fpsyg-09-00459 March 30, 2018 Time: 16:18 # 1
published: 04 April 2018
Université Libre de Bruxelles, Belgium
Ospedale Niguarda Ca’ Granda, Italy
University of Liège, Belgium
This article was submitted to
a section of the journal
Frontiers in Psychology
Received: 13 October 2017
Accepted: 19 March 2018
Published: 04 April 2018
Ma J, Gu J, Jia H, Yao Z and
Chang R (2018) The Relationship
Between Drivers’ Cognitive Fatigue
and Speed Variability During
Monotonous Daytime Driving.
Front. Psychol. 9:459.
The Relationship Between Drivers’
Cognitive Fatigue and Speed
Variability During Monotonous
Jinfei Ma1, Jiaqi Gu1, Huibin Jia2, Zhuye Yao1and Ruosong Chang1*
1School of Psychology, Liaoning Normal University, Dalian, China, 2Key Laboratory of Child Development and Learning
Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
A lack of task workload can lead to drivers’ cognitive fatigue and vigilance decrement
during a prolonged drive. This study examined the effects of speed variability on
driving fatigue in a monotonous drive. Twenty-one participants participated in a 60-
min simulated driving task. All participants’ cognitive fatigue was assessed using
psychological and physiological measurements. Results showed that among all
participants, variability of vehicle speed was negatively correlated with sleepiness and
hypo-vigilance during the driving task. Further, drivers in the large variability group
reported less sleepiness, less fatigue, and more vigilance than those in the small
variability group did during the driving task. These drivers also presented a smaller
electroencephalogram spectral index (θ+α)/βduring the task, where θ,α, and βare the
power spectra of three different frequency bands: theta (θ, 4∼8 Hz), alpha (α, 8∼13 Hz),
and beta (β, 13∼30 Hz). Our ﬁndings suggested that the larger variability of speed within
the speed limit may have a deterrent effect on drivers’ cognitive fatigue.
Keywords: cognitive fatigue, vigilance, speed variability, self-regulation, simulated driving, fatigue
Over 1.2 million people are killed in the traﬃc accidents each year around the world, with millions
of people suﬀering from severe injuries and living with long-term adverse health consequences
(World Health Organization, 2015). Hypo-vigilance and fatigue have been regarded as the
signiﬁcant contributors for road accidents. Empirical studies have reported that approximately
16–23% of car crashes on the highways in southwest and midland England and 21.9% in Italy were
caused by sleepiness or fatigue (Horne and Reyner, 1995;Garbarino et al., 2001). Albeit precise
contributors of these factors have not yet been determined in crashes, researchers have reached a
consensus that fatigue represents one major road safety hazard. Therefore, it is urgent to develop
eﬀective coping strategies for management of driver fatigue.
Driving is considered a vigilant task, in which the driver needs to maintain a high level of
alertness. May and Baldwin (2009) suggested that drivers’ cognitive fatigue could be either caused
by circadian rhythm and sleep disturbance (i.e., sleep-related fatigue) or caused by cognitive
overload or underload (i.e., task-related fatigue). For task-related fatigue, cognitive overload
commonly is induced by high task demands requiring sustained attention; however, prolonged
driving that oﬀsets driver workload can beneﬁt such form of task-related fatigue. In contrast,
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Ma et al. Cognitive Fatigue and Speed Variability
cognitive underload is induced by continuous and monotonous
driving conditions, which require an increased task novelty and
demand to reduce this form of task-related fatigue (Hancock and
Warm, 1989;Thiﬀault and Bergeron, 2003;Saxby et al., 2013).
Besides, a previous study has concluded that fatigue induced by
cognitive underload would impair a driver’s engagement more
rather than that induced by cognitive overload (Saxby et al.,
2008). Once the demands of driving become more familiar and
monotonous, the driver is more vulnerable to reach an underload
state. According to the theory of attentional resource shrinkage
(Young and Stanton, 2002), an underload state could keep the
arousal at a low level, and lead to vigilance decrements as well
as poor driving performance. Thus, a monotonous prolonged
drive requires more attentional resources that cause driver
vigilance. On the other hand, Hockey’s compensatory control
model (Hockey, 1997) proposed that we constantly regulated our
eﬀort based on the relative importance of goals we had, and
changes in mental eﬀort were representative of task diﬃculty.
Therefore, a monotonous driving task represents reduced task
eﬀort, causing boredom and fatigue.
Further, a study found that drivers’ speed variability became
larger when the monotonous drive lasted for over 80 min
(Gershon et al., 2009). They attributed this phenomenon to the
driver’s speed control capability becoming worse in the state of
cognitive fatigue. However, the task-capability interface model
suggested that the choice of vehicle speed could be inﬂuenced by
the driver’s cognitive workload (Fuller, 2005). Drivers changed
their speed maybe in order to adjust the task diﬃculty actively
and increase their arousal levels. In this study, we investigated
the eﬀect of drivers’ variability of vehicle speed (within the speed
limit) on their cognitive fatigue. Currently, neurophysiological
approaches, like electroencephalogram (EEG) and eye tracking,
are widely employed for evaluating driving cognitive fatigue
(Borghini et al., 2014). Literature has proved that EEG and pupil
diameter can be used as a physiological index for monitoring
the level of drivers’ vigilance (Makeig and Jung, 1995;Jap et al.,
2009;Zhao et al., 2012;Heeman et al., 2013;Pﬂeging et al.,
2016). In particular, the ﬂuctuations in certain EEG band power
are sensitive to the vigilance level (Zhao et al., 2012). For
example, increased EEG alpha power may indicate the vigilance
decrement (Lal and Craig, 2002). Therefore, we employed the
psychophysiological methods (e.g., subjective fatigue state, EEG
index, and eye movements) to measure driver’s cognitive fatigue
during a prolonged simulator-driving task.
MATERIALS AND METHODS
This study was approved by the Research Ethics Committee
of Liaoning Normal University in December 2016. Participants
were recruited via ﬂyers distributed in the local taxi companies
in Dalian, China, from December 2016 to January 2017. We
approached 31 professional drivers by phone. Eventually, 21
healthy male participants (mean age: 40.1 years, age range:
29–47 years), who are licensed drivers, were recruited. All
participants reported having a full license for an average of
15.8 years, driving more than 100,000 km in a typical year, and
having no prior experiences of driving in a simulator. Participants
were instructed to go to bed no later than10 P.M. before the
experiment, and to get up around 7 A.M. on the experimental
day. The experiment started at 9 A.M. It was also ensured that
all participants refrained from consuming caﬀeine or alcohol in
the morning of their visits. Participants who had not had enough
sleep (e.g., less than 8 h) were ruled out from the experiment.
All participants gave their written informed consents before the
experiment and were oﬀered a monetary compensation of 100
RMB at the end of the study.
This experiment took place in a soundproof lab of the university.
When arrived at the laboratory, participants were given a full
instruction of the experiment by the experimenter and a trial
drive in the simulator. Then, they were connected to the EEG
and eye tracker monitors for the recording of physiological
data and completed a 60-min driving task. Before and after the
driving simulation task, participants were required to complete
the questionnaires to assess their subjective fatigue state. In
addition, each participant’s vigilance level was evaluated by the
vigilance measurement at the end of the driving task.
In order to create the monotonous task, participants were
instructed to follow the former vehicle (without overtaking)
and to keep the minimum following distance at 100 m. The
lead vehicle traveled at a constant speed of 70 km/h and could
stop randomly during the experiment. Therefore, participants
were required to maintain a safe distance for preventing a rear-
end collision. During the experiment, the average driving speed
among all participants was 72 km/h, which was consistent with
that of the lead vehicle.
Perceived fatigue induced by the driving task was assessed using
the Chinese version of Swedish Occupational Fatigue Inventory
(SOFI-C) with a 0–10 numerical response scale (Åhsberg et al.,
1997), which has a good validity and reliability in China (Lin
et al., 2012). The questionnaire consists of 25 questions describing
ﬁve dimensions: physical discomfort, physical exertion, lack
of energy, lack of motivation, and sleepiness. In addition, a
retrospective assessment of the driver’s vigilance level with a 1–
9 numerical rating scale was used (Table 1). This measurement
consists of four items, which modify the Karolinska Sleepiness
Scale (KSS) (Åkerstedt and Gillberg, 1990), inattention (ATT),
and monotony (MON) (Schmidt et al., 2009), to assess each
participant’s vigilance level, including the aspects of boredom,
sleepiness, inattention, and monotony with regard to the whole
A ﬁxed-based car driving simulator (QJ-3A1; Beijing Sunheart
Simulation Technology, Co., Ltd.) was employed in this study.
The road scene of the driving simulator used in the experiment
was set as a monotonous highway condition, as shown in
Figure 1. The driving scenario was a two-lane closed track,
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Ma et al. Cognitive Fatigue and Speed Variability
TABLE 1 | Measurement of the drivers’ vigilance level.
Concerning the period of the whole drive
1. . .how would you describe your predominant state?
Extremely alert Alert Neither alert nor sleepy Sleepy, but no difﬁculty
1 2 3 4 5 6 7 8 9
2. . .how attentively have you been driving?
Extremely attentively Attentively Neither attentively nor inattentively Inattentively Extremely inattentively
1 2 3 4 5 6 7 8 9
3. . .how did you perceive the drive?
Extremely varied Varied Neither varied nor monotonous Monotonous Extremely monotonous
1 2 3 4 5 6 7 8 9
4. . .how did you feel about the driving task?
Extremely interesting Interesting Neither interesting nor boring Boring Extremely boring
1 2 3 4 5 6 7 8 9
5.25 km long, with 10 curves. Driving performance measures
including standard deviation (SD) of lane position, mean and SD
of speed, and car following distance were recorded during the
whole driving task.
Considering that the adaptation period to the driving
simulator was about 15.4 min in the curved-road scenarios
and about 12.1 min in the straight road scenarios (Ronen
and Yair, 2013), we deﬁned the ﬁrst 15 min of the driving
FIGURE 1 | The road scene of the driving simulator.
test as the adaptation period. Therefore, the whole driving
test session (considered as the dependent variable) was split
into three segments with 15 min per segment: segment 1,
segment 2, and segment 3. Each participant was required to
drive three runs per segment under the experimental road
Eye Movement Measures
Eye movement data were recorded using the head-mounted Tobii
Glasses II eye tracking system (Tobii, Sweden), which allowed
free movement of the head. The sample frequency of the eye
tracker was 50 Hz with a 82◦×52◦recording visual angle.
Eelectroencephalogram signals were recorded continuously
using a portable 63-channel ‘eego ampliﬁer’ EEG system
(eegoTMmylab, ANT B.V., Netherlands) with an extended 10–20
system layout. The sensor net was aligned with respect to three
anatomical landmarks including two pre-auricular points and the
nasion. Impedances were kept below 10 k, and the sampling
rate was 1000 Hz. The electrode CPz was used as the reference,
whereas, the electrode AFz was used as the ground.
Eye Movement Data Preprocessing
Eye movement measures involved three indices: pupil diameter
(unit: mm) and horizontal and vertical positions of each gaze
(unit: pixels). The positions of each gaze were rotations around
the horizontal axis and the vertical axis.
EEG Data Preprocessing
Electroencephalogram data were pre-processed using EEGLAB
(Delorme and Makeig, 2004), an open source toolbox running
in the MATLAB (v.2010a; The MathWorks, Inc., United States)
environment, and in-house MATLAB functions. Continuous
EEG data were band-pass ﬁltered between 0.5 and 30 Hz and the
sampling rate decreased to 250 Hz. EEG data were referenced
to the average of both mastoids (M1, M2). EEG data were
removed 30 s before and after a braking action, due to the huge
movement of the participant. Data portions contaminated by eye
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Ma et al. Cognitive Fatigue and Speed Variability
movements, electromyography, or any other non-physiological
artifacts were corrected using the Independent Component
Analysis algorithm (Makeig et al., 1997;Jung et al., 2001). Then
the pre-processed continuous EEG data were segmented into
dozens of epochs, with an epoch length of 2000 ms. EEG epochs
contaminated by strong muscle artifacts or with amplitude
values exceeding ±100 µV at any electrode were manually
To ensure the ecological validity of our experiment, this study
did not strictly control participants’ head movements during the
task. Therefore, participants having poor quality of EEG data
(<80% good data) would be excluded. As a result, all datasets
were available for the power spectral analysis.
EEG Power Spectrum Estimation
For each participant, the segmented EEG epochs were subjected
to the power spectral density analysis by fast Fourier transforms
(50% Hamming window) and were exported for analysis in
a frequency resolution of 0.5 Hz (range: 0.5–30 Hz). The
power spectrum of each frequency point was averaged over
the epochs. Then the power spectrum of four frequency bands
was computed as the mean value within each frequency limit:
delta (δ, 0.5∼4 Hz), theta (θ, 4∼8 Hz), alpha (α, 8∼13 Hz),
and beta (β, 13∼30 Hz) waves. Previous research suggested that
the increased EEG algorithm (θ+α)/βwas an eﬀective indicator
for detecting drivers’ fatigue (Jap et al., 2009). Therefore, this
ratio was chosen as a factor that assessed drivers’ fatigue
According to the scalp topography of each band power,
the largest power of delta, theta, alpha, and beta bands were
consistently shown in the frontal, central, and parietal lobes
during the whole driving task, and the power of each band was
distributed symmetrically on bilateral hemispheres. Meanwhile,
the artifact in the frontal lobe was larger than that in other
scalp regions, as shown in Figure 2. Therefore, this study chose
data from C1, C2, CP1, CP2, P1, and P2 electrodes for further
In this study, Pearson’s correlation analysis was employed
to investigate correlations between changes of subjective
fatigue (i.e., post-task–pre-task), vigilance level, EEG data,
driving performance, and eye movement data. Thereafter,
all participants were divided into two groups, according
to the participant’s driving speed variability. The average
speed variability among all participants was 19.67 km/h
(SD = 2.36 km/h). Eventually, there were 11 participants in the
small speed variability group (speed variability = 17.63 km/h),
whereas there were 10 participants in the large speed variability
group (speed variability = 21.40 km/h). This study adopted
the student’s independent-samples t-test to analyze the
group diﬀerences of the subjective fatigue and the drivers’
FIGURE 2 | Electroencephalogram (EEG) power spectra for frequencies between 0.5 and 30 Hz during the driving task. Scalp topographies are displayed at the
greatest power of delta (0.5∼4 Hz), theta (4∼8 Hz), alpha (8∼13 Hz), and beta (13∼30 Hz) bands for the adaption period, segment 1, segment 2, and segment 3.
The delta, theta, and alpha band power shows a positive maximum over the fronto-central electrodes and a negative maximum over bilateral parietal electrodes in
each segment, whereas the beta band power shows a positive maximum over the frontal electrodes and a negative maximum over bilateral parietal electrodes.
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Ma et al. Cognitive Fatigue and Speed Variability
vigilance level. Driving performance, eye movement, and
EEG data were analyzed using a 2 (speed variability) ×3
(driving session) repeated-measures analysis of variance
(ANOVA). In order to reduce the risk of a type one error
in multiple comparisons, Bonferroni-Šidák’s adjustments of
p-values were applied. A 95% conﬁdence level was employed
throughout. Statistical analyses were carried out using the SPSS
18.0 statistical analysis package (p≤0.05; SPSS, Inc., Armonk,
NY, United States).
Correlations Between Variables
Table 2 shows correlations between all measures among all
participants. Among all participants, variability of speed (i.e.,
average of SD of speed during the whole drive) was signiﬁcantly
negatively correlated with sleepiness [r(19) = −0.50, p<0.05],
and vigilance level [r(19) = −0.53, p<0.05]. Results suggested
that participants with greater speed variability reported to be
less sleepy and more vigilant after the monotonous driving
task. Among all participants, the pupil diameter (during
segment 3) was signiﬁcantly negatively correlated with sleepiness
[r(19) = −0.62, p<0.01], total fatigue [SOFI; r(19) = −0.46,
p<0.05], and vigilance level [r(19) = −0.45, p<0.05]. Results
indicated that participants having larger pupil diameters during
segment 3 of the driving task reported to be less sleepy, less
tired, and more vigilant. Among all participants, the EEG index
(during segment 3) was signiﬁcantly positively correlated with
total fatigue [r(19) = 0.55, p<0.05], lack of energy [r(19) = 0.58,
p<0.01], lack of motivation [r(19) = 0.48, p<0.05], and
sleepiness [r(19) = 0.58, p<0.01]. Results indicated that
participants showing greater EEG activities reported to have less
energy and less driving motivation and to be more tired and more
sleepy after a prolonged drive.
Among all participants, signiﬁcant diﬀerences were found in
all ﬁve dimensions and the total scale of the SOFI (p<0.05),
where the pre-task scores were signiﬁcantly lower than the
post-task ones. For the group diﬀerence analysis, the student’s
independent-samples t-test showed that there were no signiﬁcant
diﬀerences in pre-task scores on each dimension and neither on
the total scale of the SOFI between two groups with diﬀerent
speed variabilities (p>0.05; Table 3). The student’s independent-
samples t-test, by assessing the changes (i.e., post-task–pre-task)
on each dimension and on the total scale of the SOFI, showed
that they were of statistical signiﬁcance between groups when
looking at sleepiness [t(19) = 2.10; p= 0.05; η2= 0.188] as
well as of marginal signiﬁcance in total fatigue [t(19) = 1.89;
p= 0.07; η2= 0.159]. To be speciﬁc, participants with large
variability of vehicle speed had smaller changes in sleepiness
and total fatigue than those with small variability of vehicle
speed. Besides, there were signiﬁcant diﬀerences between groups
in vigilance level [t(19) = 2.32; p<0.05; η2= 0.221] after
a prolonged drive, indicating that the large speed variability
group was more vigilant during a monotonous drive. However,
there were no statistically signiﬁcant group diﬀerences in lack
of energy, physical exertion, physical discomfort, and lack of
For all participants, the one-way repeated measures ANOVA
(four driving segments: adaptive period and three driving
sessions) showed that there was no signiﬁcant main eﬀect
of driving segment [F(1,19)= 1.23, p>0.05; Table 4].
This result suggested that participants showed stable speed
variability during the whole driving task. After splitting all
participants into two groups (i.e., small vs. large speed variability),
the two-way repeated measures ANOVA showed that there
was a signiﬁcant main eﬀect of groups during the whole
task [F(1,18)= 20.92, p<0.001, η2= 0.538; Table 4]. The
main eﬀect of the driving segment and its interaction with
the group were not statistically signiﬁcant [F(1,18)= 1.25,
p>0.05]. Analysis of the SD of lane position, average of
speed, and car following distance showed no signiﬁcances of
main eﬀects or interactions between group and driving segment
TABLE 2 | Correlations for measurements of standard deviation of vehicle speed, each dimension and total scale of the SOFI (post–pre test scores), vigilance level
(post-test scores), EEG algorithm (θ+α)/β(segment 3), and pupil diameter (segment 3).
Variables 1 2 3 4 5 6 7 8 9
(1) SD of speed −
(2) LOE −0.23 −
(3) PE −0.11 0.67∗∗ −
(4) PD −0.17 0.69∗∗ 0.82∗∗ −
(5) LOM −0.35 0.76∗∗ 0.53∗0.74∗∗ −
(6) Sleepiness −0.50∗0.68∗∗ 0.31 0.49∗0.72∗∗ −
(7) SOFI −0.33 0.90∗∗ 0.77∗∗ 0.88∗∗ 0.90∗∗ 0.77∗∗ −
(8) Vigilance −0.53∗0.59∗∗ 0.56∗∗ 0.46∗0.50∗0.60∗∗ 0.64∗∗ −
(9) Pupil diameter 0.27 −0.38 −0.30 −0.27 0.34 −0.62∗∗ −0.46∗−0.45∗−
(10) EEG (θ+α)/β−0.30 0.58∗∗ 0.23 0.41 0.48∗0.58∗∗ 0.55∗0.34 −0.32
∗p<0.05; ∗∗ p<0.01. SD of speed, standard deviation of speed; LOE, lack of energy; PE, physical exertion; PD, physical discomfort; LOM, lack of motivation; SOFI,
Swedish Occupational Fatigue Inventory.
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TABLE 3 | The descriptive data on the scale of SOFI and vigilance level (M±SD).
Pre-test Post-test Pre-test Post-test
Lack of energy 1.24 ±1.88 4.18 ±2.47 0.26 ±0.75 1.98 ±2.49
Physical exertion 0.76 ±0.62 2.73 ±2.32 0.60 ±1.05 1.52 ±1.83
Physical discomfort 0.49 ±0.78 3.31 ±3.14 0.42 ±1.13 1.52 ±1.73
Lack of motivation 1.82 ±2.05 5.00 ±2.17 0.76 ±1.61 2.42 ±2.46
Sleepiness 1.62 ±2.50 4.89 ±2.56 0.68 ±1.23 1.86 ±1.54
SOFI 1.19 ±1.38 4.02 ±2.35 0.54 ±1.06 1.86 ±1.87
Vigilance −6.45 ±1.81 −4.70 ±1.64
SOFI, Swedish Occupational Fatigue Inventory.
Our 2 ×3 experimental design used the variability of vehicle
speed (i.e., small vs. large speed variability group) as the
independent variable and driving segment (i.e., segment 1,
segment 2, and segment 3) as the dependent variable. As
shown in Figure 3, a two-way repeated measures ANOVA
on the EEG algorithm (θ+α)/βyielded a signiﬁcant main
eﬀect of the groups [F(1,19)= 4.79, p<0.05, η2= 0.201].
Results indicated that participants in the small speed variability
group had a greater EEG activity than those in the large
speed variability group. The main eﬀect of driving segment
and interactions between group and driving segment were
not statistically signiﬁcant (p>0.05). In addition, the
student’s independent-samples t-test showed that there
was no signiﬁcant diﬀerence in EEG data between two
groups during the adaption period (p>0.05), suggesting
that participants in two diﬀerent speed variability groups
had similar fatigue state at the beginning of the driving
Eye Movement Data
As shown in Figure 4, a two-way repeated measures ANOVA
on pupil diameter yielded the signiﬁcant main eﬀect of driving
segment [F(1,19)= 4.79, p<0.05, η2= 0.201], showing that
all participants’ pupil diameters became smaller as the driving
duration increased. However, the main eﬀect of variability
of vehicle speed and its interactions with driving segment
were not statistically signiﬁcant (p>0.05). In addition, the
Student’s independent-samples t-test showed that there was
no signiﬁcant diﬀerence in the pupil diameter between two
groups during the adaption period (p>0.05). Horizontal and
vertical spread of attention showed no signiﬁcance of main
eﬀects or interactions between group and driving segment
In this study, the psychological and physiological data conﬁrmed
that the 60-min prolonged monotonous driving task successfully
elicited driver’s cognitive fatigue, indicating an underload
state (Desmond and Hancock, 2001). In addition, drivers’
speed variability was associated with vigilance decrement and
cognitive fatigue. Speciﬁcally, drivers with large speed variability,
compared with those with small speed variability, had less
sleepiness, more vigilance, and a smaller EEG algorithm
Diﬀerent from previous studies (Charlton and Starkey,
2011, 2013), we observed that the drivers’ speed variability
was not signiﬁcantly diﬀerent in diﬀerent driving segments.
This may be due to the short driving period (1 h in
total), during which the drivers’ speed variability did not
decline signiﬁcantly. Indeed, our study demonstrated stable
individual diﬀerences of driver’s speed variability, which
could last for the whole driving task. Thus, our results
conﬁrmed that the driving speed preference varied among
Our study found that during the driving task, drivers with
smaller pupil diameter displayed less sleepiness, less fatigue,
and more vigilance, whereas, those with stronger EEG activities
had less energy, less driving motivation, and more fatigue.
It has been well-known that cognitive fatigue may induce
alterations of the EEG band power (Zhao et al., 2012). These
neurophysiological indicators supported the dynamic model of
stress and sustained attention, claiming that a monotonous drive
can induce an underload state (Hancock and Warm, 1989). Our
ﬁndings also reported that drivers with larger speed variability
were less sleepy and more vigilant during a monotonous drive.
Larger speed variability indicates a larger degree of changes
in driving speed, which requires more attentional resources
and increases mental workload. Additionally, driving behaviors,
like speed variability, are sensitive to a driver’s alertness state
(Ghosh et al., 2015). According to Hancock and Warm’s (1989)
theory, increasing the workload can help drivers maintain the
optimal arousal level. Therefore, our ﬁndings may suggest
that driving behaviors, in particular speed variability, may
have positive impacts on cognitive fatigue in an underload
Cognitive fatigue is considered to be associated with vigilance
decrement. In our study, the large speed variability group
reported less sleepiness and more vigilance, compared with the
TABLE 4 | Variability of vehicle speed in the adaptive period and three driving sessions (M±SD).
Adaption period Segment 1 Segment 2 Segment 3
All participants (n= 21) 20.06 ±2.79 18.89 ±3.43 19.14 ±2.20 18.84 ±3.97
Small speed variability group (n= 11) 18.45 ±2.76 17.00 ±3.44 18.27 ±2.23 16.82 ±3.35
Large speed variability group (n= 10) 22.02 ±1.06 21.20 ±1.48 20.20 ±1.71 21.31 ±3.29
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FIGURE 3 | Comparisons of the EEG index between participants with small
vs. large variability of speed in three driving segments. Three 15-min driving
segments respectively correspond to segment 1, segment 2, and segment 3.
Small variability represents participants with small speed variability, whereas
large variability represents participants with large speed variability. Data from
small and large speed variability groups are marked in black and gray,
respectively. Error bars represent the standard errors of the means.
FIGURE 4 | Comparisons of the pupil diameter between participants with
small vs. large variability of speed in three driving segments. Three 15-min
driving segments respectively correspond to segment 1, segment 2, and
segment 3. Small variability represents participants with small speed variability,
whereas large variability represents participants with large speed variability.
Data from small and large speed variability groups are marked in black and
gray, respectively. Error bars represent the standard errors of the means.
group with small speed variability. Psychological results further
supported that drivers with large speed variability may keep
themselves in an optimal arousal level during a monotonous
drive, thereby reducing driving fatigue. Furthermore, our
neurophysiological data showed that drivers who had a large
variability of vehicle speed showed a signiﬁcantly smaller EEG
algorithm (θ+α)/βduring driving segments than those with
small speed variability. Several EEG studies have concluded the
diﬀerent roles of the EEG power band in the evaluation of
cognitive fatigue (Okogbaa et al., 1994;Lal and Craig, 2002;
Zhao et al., 2012). In driving duration, increased alpha activity
implies decreased attention, whereas increased theta activity
signals the onset of sleep. In contrast, decreased beta activity
indicates the decrement of cortical arousal level. The EEG
algorithm (θ+α)/βis considered as one of the most eﬀective
physiological indices to measure the driving fatigue. The smaller
EEG algorithm (θ+α)/βis, the less fatigue the driver has
(Jap et al., 2009). Therefore, the large speed variability group
with smaller EEG algorithm (θ+α)/βmay have less cognitive
fatigue. This further proved the suggestion of the dynamic
model (Hancock and Warm, 1989): in an underload condition,
increasing workload, for example regulating speed variability, can
help maintain vigilance and resist cognitive fatigue. Meanwhile,
our results also implied that drivers could maintain vigilance
through manipulating driving behaviors rather than employing
extra cognitive tasks. For example, drivers can spontaneously
regulate their vehicle speed variability to increase vigilance.
According to Hockey’s (1997) compensatory control model, a
driver’s self-regulation of vehicle speed is initiatively based on
the task diﬃculty and the workload. Experienced drivers are able
to control the resources of cognition and attention, which help
them maintain optimal performance using the speed adjustment
strategy. Our study indicated that drivers who used better speed
adjustment strategies had lower levels of subjective and physical
Consistent with the previous study, our study found that
the pupil diameter of participants gradually became smaller, as
they became more sleepy and less vigilant during the driving
task, indicating that participants’ fatigue states were deepened
(Heitmann et al., 2001). However, there were no signiﬁcant
group diﬀerences in pupil diameter. The possible reason may
be that drivers with larger variability of vehicle speed had more
workload of processing visual information, thereby increasing the
contractility of pupil diameter that resisted their fatigue.
The present study indicated that among the drivers, some
preferred larger variability of vehicle speed, whereas others
preferred the smaller one during a prolonged drive. However,
drivers with large variability of vehicle speed had less sleepiness,
less fatigue, and more vigilance in the driving task. These
results were consistent with the dynamic model of stress and
sustained attention, indicating that drivers with larger speed
ﬂuctuation could increase workload that helps them maintain
the optimal arousal level in monotonous highway conditions.
Physiological data also proved that drivers with large variability
of vehicle speed had a smaller EEG algorithm (θ+α)/β. These
results proposed that manipulating driving behaviors, like speed
variability, could have a positive eﬀect on coping with driving
fatigue and maintaining vigilance in the underload condition.
Future studies need to combine drivers’ individual diﬀerences,
road conditions, and the diﬃculty of driving tasks with the
self-regulation strategy to further validate the eﬀect of driving
behaviors on cognitive fatigue.
This study was carried out in accordance with the
recommendations of the ethical review committee of Liaoning
Normal University with written informed consent from all
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Ma et al. Cognitive Fatigue and Speed Variability
subjects in accordance with the Declaration of Helsinki. The
protocol was approved by the ethical review committee of
Liaoning Normal University.
JM and RC conceived and designed the experiments, and
wrote the manuscript. JM and JG were involved in the
data collection. JM and HJ were involved in the data
analysis. RC and ZY made critical comments and revised the
This study was funded by 2016 Liaoning Doctor Scientiﬁc
Research Initiation Foundation Program (201601242) and
Liaoning Education Department Humanistic Society Scientiﬁc
Research Program (W201683617).
We appreciate Mrs. Lirong Xia from Shanghai Psytech Electronic
Technology, Co., Ltd., for her help and technical support.
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Conﬂict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or ﬁnancial relationships that could
be construed as a potential conﬂict of interest.
Copyright © 2018 Ma, Gu, Jia, Yao and Chang. This is an open-access article
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Frontiers in Psychology | www.frontiersin.org 9April 2018 | Volume 9 | Article 459