T obias Rieger, Dietr ich Manz e y
Human perf ormance consequences of automated
decision aids: The impact of time pressure
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Rieger , T ., Manze y , D . (2020). Human P erf or mance Consequences of A utomated Decision Aids: The Impact
of Time Pressure. Human F actors: The Jour nal of the Human F actors and Ergonomics Society ,
001872082096501. https://doi.org/10.1177/0018720820965019.
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R unning head: TIME PRESSURE AND A UTOMA TION 1
Human p erformance consequences of automated decision aids: The impact of time pressure
T obias Rieger
T ec hnisc he Univ ersität Berlin
Dietric h Manzey
T ec hnisc he Univ ersität Berlin
This is a p ost-p eer-review, pre-cop y edit v ersion of an article publis hed in Human F actors
(accepted Septem b er 7th, 2020). The final authen ticated v ersion is a v ailable online at:
h ttp://dx.doi.org/10.1177/0018720820965019
A uthor Note
A ddress corresp ondence to: T obias Rieger, T ec hnisc he Univ ersität Berlin,
Departmen t of Psyc hology and Ergonomics, Chair of W ork, Engineering, and
Organizational Psyc hhology , Marc hstraße 12, 10587 Berlin, German y , email:
tobias.rieger@tu-b erlin.de. The authors w ould lik e to thank Janina Dubb erk e for help in
data collection of Exp erimen t 1.

TIME PRESSURE AND A UTOMA TION 2
Abstract
Ob jectiv e: The study addresses the impact of time pressure on h uman in teractions
with automated decision supp ort systems (DSS) and related p erformance consequences.
Bac kground: When h umans in teract with DSS, this often results in w orse
p erformance than could b e exp ected from the automation alone. Previous researc h has
suggested that time pressure migh t make a difference b y leading h umans to rely more on a
DSS.
Metho d: In t w o lab oratory exp erimen ts, participan ts p erformed a luggage
screening task either man ually , supp orted b y a highly reliable DSS, or a lo w reliable DSS.
Time pro vided for insp ecting the X-ra ys w as 4.5 vs 9 sec. P articipants in the automation
conditions w ere either sho wn the automation’s advice prior (Exp erimen t 1) or follo wing
(Exp erimen t 2) their o wn insp ection, b efore they made their final decision.
Results: In Exp erimen t 1, time pressure compromised p erformance indep enden t of
whether the task w as p erformed man ually or with automation supp ort. In Exp erimen t 2,
the negativ e impact of time pressure w as only found in the manual, but not the t w o
automation conditions. Ho w ev er, neither exp erimen t rev ealed an y p ositiv e impact of time
pressure on o v erall p erformance, and the join t p erformance of h uman and automation w as
mostly w orse than the p erformance of the automation alone.
Conclusion: Time pressure compromises the qualit y of decision making. Pro viding
a DSS can reduce this effect, but only if the automation’s advice follo ws the assessmen t of
the h uman.
Application: The study pro vides suggestions for effective implemen tation of DSSs,
but also supp orts concerns that highly reliable DSSs are not used optimally b y h uman
op erators.
Keyw ords: Decision making, Human-automation in teraction, Compliance and
reliance, Stress, T rust in automation

TIME PRESSURE AND A UTOMA TION 3
Human p erformance consequences of automated decision aids: The impact of time pressure
A utomation and sp ecifically computer-based automated assistance systems are
presen t in virtually eve ry field to day . The main ideas wh y automation is in tro duced in a
large n um b er of settings are that automation supp osedly mak es w ork safer, reduces
w orkload, and impro v es o v erall p erformance (F renc h, Duenser, & Heathcote, 2018;
Sheridan & P arasuraman, 2005). One sp ecial case of automation are decision supp ort
systems (DSSs) whic h are intended to aid a h uman execute a certain task (Mosier &
Fisc her, 2010; Mosier & Manzey, 2020). DSSs can widely v ary in their complexit y but the
general idea is that they pro vide users information on the true state of the w orld, based on
automated pro cessing and ev aluation of information from the en vironmen t. They include a
v ariet y of systems ranging from binary alarm or target detection systems (e.g., smok e
detection) to complex exp ert systems pro viding automatically generated diagnoses to
radiologists or surgeons, based on artificial in telligence applications (e.g., Jiang et al.,
2017). Regardless of the t yp e of system, though, the final output to the user with DSSs is
usually straigh tforw ard, i.e., includes a definite recommendation, diagnosis or just
indication of a certain state, ev en though the pro cessing (e.g., complex image pro cessing)
in the bac kground might be highly complex. Ho w ev er, in a great deal of cases, h umans do
not use the DSS appropriately , whic h can result in automation misuse or disuse
(P arasuraman & Riley, 1997). With resp ect to binary DSSs lik e alarm systems or target
detection supp ort, t w o differen t asp ects of automation use whic h can b e closely link ed to
p oten tial misuse or disuse are compliance and reliance (Mey er, 2001, 2004). Sp ecifically ,
op erator compliance is defined b y op erators agreeing with the automation when it indicates
that a target is presen t. Con v ersely , op erator reliance is defined b y op erators agreeing with
the automation when it indicates that no target is presen t. Th us, op erator compliance can
b e considered to reflect the b elief that a critical ev en t is actually presen t when an alarm
o ccurs, and op erator reliance can b e considered to reflect the b elief that that the system
will actually alert in case of a critical ev en t (Mey er, 2001). Ho w ev er, there are cases where

TIME PRESSURE AND A UTOMA TION 4
the reliance or compliance of op erators do es not seem to b e prop erly calibrated to the
p erformance of a DSS.
Th us, in terestingly , often when highly reliable DSSs are used, the join t p erformance
of op erator and automation is w orse than that of the automation alone (see Bartlett &
McCarley, 2017, for mo del-based accoun ts of that phenomenon; see also Alb erdi,
P o vy akalo, Strigini, & A yton, 2004; Mey er, 2001; Mey er, Wiczorek, & Günzler, 2014). Note
that this refers to a h yp othetical comparison of the p erformance of the automation with
the op erator p ossibly in terv ening (i.e., p erformance of the h uman-automation dy ad) v ersus
a situation where the automation alone w ould b e in c harge of the decision.
This sho ws that even with highly reliable systems, trust (and accordingly , compliance
and reliance) are often mis-calibrated (P arasuraman & Riley, 1997), leading op erators to
alter outputs of the alarm system whic h w ere actually true.
Time Pressure and A utomated DSSs
Ho w ev er, the degree of time pressure op erators ha v e in making their decisions while
in teracting with an automated DSS might mak e a difference in this resp ect. Time pressure
is an ev eryda y phenomenon in a plethora of w orkplaces and is usually considered a
to-b e-a v oided w orkload factor in the h uman factors literature (e.g., Cara y on & Gurses,
2008; Hendy , Liao, & Milgram, 1997; Mora y , Dessouky , Kijo wski, & A dapath y a, 1991).
Concerning trust in automation, w e refer to the three-la y ered mo del of Hoff and B ashir
(2015), where time pressure w ould b e considered as an external situational factor. Note,
ho w ev er, that in the presen t researc h, our main fo cus is on b eha vioral consequences of trust
rather than sub jectiv e measures of trust.
Sp ecifically , there is evidence that time pressure can lead to more heuristic
decision-making and th us not necessarily lead to concomitan t p erformance decreases (see
Gigerenzer & Gaissmaier, 2011; Shah & Opp enheimer, 2008, for reviews). In the con text of
h uman-automation-in teraction, a p ossible heuristic migh t b e an increased dep endence on

TIME PRESSURE AND A UTOMA TION 5
the automation’s suggestion. Using automation as a heuristic has b een referred to as
automation bias (Mosier, Skitka, Burdic k, & Heers, 1996; P arasuraman & Manzey, 2010)
and is usually considered as something that can negativ ely impact p erformance if critical
ev en ts are o v erlo ok ed due to automation misuse. If a DSS is highly reliable, ho w ev er,
automation bias can p oten tially ev en lead to impro v ed p erformance—esp ecially if the DSS
p erforms b etter than the h uman alone. One factor whic h is kno wn to increase the use of
heuristics is time pressure (P ayne, Bettman, & Johnson, 1988), and there are indeed
findings whic h suggest that dep endence on DSS increases under high time pressure (Rice,
Hughes, McCarley , & Keller, 2008; Rice & Keller, 2009; Rice, Keller, T rafimo w, & Sandry,
2010; Rice & T rafimo w, 2012). F or example, in their study , Rice and Keller (2009) found
o v erall p erformance b enefits of time pressure if the automation w as highly reliable. T o
in v estigate this, they used a military con text and presen ted their participan ts aerial view
photographs with the participan ts having to decide whether there w as a tank presen t or
not. In order to v ary time pressure, the time a v ailable for insp ection of the photographs
w as v aried across participan ts (8 vs. 2 sec). Moreo v er, participan ts w ere assigned a DSS to
aid their decisions and DSS reliabilit y w as v aried in fiv e conditions (65%, 80%, 95%, 100%,
man ual). The k ey finding of this study w as that participan ts who w ork ed under high time
pressure, dep ended more strongly on the DSS whic h led to concordan t p erformance
increases under high time pressure. Ho w ev er, in b oth this study (Rice & Keller, 2009) as
w ell as later corrob orating studies (Rice et al., 2010; Rice & T rafimo w, 2012), the extremely
high time pressure (i.e., 2 seconds for complex visual stim uli) migh t ha v e left participan ts
with no real c hoice but to follo w the DSS. Th us, the main finding of a higher dep endence
on automation with time pressure migh t just b e an artifact of this extreme time pressure
condition. Moreo v er, time pressure w as v aried b et w een-sub jects—something that seems to
b e rather unlik ely to happ en in the real w orld where time pressure is exp ected to v ary
more frequen tly across differen t trials, dep ending on situational circumstances.
Consequen tly , w e aim to revisit this issue using somewhat less extreme and more

TIME PRESSURE AND A UTOMA TION 6
realistic time pressure v ariation, giving participan ts an actual c hance to insp ect the
stim ulus themselv es ev en under time pressure, as w ould b e the case in most real-w orld
scenarios.
One real-w orld con text where b oth time pressure and automated DSSs can pla y an
imp ortan t role is luggage screening at airp ort securit y c hec kp oin ts (e.g., Cha v aillaz,
Sc h w aninger, Mic hel, & Sauer, 2018). Here, screeners m ust classify whether there is a
prohibited article in the bag or not and time pressure while p erforming this screening task
ma y v ary frequen tly across the da y dep ending on the n um b er of passengers w aiting in the
line. Normally , airp ort securit y screeners ha v e around 10 seconds p er image (Buser,
Sterc hi, & Sc h w aninger, 2019), whic h decreases to around 4 seconds during busier p erio ds
(Sc h w aninger, Hardmeier, & Hofer, 2004). The basic task to b e p erformed in luggage
screening can b e considered as a signal-detection task. As has formally b een describ ed in
signal detection theory (SDT, Green & Sw ets, 1966), the p erformance in suc h tasks
dep ends on t w o v ariables, i.e., the sensitivit y (d’) in terms of ability to distinguish b et w een
non-target (e.g., a p en) and targets (e.g., a knife), and the resp onse threshold (C), i.e., on
ho w m uc h evidence decisions ab out the presence of targets are based (see Stanisla w &
T o doro v, 1999, for calculation of d’ and C). DSSs are used in suc h tasks in order to help
op erators optimizing b oth asp ects. Ho w ev er, dep ending on their capabilit y , these systems
migh t differ in their reliability , i.e., the probabilit y that their advice is correct. Sp ecifically ,
due to a safe-engineering approac h, such target detection systems often ha v e lib eral
resp onse thresholds whic h mak e them more or less false-alarm prone. If time pressure do es
indeed increase DSS dep endence, the related h uman p erformance consequences should b e
dep enden t on the reliabilit y of the DSS. Only in the case that the automation reliabilit y is
considerably greater than the h uman alone p erformance, one w ould exp ect a visible b enefit
of the DSS in the join t h uman-automation p erformance.

TIME PRESSURE AND A UTOMA TION 7
The Presen t Exp erimen ts
The goal of the presen t exp erimen ts w as to examine the influence of time pressure in
a luggage screening task with and without an automated DSS a v ailable. T o this end, w e
used a luggage screening task and participan ts p erformed the task either with no DSS
supp ort (i.e., man ually), with a lo w reliabilit y DSS (75% correct suggestions), or with a
high reliabilit y DSS (95% correct suggestions). Corresp onding to most applications in
safet y-critical en vironmen ts suc h as luggage screening, b oth conditions w ere essen tially
false-alarm prone. Ho w ev er, also some rare misses w ere sim ulated. 75% w as c hosen as the
lo w er reliabilit y lev el, in order to create a clear v ariation but still ha v e a lev el whic h seems
to b e realistic for false-prone systems and whic h is still higher than the 70% threshold
assumed to b e the minim um reliabilit y where automated systems are still considered to b e
useful (Wic k ens & Dixon, 2007). Con trasting to some earlier researc h (e.g., Rice & Keller,
2009), w e v aried time pressure blo c kwise within-sub jects b ecause real-w orld op erators’
w ork en vironmen ts (and th us, time constrain ts) also c hange from time to time and do not
remain constan t. F urthermore, a time pressure condition w as c hosen whic h put the
participan ts under considerable pressure to p erform the task quic kly , but also w as long
enough to get the task principally done ev en without automation.
In Exp erimen t 1, w e used a paradigm where participan ts in the automation
conditions w ere first show n the DSS’s advice and then made their o wn c hoice. Then, in
Exp erimen t 2, w e rev ersed that order—that is, participan ts could first mak e their initial
c hoice and w ere then sho wn the advice of the DSS with the p ossibilit y of either confirming
or c hanging the ow n decision based on that advice.
Exp erimen t 1
As w as men tioned ab ov e, w e used b oth a high reliabilit y DSS condition and a low
reliabilit y DSS condition as well as an unsupp orted man ual condition. These conditions
w ere v aried b et w een-sub jects b ecause w e w an ted to a v oid prior exp eriences with a highly

TIME PRESSURE AND A UTOMA TION 8
(or lo w) reliable system influence the use of the DSS. W e v aried time pressure
within-sub jects—that is, time pressure (i.e., lo w vs. high) w as v aried blo c kwise b ecause it
seems lik ely that in a lot of real-w orld con texts time pressure also v aries from time to time.
W e h yp othesized that participan ts in the high reliabilit y DSS condition w ould ha v e
b etter p erformance than those in the lo w reliabilit y condition. The man ual condition
mainly serv ed as a control condition to understand the effects of time pressure for the
luggage-screening task without DSS supp ort. Moreo v er, based on previous findings (e.g.,
Rice & Keller, 2009), w e hypothesized that time pressure w ould decrease p erformance, but
only if the automation w ere not highly reliable. This is based on the idea that op erators
are more dep enden t on the DSS under time pressure. Con v ersely , an increased dep endence
can p ossibly ev en lead to p erformance increases if the automation is highly reliable,
although in this case, op erators often sho w a high lev el of reliance and compliance, an yw a y .
Th us, in the high reliability DSS condition, participan ts should ha v e greater p erformance
than in the man ual condition, whereas this difference is not as clear a priori for the lo w
reliabilit y DSS condition. Bey ond that, w e also in v estigated whether time pressure w ould
also mak e a difference for the lev el of reliance whic h has not b een addressed b efore.
Metho d
This researc h complied with the tenets of the Declaration of Helsinki and w as
appro v ed b y the Institutional Review Board at the T ec hnisc he Univ ersität Berlin,
Departmen t of Psyc hology . Informed consen t w as obtained from eac h participan t.
P articipan ts. 60 participan ts (24 female) w ere recruited through the online
recruiting system of the Departmen t of Psyc hology at T ec hnisc he Univ ersität Berlin.
P articipan ts to ok part in the exp erimen t for either course credits or monetary
comp ensation of 9 € . The mean age of participan ts w as 28.62 ( SD : 4.75), and the sample
w as predominan tly righ t-handed (56 righ t-handed). T w o additional participan ts in the
man ual condition w ere tested but excluded from an y analyses due to accuracy not

TIME PRESSURE AND A UTOMA TION 9
deviating from c hance in the exp erimen tal blo c ks.
Apparatus and Stim uli. W e used images from the X-Ra y Ob ject Recognition
T ests (X-Ra y OR T) 1.3 and 2.0 (Hardmeier, Hofer, & Sc h w aninger, 2005; Sc h w aninger,
Hardmeier, & Hofer, 2005) as stim uli. These stim uli can b e classified as hard or easy based
on three dimensions: p oin t of view (i.e., canonical or not), bag complexit y (i.e., high or lo w
clutter in the bag), and ob ject o v erlap (i.e., whether there is little or strong o v erlap of
other ob jects on the target). T o ensure similar difficult y for the stim uli—esp ecially
considering the time constrain ts for resp onding—w e selected images with a similar
difficult y according to these dimensions, using the images whic h w ere scored as difficult in
an y t w o of the three categories. These images could then either con tain a target (i.e., gun
or knife) or not, and w e mirrored the images b oth horizon tally and v ertically to double the
n um b er of a v ailable stim uli, resulting in a total stim ulus set of 320 images.
One to six participan ts were sim ultaneously tested at indep enden t PC w orkstations,
separated b y opaque screens. The exp erimen t w as run with E-Prime 3.0. W e used 24 inc h
screens with a 1920x1200 resolution, and the stim uli presen ted w ere sized 700x550 pixels,
cen tered v ertically and horizon tally on screen. Resp onses w ere made using the ’q’ and ’p’
k eys on a German standard k eyb oard, with the left and righ t index fingers, resp ectiv ely ,
and it w as counterbala nced across participan ts whic h resp onse side w as asso ciated with a
target presen t/absen t resp onse.
Pro cedure. P articipan ts w ere randomly assigned to one of the three
b et w een-sub jects conditions: One third of the participan ts w as tested in the man ual
condition, one third in the high reliabilit y (i.e., 95%) automation condition, and one third
in the lo w reliability (i.e., 75%) condition. The exp erimen t consisted of t w o training blo c ks
and six exp erimen tal blo c ks, with 40 trials eac h. In ev ery blo c k, half of the trials w ere
target presen t trials, and half of the trials w ere target absen t trials. The full exp erimen t
to ok around 60 min utes.
A t the b eginning of the exp erimen t, participan ts w ere sho wn four o v erview images

TIME PRESSURE AND A UTOMA TION 10
with all target stim uli (i.e., tw o gun and t w o knife o v erview images) for 10 seconds to
familiarize participan ts with the target items. All participan ts had the same first training
blo c k, with easier stim uli than in the remainder of the exp erimen t (i.e., stim uli with only
one of the three dimensions scored as difficult). Because the first training blo c k w as mainly
used to familiarize participan ts with the basic screening task, no automation supp ort w as
giv en in this blo c k. P articipan ts w ere instructed to resp ond as fast and as accurately as
p ossible.
In the automation conditions, the second training blo c k in tro duced the automated
DSS, whic h w as visualized b y sho wing t w o circles with one of them b eing filled with either
red or green color, to indicate a dangerous item or to indicate that no target w as presen t,
prior to stim ulus onset. The t w o automation conditions differed in terms of their false
alarm rate, but not in their hit rate, and the differences b et w een the automation conditions
are sho wn in T able 1. In addition to the false alarms, one miss eac h app eared in the last
and p en ultimate blo c k of b oth conditions. Misses w ere included only to w ard the end of the
blo c ks in order to a void an y strong p erformance consequences of first-failure effects of
misses sk ewing the whole exp erimen t (Wic k ens & Xu, 2002). W e made sure that there were
no automation failures of an y kind in consecutiv e trials. Moreo v er, there w ere no cases of
more than three consecutiv e trials requiring the same resp onse and also no cases of the
same image app earing on consecutiv e trials. All of these constrain ts w ere em b edded in a
sequen tial trial pro cedure.
After the DSS w as introduced in the second blo c k, participan ts filled out tw o
questionnaires. First, they w ere ask ed to fill out a 2x2 con tingency table (i.e., Hits, F As,
Misses, CRs) on ho w they p erceiv ed the automation in the past 40 trials. Second, they
w ere ask ed to fill a questionnaire on trust in tec hnical systems (Wiczorek, 2011).
Subsequen t to filling out the questionnaires, participan ts w ere sho wn the true 2x2
con tingency table for 100 exp erimen tal trials, to bridge the description-exp erience gap
(e.g., Hert wig & Erev, 2009).

TIME PRESSURE AND A UTOMA TION 11
Condition Reliabilit y Hit Rate F A Rate d’ C
High Reliabilit y 95% 98.33% 8.33% 3.40 -0.34
Lo w Reliabilit y 75% 98.33% 48.33% 2.08 -1.00
T able 1
K ey char acteristics of the automate d de cision supp ort systems in the exp erimental blo cks.
Note that the signal dete ction the ory (SDT) me asur es ar e lo gline ar-c orr e cte d (Hautus,
1995) b e c ause some c el ls of p articip ant x c ondition pr o duc e d p erfe ct hit r ates, r e quiring this
c orr e ction for the empiric al data. Thus, we have c orr e cte d d’ and the criterion C in this
table analo gously. F A: false alarm. d’: dete ction p erformanc e in SDT, C: criterion to
me asur e r esp onse bias.
After the first t w o training blo cks, the time pressure manipulation w as in tro duced
and the six exp erimen tal blo c ks started. That is, in half of the blo c ks, participan ts had 4.5
seconds to mak e their resp onse, and 9 seconds in the other half. These cutoffs w ere c hosen
based on a pilot study to allo w participan ts to still pro cess the stim ulus but to set them
under mo derate time pressure. Time pressure alternated blo c kwise and it w as
coun terbalanced across participan ts whether time pressure w as high in o dd/ev en blo c ks.
The trial pro cedure w as as follo ws. In the man ual condition, trials started with a
fixation cross for 2000 ms. In the automated DSS conditions, trials started with the
automation advice b y presen ting either a red (target presen t) or green (target absen t)
circle as the automation advice for 1500 ms, follo w ed b y a 500 ms fixation cross. The side
where red/green circles w ere presen ted corresp onding with the resp onse k ey assignmen t.
Then the stim ulus remained on screen for a maxim um of 4.5/9 seconds, dep ending on the
time pressure condition in the resp ectiv e blo c k, or un til a resp onse w as made. This w as
indicated b y a countdo wn on the upp er left ab o v e the image. Eac h trial w as follow ed b y a
500 ms in ter-trial-in terv al with a blank screen. In the t w o practice blo cks, instead of the

TIME PRESSURE AND A UTOMA TION 12
in ter-trial-in terv al, participan ts receiv ed feedbac k on their resp onse after ev ery trial for
1000 ms. The trial pro cedure for the automation conditions’ exp erimen tal blo c ks is
visualized in Figure 1.
Figur e 1 . T ypical trial sequence in the automation condition of Exp erimen t 1. ITI:
in ter-trial-in terv al (blank screen).
Design. A utomation condition w as v aried b et w een sub jects (i.e., high reliabilit y ,
lo w reliabilit y , man ual), and time pressure (i.e., high, lo w) alternated blo c kwise within
sub jects. Th us, the presen t study used a 3 (automation condition) x 2 (time pressure)
rep eated-measures mixed design.
Results
P erformance. W e conducted a 3 (automation condition) x 2 (time pressure)
ANO V A on the p ercen tage of correct resp onses (PC) as our primary analysis. The

TIME PRESSURE AND A UTOMA TION 13
60
70
80
90
100
PC
Lo w High
T ime Pressure
Accurac y
High Reliability DSS
Low Reliability DSS
Manual
A
0
1
2
3
4
d’
Lo w High
T ime Pressure
d’
High Reliability DSS
Low R eliability DSS
Manual
B
60
70
80
90
100
% Agreement
Lo w High
T ime Pressure
Compliance
High Reliability DSS
Low Reliability DSS
C
60
70
80
90
100
% Agreement
Lo w High
T ime Pressure
Reliance
High Reliability DSS
Low R eliability DSS
D

Figur e 2 . A: P ercent correct (PC), B: signal detection theory’s d’ (loglinear corrected), C:
compliance, and D: reliance as a function of time pressure, separately for eac h condition.
In subplots A and B, the dotted horizon tal lines represen t the v alues (PC and d’,
resp ectiv ely) for the p erformance of the DSS alone. Error bar represen ts the p o oled
standard error. DSS = decision supp ort system.
alpha-lev el for this and all subs equen t analyses w as set to the con ven tional .05 lev el. The
corresp onding means of this ANO V A are displa y ed in Figure 2A. This ANO V A rev ealed a
significan t main effect of automation condition, F (2 , 57) = 37.774, p<. 001 , η 2
p = 0 . 57 .
That is, pairwise comparisons with a Bonferroni-corrected alpha lev el rev ealed that
resp onses in the high reliabilit y DSS condition w ere significan tly more accurate (84.7%)

TIME PRESSURE AND A UTOMA TION 14
than in the lo w reliability DSS condition (71.1%, p < .001) and than in the man ual
condition (70.3%, p < .001). The difference b et w een the man ual and the lo w reliabilit y
DSS condition w as non-significant ( p = .678). Moreo v er, the main effect of time pressure
w as also significan t, F (1 , 57) = 23.803, p<. 001 , η 2
p = 0 . 295 , indicating a higher PC under
lo w (77.8%) than under high (72.9%) time pressure. In terestingly , the in teraction w as
non-significan t, F (2 , 57) = 1.022, p = . 367 , η 2
p = 0 . 035 .
A dditionally , in the high reliabilit y automation condition, the a v erage p erformance of
the h uman-automation-dy ad w as significan tly w orse than the automation alone, ev en
without time pressure (86.7%), t (19) = 7.454, p < .001, clearly sho wing a sub-optimal DSS
use. In the lo w reliabilit y automation condition, join t p erformance under lo w time pressure
(74.5%) did not differ from the automation reliabilit y , t (19) = 0.378, p = .710.
W e conducted a parallel analysis on the signal detection measure d’ and the
corresp onding means can b e found in Figure 2B. Because some cells of participan t x
condition pro duced p erfect hit rates, w e loglinear corrected all signal detection analyses
according to Hautus (1995). Moreo v er, b ecause it is not that clear for target absen t trials
with no resp onse whether this trial should coun t as a correct rejection or false alarm, w e
excluded these trials from the SDT analyses. Ob viously , target presen t trials with no
resp onse w ere coun ted as a miss. This ANO V A again rev ealed a significan t main effect of
automation condition, F (2 , 57) = 53.692, p<. 001 , η 2
p = 0 . 653 . P airwise comparisons with
a Bonferroni-corrected alpha lev el revea led significan t differences b et w een the high
reliabilit y DSS (2.31) and the low reliabilit y DSS (1.27, p < .001) as w ell as the man ual
(1.27, p < .001) condition. The difference b et w een the lo w reliabilit y DSS condition and
the man ual condition w as non-significan t ( p = .974). Moreo v er, there w as again a
significan t main effect of time pressure, F (1 , 57) = 5.212, p = . 026 , η 2
p = 0 . 084 , with a
higher sensitivit y under low (1.69) than under high (1.55) time pressure. Again, the
in teraction w as non-significan t, F (2 , 57) = 0.389, p = . 680 , η 2
p = 0 . 013 .

TIME PRESSURE AND A UTOMA TION 15
Compliance and Reliance. The finding that time pressure led to impairmen ts of
p erformance indep enden t of automation reliabilit y w as surprising. W e analyzed the effects
of time pressure separately for compliance and reliance to b etter understand to what
exten t the effects on ov erall p erformance w ere related to time pressure induced c hanges in
compliance and reliance in the t wo automation supp ort conditions. W e defined compliance
as the agreemen t rate with the automation when the automation indicated that there w as
a target presen t, and reliance as the agreemen t rate with the automation when the
automation indicated that there w as no target presen t.
W e conducted a 2 (automation condition) x 2 (time pressure) ANO V A on
compliance. The corresp onding means can b e found in Figure 2C. This ANO V A rev ealed a
significan t main effect of automation condition, F (1 , 38) = 48.526, p<. 001 , η 2
p = 0 . 561 ,
with higher compliance in the high reliabilit y (82.4%) than in the lo w reliabilit y (60.6%)
condition. Neither the main effect of time pressure w as significan t, F (1 , 38) = 1.334,
p = . 255 , η 2
p = 0 . 034 , nor the in teraction of condition and time pressure, F (1 , 38) = 0.174,
p = . 679 , η 2
p = 0 . 005 .
Results of a parallel analysis for reliance are visualized in Figure 2D. The ANO V A
rev ealed a main effect of condition, F (1 , 38) = 5.619, p = . 023 , η 2
p = 0 . 129 , with higher
reliance in the high reliabilit y (87.5%) than in the lo w reliabilit y (80.5%) condition.
Moreo v er, in terestingly , the main effect of time pressure w as significan t, F (1 , 38) = 17.947,
p<. 001 , η 2
p = 0 . 321 , with less reliance under high (80.9%) than under lo w (87.1%) time
pressure. The in teraction w as non-significan t, F (1 , 38) = 0.117, p = . 734 , η 2
p = 0 . 003 .
Resp onse Times. Finally , w e also analyzed resp onse times (R T s) as a dep enden t
measure. W e excluded incorrect resp onses from the R T analyses (24.7%), and after visual
insp ection w e also excluded R T s shorter than 500 ms (0.2% of the remaining trials).
Because a luggage screening task is essen tially a visual searc h task with a self-terminating
searc h, with usually longer R T s for target absent trials, w e added target presen t/absen t as
an additional factor in the ANO V A, resulting in a 3(condition) x 2(time pressure) x

TIME PRESSURE AND A UTOMA TION 16
2(target presen t/absen t) ANO V A with rep eated measures for the last t w o factors, and the
results are displa y ed in Figure 3A-C. Unsurprisingly , R T s w ere faster in target presen t
(2593 ms) than in target absen t (4023) trials, F (1 , 57) = 392.93, p<. 001 , η 2
p = 0 . 873 .
Moreo v er, resp onses w ere faster under high (2583 ms) than under lo w (4043 ms) time
pressure, F (1 , 57) = 360.72, p<. 001 , η 2
p = 0 . 864 . The main effect of condition w as
non-significan t, F (2 , 57) = 3.0525, p = . 055 , η 2
p = 0 . 097 , with unreliably smaller R T s in the
high reliabilit y DSS (3086 ms) than in the lo w reliabilit y DSS (3390 ms) and the man ual
(3448 ms) conditions. In terestingly , the in teraction of target presence and time pressure
w as significan t, F (1 , 57) = 126.43, p<. 001 , η 2
p = 0 . 689 , with a m uch larger effect of time
pressure in target absen t ( ∆ : 1946 ms) than in target presen t ( ∆ : 913 ms) trials. The
in teraction of target presence and condition was also significan t, F (2 , 57) = 10.264,
p<. 001 , η 2
p = 0 . 265 . Here, the effect of target presence w as largest in the man ual
condition (1603 ms), and smaller in the lo w reliabilit y DSS (1482 ms) and high reliabilit y
DSS (1269 ms) conditions. The three-w a y in teraction w as also significan t, F (2 , 57) =
5.2916, p = . 008 , η 2
p = 0 . 157 . That is, as b ecomes eviden t from Figure 3 the difference
b et w een target presen t/absen t trials under high time pressure w as particularly small in the
high reliabilit y DSS condition ( ∆ : 671 ms) compared to the man ual ( ∆ : 1088 ms) and lo w
reliabilit y DSS ( ∆ : 983 ms) conditions. The in teraction of time pressure and condition w as
non-significan t ( p = .203).
In an exploratory manner, w e extended our R T analyses to distributional analyses,
using the deciles of the R T distribution to b etter understand the effects of time pressure.
T o that end, w e calculated the deciles for eac h participan t, separately for eac h time
pressure condition, and a veraged these deciles for eac h condition across participan ts. These
deciles are visualized in Figure 3D-F. As is eviden t from the figure, participan ts in the high
time pressure condition usually resp onded faster than they had to—as there is already a
difference in the 20th p ercen tile of trials.

TIME PRESSURE AND A UTOMA TION 17
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10 20 30 40 50 60 70 80 90
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F

Figur e 3 . Resp onse time (R T) data separately for eac h condition of Exp erimen t 1. P anels
A-C displa y the mean R T s separately for target present and absen t trials, as a function of
time pressure. P anels D-F displa y the deciles of the R T distribution separately for lo w and
high time pressure trials. Error bar in the upp er panels represen ts the p o oled standard
error. DSS = decision supp ort system.
Questionnaire Data. T o c hec k ho w participan ts p erceiv ed the automation, w e
also analyzed sub jectiv e data. That is, w e analyzed questionnaire on trust in tec hnical
systems (Wiczorek, 2011, scale from 1-4). P articipan ts in the high reliabilit y (3.20) group
had higher trust on the trust in tec hnical systems questionnaire than participan ts in the
lo w reliabilit y (2.89) group, t (38) = 2.807, p = .008, d = 0.88. In the con tingency table,
participan ts on a v erage estimated the high reliabilit y DSS to b e 89.3% accurate, and the
lo w reliabilit y DSS to b e 72.4% accurate. Because participan ts w ere informed ab out the
true reliabilit y of their DSS after filling out the table, w e are confident that participan ts

TIME PRESSURE AND A UTOMA TION 18
generally had a realistic p erception of the DSS’s reliabilit y .
Discussion
The k ey findings of Exp erimen t 1 are that (a) time pressure led to w orse o v erall
p erformance, (b) this negativ e effect of time pressure w as not atten uated (or ev en led to
p ositiv e effects) when a highly reliable automation w as a v ailable, and (c) that p erformance
increased with a highly reliable automation but w as still w orse than the automation alone.
Moreo v er, participan ts’ o v erall compliance did not differ b et w een the high and lo w time
pressure blo c ks but in terestingly , participan ts w ere more relian t on the automation under
lo w than under high time pressure. The o v erall p erformance decreases th us seem to b e
connected to a decreased reliance under high time pressure, in b oth automation conditions.
Besides the general p erformance consequences, in the R T data, w e found that participan ts
sp ed up their resp onses a lot more than necessary , and could ha v e tak en more time in a lot
of high time pressure trials to inform their decision.
The finding of a general negativ e effect of time pressure, ev en with a highly reliable
DSS a v ailable, clearly con trasts some earlier findings (Rice & Keller, 2009; Rice et al.,
2010; Rice & T rafimo w, 2012). In this earlier researc h, time pressure w as found to increase
the dep endence on the automation whic h clearly impro v ed o v erall p erformance in cases of a
highly reliable system. The results of the presen t exp erimen t suggest that these earlier
findings could ha v e mainly b een due to the extreme lev el of time pressure used in that
study (only t w o seconds to insp ect complex aerial photographs) whic h probably left
participan ts no other c hoice than to follo w the advice of the automation. In con trast, the
presen t exp erimen t did not use a time pressure manipulation that extreme, lea ving the
participan ts the p ossibilit y to also at least to some degree man ually insp ect the visual
stim uli. In this exp erimen t, it is quite clear that participan ts made use of this p ossibilit y
whic h is most clearly reflected in the reliance measure whic h sho w ed that reliance ev en
decreased somewhat with increasing time pressure. Also, the elev ated time pressure led

TIME PRESSURE AND A UTOMA TION 19
them to sp eed up their resp onses more than w ould ha v e b een needed. This suggests that
time pressure in our study did not seduce participan ts to delegate their resp onsibilit y to
the automation but, instead, induced a sort of self-pressure to insp ect the X-ra y as quic kly
as p ossible. Moreo v er, the data clearly suggests that in our study participan ts generally did
not use the highly reliable automation adequately , regardless of whether they w ere under
time pressure or not. That is, indep enden t of the lev el of time pressure, participan ts w ould
ha v e generally b een b etter serv ed to just follo w the DSS advice rather than in terfering with
the automation. This lac k of adequate use of the DSS also reiterates the problem that join t
p erformance of h uman and automation is often w orse than that of the automation alone
(Bartlett & McCarley, 2017).
Ov erall, this pattern of results suggests that the participan ts w ere generally reluctan t
to just follo w the automated decision aid. P erhaps, they w an ted to mak e sense of their role
as resp onsible op erator b y c hec king and correcting the automation. In terv ening with the
DSS’s recommendations then led to more false than prop er corrections, particularly with
the high reliabilit y DSS. One reason for this migh t b e b ecause participan ts alw a ys
insp ected the stim ulus after seeing the advice. This could ha v e prompted them in a
particular w a y to b ecome active and not just accepting the automated advice. Th us, w e
designed Exp erimen t 2 in order to giv e participan ts the p ossibilit y to first mak e their o wn
decision and then giv e them the DSS advice—allo wing for the p ossibilit y to sp ecifically use
the automation in cases of o wn uncertain t y and to still con tribute prop erly to the
decision-making pro cess in cases where one is confiden t in the o wn decision. One w ould
then assume that under time pressure, participan ts should feel less sure ab out their o wn
decisions and dep end more on the automation. This setup could then p ossibly reduce the
negativ e effects of time pressure—or even lead to p ositiv e effects if participan ts realize they
should dep end on the automation as m uc h as p ossible under time pressure.

TIME PRESSURE AND A UTOMA TION 20
Exp erimen t 2
The second exp erimen t w as designed to c hec k whether c hanging the order of image
displa y (and the concomitan t resp onse) and automation advice mak es a difference for joint
h uman-automation decision-making. That is, in the automation conditions, participan ts
w ere alw a ys first sho wn the image and w ere ask ed to mak e their initial resp onse to the
stim ulus while the stimulus w as displa y ed. Then, they w ere sho wn the automation advice
and had the opp ortunit y to sta y with their c hoice or to make a c hange based on the
automation’s advice. Note that the automated DSSs w ere the same as in Exp erimen t 1,
and the man ual condition was exactly the same as in Exp erimen t 1.
Our h yp otheses w ere largely the same as in Exp erimen t 1. W e exp ected an increased
dep endence on the automated DSS under high time pressure compared to lo w time
pressure. That is, as w as argued ab o v e, w e exp ected that b ecause participan ts can no w
first mak e their own decision and then get the advice, they can use it more sp ecifically in
trials where they are less secure ab out their o wn judgmen t. Th us, they w ould still feel in
the lo op (rather than just follo wing a cue’s advice) but could still b enefit from the
automation. Again, w e h yp othesized that time pressure w ould decrease p erformance, but
only if the automation is not highly reliable. Con v ersely , high time pressure could ev en
lead to p erformance increases with a highly reliable automation.
Metho d
P articipan ts. A fresh sample of 60 participan ts (34 female, 1 div erse) w as tested in
Exp erimen t 2. They ranged in age from 18 to 37 ( M = 26.35) and were predominan tly
righ t-handed (52 righ t-handed). Participan ts to ok part in the exp erimen t for either course
credits or monetary comp ensation of 9 € . T w o additional participan ts in the lo w reliabilit y
condition w ere also tested but excluded from an y analyses due to issues with understanding
the instructions in one case and the exp erimen t en vironmen t crashing in the other case.

TIME PRESSURE AND A UTOMA TION 21
Apparatus, Stim uli, Pro cedure, and Design. No c hanges w ere made in the
man ual condition. In the automation supp ort conditions, the apparatus, stim uli,
pro cedure, and design w ere the same as in Exp erimen t 1 except for the follo wing c hanges.
First, in the automation conditions, the fixation cross w as alw a ys presen t for 2 seconds,
regardless of automation condition. Second, participan ts alw a ys first made their target
absen t/presen t c hoice and only subsequen tly receiv ed automation supp ort for their
decision. The time pressure manipulation remained the same, that is, it c hanged blo c kwise
whether participan ts had 4.5 or 9 seconds to mak e their initial decision. Then, after seeing
the automation decision, they had the opp ortunit y to either confirm their initial judgmen t
b y pressing the same key again or to c hange their decision b y pressing the other resp onse
k ey . The stim ulus disapp eared from the screen con tingen tly after the initial resp onse if one
w as giv en or after a maxim um of 4.5/9 seconds. P articipan ts w ere alw a ys sho wn their o wn
resp onse for 500 ms after they made their resp onse, indicated b y a green/red circle for
target absen t/presen t resp onses, or an indication that they did not resp ond in time,
resp ectiv ely . Subsequen tly , the automation advice app eared on the screen b elo w their o wn
initial decision, also displa yed b y a green/red circle, for a maxim um of 4.5 or 9 seconds in
the high time pressure blo c ks and lo w time pressure blo c ks, resp ectiv ely . P articipan ts w ere
instructed that if they decided to not press an y resp onse k ey when the automation w as
sho wn, that this was in terpreted as sta ying with the initial judgmen t.
Results
P erformance. F or the automation conditions, w e alw a ys used the last resp onse
giv en as the final decision to inform the p erformance measures. As in Exp erimen t 1, w e
conducted a 3 (automation condition) x 2 (time pressure) ANO V A on PC, and the
corresp onding means can b e found in Figure 4A. This ANO V A again rev ealed a significan t
main effect of condition, F (2 , 57) = 28.349, p<. 001 , η 2
p = 0 . 499 . P airwise comparisons
with a Bonferroni-corrected alpha lev el revealed more accurate resp onses in the high

TIME PRESSURE AND A UTOMA TION 22
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Figur e 4 . A: P ercent correct (PC), B: signal detection theory’s d’ (loglinear corrected), C:
compliance, and D: reliance as a function of time pressure, separately for eac h condition.
In subplots A and B, the dotted horizon tal lines represen t the v alues (PC and d’,
resp ectiv ely) for the p erformance of the DSS alone. Error bar represen ts the p o oled
standard error. DSS = decision supp ort system.
reliabilit y DSS condition (84.3%) than in the lo w reliabilit y DSS (72.1%, p < .001) and
than in the man ual (69.1%, p < .001) conditions. The difference b et w een the man ual and
the lo w reliabilit y DSS condition w as non-significan t ( p = .083). There w as also a
significan t main effect of time pressure, F (1 , 57) = 7.583, p = . 008 , η 2
p = 0 . 117 , with less
accurate resp onses under high (74.2%) than under lo w (76.2%) time pressure. In terestingly ,

TIME PRESSURE AND A UTOMA TION 23
and con trasting to Exp erimen t 1, there w as no w a significan t in teraction of automation
condition and time pressure, F (2 , 57) = 4.660, p = . 013 , η 2
p = 0 . 141 . As is eviden t in
Figure 4A, the time pressure effect seemed to v anish in b oth the high reliabilit y DSS ( ∆ :
0.3%) and the lo w reliability DSS ( ∆ : 0.6%) conditions, but not in the man ual condition
( ∆ : 5.2%).
As in Exp erimen t 1, w e additionally c hec k ed whether the p erformance of the
h uman-automation dy ad differed from the p erformance alone under lo w time pressure.
Again, in the high reliabilit y automation condition, the a v erage p erformance of the
h uman-automation-dy ad w as significan tly w orse than the automation alone, ev en without
time pressure (84.4%), t (19) = 5.249, p < .001. In con trast to Exp erimen t 1, the
p erformance of the dy ad in the lo w reliabilit y condition (72.4%) also differed significan tly
from the automation alone, t (19) = 2.328, p = .031.
W e again conducted a parallel analysis for d’ and the results are displa y ed in
Figure 4B. This ANO V A revealed a significan t main effect of automation condition,
F (2 , 57) = 18.452, p<. 001 , η 2
p = 0 . 393 . P airwise comparisons with a Bonferroni-corrected
alpha lev el rev ealed greater sensitivit y in the high reliabilit y DSS condition (2.19) than in
b oth the lo w reliabilit y DSS (1.34, p < .001) and the man ual (1.34, p < .001) conditions.
There w as no difference b et w een the man ual and the lo w reliabilit y DSS conditions ( p =
.991). F or d’, the main effect of time pressure w as non-significan t, F (1 , 57) = 3.091,
p = . 084 , η 2
p = 0 . 051 . The in teraction w as non-significan t, F (2 , 57) = 1.686, p = . 194 ,
η 2
p = 0 . 056 , ho w ev er, a v ery similar visual pattern is eviden t from Figure 4B.
Compliance and Reliance. Due to the exp erimen tal set-up of Exp erimen t 2, w e
had to tak e a slightly differen t approac h to measure compliance and reliance. That is, w e
restricted compliance and reliance analyses to all trials where the initial resp onse w as not
the same as the automation’s suggestion whic h w as sho wn to participan ts. Then, w e
measured compliance and reliance as the prop ortion of trials where the participan t’s
resp onse equaled the automations’ decision after they had seen the automation’s advice.

TIME PRESSURE AND A UTOMA TION 24
Th us, the agreemen t rates of b oth exp erimen ts are not directly comparable b ecause the
underlying trial base w as not the same.
W e conducted a 2 (automation condition) x 2 (time pressure) ANO V A for compliance,
and the results are visualized in Figure 4C. This ANO V A rev ealed a significan t main effect
of automation condition, F (1 , 38) = 8.239, p = . 007 , η 2
p = 0 . 178 , with higher compliance in
the high reliabilit y DSS condition (66.7%) than in the lo w reliability DSS condition
(46.9%). The main effect of time pressure w as non-significan t, F (1 , 38) = 1.866, p = . 180 ,
η 2
p = 0 . 047 . The in teraction w as also non-significan t, F (1 , 38) = 0.063, p = . 803 , η 2
p = 0 . 002 .
A parallel ANO V A was conducted for reliance with the results sho wn in Figure 4D.
F or reliance, there w as no main effect of automation condition, F (1 , 38) = 1.030, p = . 317 ,
η 2
p = 0 . 026 . In terestingly , there w as a main effect of time pressure, F (1 , 38) = 7.087,
p = . 011 , η 2
p = 0 . 157 , with higher reliance under high (60.6%) than under lo w (52.8%) time
pressure. The in teraction w as non-significan t, F (1 , 38) = 0.0096, p = . 923 , η 2
p = 0 .
Resp onse Times. As in Exp eriment 1, w e also analyzed R T s. W e used only the
R T s during the initial target displa y , b efore the automation w as sho wn b ecause that most
lik ely represen ts the actual target searc h. Again, w e restricted our analyses to correct R T s
only (exclusion of 34.4% of all trials) with a minim um R T of 500 ms (0.2% of all remaining
trials). W e again included target presence as an additional factor in the ANO V A. The
corresp onding means of this ANO V A are displa y ed in Figure 5A-C. This ANO V A rev ealed
faster R T s in target presen t (2252 ms) than in target absen t (3597 ms) trials, F (1 , 57) =
250.84, p<. 001 , η 2
p = 0 . 815 . Moreo v er, resp onses w ere faster under high (2472 ms) than
under lo w (3377 ms) time pressure, F (1 , 57) = 84.218, p<. 001 , η 2
p = 0 . 596 . As in
Exp erimen t 1, the in teraction of target presence and time pressure w as again significant,
F (1 , 57) = 74.539, p<. 001 , η 2
p = 0 . 567 , with a larger effect of time pressure in target
absen t (547 ms) than in target presen t (1265 ms) trials. No effect including condition w as
significan t ( p -v alues > .427) whic h mak es sense b ecause the initial decision in
Exp erimen t 2 w as basically a manual decision.

TIME PRESSURE AND A UTOMA TION 25
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Percentile
Lo w Reliability DSS Condition
Low T ime Pressure
High T ime Pressure
F

Figur e 5 . Resp onse time (R T) data separately for eac h condition of Exp erimen t 2. P anels
A-C displa y the mean R T s separately for target present and absen t trials, as a function of
time pressure. P anels D-F displa y the deciles of the R T distribution separately for lo w and
high time pressure trials. Error bar in the upp er panels represen ts the p o oled standard
error. DSS = decision supp ort system.
W e again conducted an exploratory analysis on the R T distribution whic h is
visualized in Figure 5D-F. As in Exp erimen t 1, it seems lik e participan ts resp onded m uc h
faster than necessary under time pressure, again with differences in the faster part of the
R T distribution. Th us, without the real need to sp eed up the fastest resp onses, it seems
lik e participan ts still did so under time pressure.
Questionnaire Data. In Exp erimen t 2, there w as only a sligh t descriptiv e trend in
the sub jectiv e data, with unreliably higher trust in the high reliabilit y DSS (3.09) than in
the lo w reliabilit y DSS (2.88) on the scale of Wiczorek (2011), t (38) = 1.656, p = .106, d =

TIME PRESSURE AND A UTOMA TION 26
0.523. In the con tingency table, participan ts on a v erage estimated the high reliabilit y DSS
to b e 90.8% accurate, and the lo w reliabilit y DSS to b e 77.6% accurate, again close to the
actual reliabilities.
Discussion
T o summarize the k ey finding of Exp erimen t 2, the initial-decision then
automation-advice setup apparen tly reduced the negative impact of time pressure on
p erformance to little or no effect, as indicated b y the PC data. Moreo v er, w e also found
in teresting results for compliance and reliance, with an increase for reliance under high
time pressure and higher compliance in the high reliabilit y DSS condition. Th us, it seems
lik e the exp erimen tal setup of Exp erimen t 2, with participan ts receiving the automation
advice only after ha ving made their o wn decision, made quite a difference for op erator
reliance under time pressure. That is, in this scenario, participan ts reliance w as greater
under high than under lo w time pressure, probably also pla ying a role in the significan t
in teraction found for PC. It also mak es sense that there was only a significan t effect of
condition on compliance, as the alarm systems only differed in terms of their false alarm
rate. The results of Exp erimen t 2 th us seem to confirm the h yp othesis that giving
participan ts the DSS’s advice only after they had made their initial c hoice could reduce
negativ e effects of time pressure by increasing the use of the DSS in those high time
pressure trials, particularly for reliance. The presen t findings align w ell with the findings
b y Ho, P a vlo vic, My ers, and Arrabito (2013) who found that giving participan ts the
p ossibilit y to only use the automation when they felt it w as useful increased o v erall
p erformance compared to higher lev els of automation that alw a ys ga v e recommendations.
General Discussion
The goal of the presen t research w as to in v estigate p erformance consequences of time
pressure with differen t automated DSSs (or none) a v ailable. T o this end, w e used a
luggage-screening task and had participan ts carry out this task, under b oth lo w and high

TIME PRESSURE AND A UTOMA TION 27
time pressure. Moreo v er, participants w ere randomly assigned to one of three conditions
using either no automated DSS (man ual), a highly reliable DSS (95% reliabilit y) or a lo w
reliable DSS (75% reliabilit y). In Exp erimen t 1, participan ts in the automation conditions
w ere first sho wn a cue and then the stim ulus, and in Exp erimen t 2, w e reversed that order
and ga v e participan ts the p ossibilit y to c hange their initial decision based on the DSS’s
advice. The main findings w ere that time pressure largely led to negativ e effects on
p erformance—ho w ev er, rev ersing the order of decision-making in Exp erimen t 2 strongly
reduced these negativ e effects. Moreo v er, regarding the dep endence on the automation, our
results w ere rather mixed—with less reliance on the DSS under high time pressure in
Exp erimen t 1 and an increased reliance under high time pressure in Exp erimen t 2.
Compliance w as largely unaffected by in tro ducing time pressure. P erhaps most
in terestingly , in b oth exp erimen ts and in all four automation conditions, the join t mean
p erformance (in b oth PC and d’) of the h uman-automation dy ad w as descriptiv ely w orse
than the automation alone.
As w as men tioned ab o v e, con trary to some earlier findings (e.g., Rice & Keller, 2009),
w e did not find time pressure induced b enefits for p erformance ev en with a highly reliable
DSS. Th us, it seems as if time pressure—at least in the presen t study—did not lead to
more heuristic or optimized decision-making, as w as suggested b y earlier studies (e.g.,
P a yne et al., 1988; Rice & Keller, 2009). A t b est, our results indicate that ha ving a DSS
a v ailable after ha ving made the initial o wn c hoice can reduce the negativ e effects of time
pressure, as w as shown in Exp erimen t 2. That is, c hanging the order of decision-making
reduced the negativ e effects of time pressure, and increased reliance on the DSS, pro viding
some evidence for an increased automation dep endence under high time pressure. Th us, it
seems lik e participan ts used the DSS more selectiv ely in those trials where they w ere less
secure ab out their o wn decisions—and it mak es sense that those trials w ere mostly
time-pressured trials. Ho w ever, there w ere still no p ositiv e effects of time pressure as w as
rep orted b y earlier studies (e.g., Rice & Keller, 2009). F rom a practical standp oin t,

TIME PRESSURE AND A UTOMA TION 28
c hanging the order of decision-making could b e recommendable in con texts where time
pressure cannot b e a v oided.
The presen t findings also reinforce earlier concerns (e.g., Bartlett & McCarley, 2017;
Mey er, 2001; Mey er et al., 2014) ab out the join t p erformance of a h uman-automation dy ad.
It seems clear that participan ts did not use the automation adequately , regardless of
whether they w ere under time pressure or not. That is, if the automation w ould not ha v e
b een in terfered with b y a human, the o v erall p erformance w ould ha v e b een higher,
esp ecially with the high reliabilit y DSS. Ev en under time pressure there w as no more
adequate automation use, despite the fact that time pressure decreased man ual
p erformance—and one w ould assume that decreased p ersonal p erformance capabilit y w ould
lead to stronger dep endence on the automation (Rice & Keller, 2009).
The com bined findings of a negativ e impact of time pressure on p erformance, and the
fact that the automation alone mostly sho w ed greater p erformance than the
h uman-automation dy ad, raises the question of whether a higher lev el of automation (e.g.,
Kab er, 2018; Sheridan & V erplank, 1978) migh t b e adequate under high time pressure, as
previously suggested b y Moray , Inagaki, and Itoh (2000). That is, Mora y et al. (2000)
argued that under high time pressure, one can truly b enefit from automation, and
particularly if the automation k eeps the h uman out of the lo op. This prop osal seems
particularly promising considering the fact that the join t p erformance w as b elo w that of
the automation in the presen t exp erimen ts. Moreo v er, Johnson, Ren, Kuc har, and Oman
(2002) also suggested that "sub jects w ere reticen t to deviate from highly automated ...
suggestions ev en when significan t impro v emen ts w ere still p ossible" (p.132)—th us, k eeping
the h uman out of the lo op migh t b e b est for o v erall p erformance. Ob viously , in a lot of
w ork settings, it is not p ossible to remo v e the h uman from the lo op, b e it for legal reasons
or a v ailabilit y in emergency situations—and the presen t results implicate that time
pressure should b e a v oided in s uc h situations.
Besides the main p erformance consequences of time pressure and automation, w e also

TIME PRESSURE AND A UTOMA TION 29
analyzed some additional measures, with some in teresting implications. Sp ecifically , in
b oth exp erimen ts, participan ts under high time pressure to ok less time than they could
ha v e had to mak e their decision. This not only sho ws that the manipulation w as successful,
but also that in real-w orld contexts, one could try to use recommendations to w ork ers that
they should alw a ys tak e the time they ha v e at hand to complete a task, a v oiding rushed
decisions.
No study comes without limitations and the presen t exp erimen ts are no exception to
that. First, the presen t study used quite a high base rate (50%). Obviously , the base rate
at airp ort securit y c hec kp oin ts is m uc h lo w er, but this giv es nonetheless ev en more
imp ortance to the lo w reliabilit y DSS condition with a high false alarm rate. That is, it is
necessarily true that with decreasing base rates and a constan t automation reliabilit y , more
false alarms are pro duced (P arasuraman & Riley, 1997). Nev ertheless, future researc h
migh t in v estigate the influence of base rate. Second, the DSS w e used w as of rather simple
nature, and evidence has b een pro vided (Cha v aillaz et al., 2018) that direct cues (i.e., cues
marking the target) can impro ve performance compared to a simple cue (suc h as the one
w e used). Third, w e m ust ac kno wledge that giving participan ts a se c ond chanc e for their
decision in the automation conditions in Exp erimen t 2 migh t ha v e decreased time pressure,
esp ecially b ecause participan ts resp onded rather quic kly after seeing the DSS’s advice, as
this w as a rather simple agree/disagree statemen t. Note also that ev en though the negativ e
effect of time pressure w as ameliorated in Exp erimen t 2, the o v erall p erformance in the
automation conditions did not really differ b et w een Exp erimen t 1 and 2.
One question whic h our present researc h cannot address is what role self-confidence
with the task pla ys for automation dep endence, and future researc h should in v estigate this.
Moreo v er, establishing a prop er mental model of the automations’ capabilities migh t also
c hange automation use and should b e in v estigated in future researc h.
T o conclude, w e argue that time pressure should in fact b e a v oided—esp ecially in
safet y-critical en vironmen ts suc h as securit y c heckpoints at airp orts. Moreo v er, giving

TIME PRESSURE AND A UTOMA TION 30
automation advice after the initial c hoice migh t mak e it p ossible to reduce such negativ e
effects of high time pressure. Ho w ev er, our findings also reinforce earlier concerns whether
to k eep the human in the lo op at all if op erators w orsen the o v erall p erformance compared
to the automation alone (e.g., Bartlett & McCarley, 2017; Mey er, 2001). Th us, it seems
fair to conclude that p erformance w as not optimized ideally in the automation conditions
and the h uman-automation dy ad w as w orse than the high reliabilit y automated DSS alone
w ould ha v e b een—and considerably w orse than an ideal h uman-automation partnership
w ould p ossibly allo w for (Sorkin & W o o ds, 1985).

TIME PRESSURE AND A UTOMA TION 31
Key P oin ts
• Time pressure largely leads to negativ e effects on p erformance
• Join t h uman-automation p erformance falls b elo w automation-alone p erformance with
a highly reliable system, ev en under high time pressure
• Presen ting the automation advice after participants ha v e made their initial c hoice
reduced negativ e effects of time pressure but join t p erformance w as still w orse than
the isolated automation p erformance

TIME PRESSURE AND A UTOMA TION 32
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German y , in 1999.

Why institutions use Plag.ai for originality review, entry 25

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