. (2000). The unity and
diversity of executive functions and their contributions
to complex frontal lobe tasks: A latent variable analy-
sis. Cognitive psychology, 41(1):49–100.
Morrow, D. G., Menard, W. E., Ridolfo, H. E., Stine-
Morrow, E. A., Teller, T., and Bryant, D. (2003). Ex-
pertise, cognitive ability, and age effects on pilot com-
munication. The International Journal of Aviation
Psychology, 13(4):345–371.
Panganiban, A. R. and Matthews, G. (2014). Executive
functioning protects against stress in uav simulation.
In Proceedings of the Human Factors and Ergonomics
Society Annual Meeting, volume 58, pages 994–998.
SAGE Publications Sage CA: Los Angeles, CA.
Santiago-Espada, Y., Myer, R. R., Latorella, K. A., and
Comstock Jr, J. R. (2011). The multi-attribute task
battery ii (matb-ii) software for human performance
and workload research: A user’s guide. Technical re-
port, NASA.
Scannella, S., Roy, R. N., Laouar, A., and Dehais, F. (2018).
Auditory neglect in the cockpit: using erps to disen-
tangle early from late processes in the inattentional
deafness phenomenon. In Neuroergonomics, pages
207–208. Elsevier.
Scarpina, F. and Tagini, S. (2017). The stroop color and
word test. Frontiers in psychology, 8:557.
Smit, D., Geppert, S. E., Daneshnia, N., Lafont, A., and
Dehais, F. (2021). Executive functions and flying per-
formance in pilots. In Neuroergonomics Conference
2021.
Sorel, O. and Pennequin, V. (2008). Aging of the planning
process: The role of executive functioning. Brain and
cognition, 66(2):196–201.
St Clair-Thompson, H. L. and Gathercole, S. E. (2006). Ex-
ecutive functions and achievements in school: Shift-
ing, updating, inhibition, and working memory. Quar-
terly journal of experimental psychology, 59(4):745–
759.
Verbruggen, F., Aron, A. R., Band, G. P., Beste, C., Bis-
sett, P. G., Brockett, A. T., Brown, J. W., Chamberlain,
S. R., Chambers, C. D., Colonius, H., et al. (2019). A
consensus guide to capturing the ability to inhibit ac-
tions and impulsive behaviors in the stop-signal task.
elife, 8:e46323.
Yesavage, J. A., Jo, B., Adamson, M. M., Kennedy, Q.,
Noda, A., Hernandez, B., Zeitzer, J. M., Friedman,
L. F., Fairchild, K., Scanlon, B. K., et al. (2011).
Initial cognitive performance predicts longitudinal
aviator performance. Journals of Gerontology Se-
ries B: Psychological Sciences and Social Sciences,
66(4):444–453.
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