2. 1
1. INTRODUCTION
Recent evidence shows that novice drivers are more likely than experienced drivers to be involved in car
crashes, mainly due to their poor hazard anticipation abilities (e.g., [1]; [7]; [8]). Hazard anticipation or hazard
perception may be defined as drivers' ability to read the road and anticipate hazardous situations [11]. As any
skill, hazard perception improves with practice [9]. A large body of research has shown that novice drivers
can improve their abilities to anticipate hazards to some extent through training [8]. To date, most training
programs have been based on explicit, deliberate learning, in which novice drivers were explicitly told what
kind of hazards they should look for and where they should look for them (e.g., [4]; [12]). Considering the fact
that hazard anticipation improves as driving experience accumulates it may well be that learning to anticipate
hazards can be facilitated by using implicit-based, procedural training. The current study investigated whether
novice drivers and experienced drivers can learn to anticipate hazards through implicit procedural learning.
Implicit procedural learning is usually defined as the non-deliberate acquisition of dynamic patterns of
behavior that ultimately lead to implicit memory [14]. In procedural learning, it is difficult to explain precisely
when or how the specific knowledge is encoded into memory, but knowledge acquisition can be
demonstrated through the detection of an ability that did not exist prior to the learning process or through the
improvement of an existing ability. Procedural knowledge that is acquired implicitly is widely reflected in our
performance of everyday activities such as walking, running, and catching objects that are unexpectedly
thrown at us. Implicit procedural learning requires no special allocation of attention [6], and in at least several
cases, implicit learning is the foundation of automatic behavior [18]. This type of learning occurs passively,
incidentally, and automatically: No conscious effort is made to absorb the knowledge, and no comprehension
of its contents is required.
Implicit procedural learning has numerous advantages: it is not age- or IQ-dependent, and knowledge
acquired via this method is more stable and resilient than knowledge acquired through explicit learning [15].
Information conveyed implicitly to participants is converted into skills that are usually more easily retrievable
than skills acquired in an explicit learning process involving comprehension and memory [18]. Implicit
procedural learning also has better ecological validity than other training methods, as most everyday
activities encompass implicit and unconscious form of learning. Hence, implicit procedural training
methodology may be at preference when applied to the improvement of driving skills and hazard perception,
over existing training methods that teach hazard perception skills in an explicit manner, since driving skills,
and especially hazard anticipation skills are typically acquired implicitly.
A typical way to evaluate hazard perception abilities is to use short video clips of real world situations
presented on a computer screen where drivers are asked to press a response button each time they identify
a hazard (e.g., [13]; [2]; [4]; [10]). Those videos are usually taken from the perspective of a driver driving
down the road. The selected hazardous situations vary from one study to another but in general they are all
used to examine whether drivers identify the hazard (pressing a response button) and how fast they respond.
Some studies also use an eye tracking system to examine the way drivers scan the road and whether or not
they fixated on the hazard instigator ([5]; [2]).
Recently, based on their findings with regard to hazard that best differentiate between novice and
experienced drivers, Borowsky and his colleagues proposed a new taxonomy to describe road hazards that
3. 2
is based on two factors ([1]; [2]). The first is whether the hazard is unmaterialized (i.e., potential) or
materialized. Materialized hazard is defined as a hazard that calls for a driverβs immediate response in order
to prevent a crash (such as a bicyclist on the sidewalk who suddenly burst into the driver's travel lane). An
unmaterialized hazard is defined as a hazard source that should be monitored but it may or may not
materialize eventually (e.g., a bicyclist on the sidewalk who remains on the sidewalk throughout the
scenario). The second factor is whether the hazard instigator is visible or hidden when the hazardous
situation begins. A source of danger that is hidden by other road users or environmental factors is considered
a hidden hazard (e.g., a pedestrian who is obscured behind parked cars). Likewise, a source of danger that is
visible to the driver is considered a visible hazard (e.g., a pedestrian who is about to cross the road but there
is nothing that obscures him or her). These two factors can be combined and create four types of hazards
(i.e., hidden materialized, hidden unmaterialized, visible unmaterialized and visible materialized). A hidden
materialized hazard is a situation where the source of danger is hidden by other road users or environmental
factors and is therefore unobservable, but as the situation evolves, the source of danger becomes visible for
an immediate response by the driver (such as a pedestrian who is first obscured behind a parked bus but at a
certain point in time walks into the road).
With respect to the proposed taxonomy, it has been demonstrated that novice drivers successfully identify
and respond quickly to materialized hazards and are not very different in their performance compared with
experienced drivers. Addressing hidden unmaterialized hazards, such hazards typically requires deeper
understanding of the situation in order to predict the areas from where a hazard might appear and therefore
require pre-established schemata (usually acquired through experience) and greater cognitive resources.
Consequently, unexperienced drivers find it difficult to identify hidden and potential hazards ([1]; [2]; [17]).
The aim of the current study is to examine whether novice drivers' abilities to anticipate road hazards can be
improved by using a novel training methodology that is based on principles of implicit procedural learning. In
the proposed training method participants were connected to an eye tracker and viewed short video clips of
various driving situations encompassing all four types of hazards described above. During implicit training
procedure, each video clip was presented to the trainee four times (in a randomized order and embedded
within other movies). Participants were asked to press a response button each time they identify a hazard.
The training set was followed by a hazard perception post training set, in which participants viewed a series
of new video clips representing the same type of hazards presented in training, where each clip is shown only
once. Participants task was similar to that in the training set.
2. HYPOTHESES
2.1. Compare to untrained novice drivers, trained novice drivers will show same baseline hazard
identification abilities, but while hazard perception skills of untrained novice drivers will show a minor
or no change over the course of the experiment novice trained drivers will demonstrate gradual
improvement during and after training, at the post-training (test) stage: Eye movements of the novice
trained drivers will demonstrate more fixation toward the hazards, earlier fixations latencies, and
reduced dispersion of the fixations locations. In addition, regard to the behavioral responses trained
novice drivers will demonstrate higher hazard identification rate and shorter latencies
2.2. Compared to trained novice drivers, experienced trained drivers who have already gained sufficient
driving experience and therefore can hardly benefit from such training, will show a better hazards
identification achievements at the baseline stage before training, and will benefit less from the
4. 3
training stage, so the baseline gap will diminish at the end of the training process. Nevertheless, we
hypotheses that the performance gap between experienced and novice drivers will remain with
regard to the untrained group.
2.3. Finally, we hypotheses that novice trained drivers will demonstrate better training outcomes when the
hazard type is materialized compare to unmaterialized (potential) hazards, which require higher order
cognitive demands such as anticipation, and prediction.
3. NETHOD
3.1. Participants
Fifty-four drivers (30 males and 24 females) participated in this study as paid volunteers Twenty-nine were 17
to 18 years old (M=17.8, SD=0.4) novice drivers, with an average driving experience of 11 months. Twenty-
six participants were 23 to 40 years old experienced drivers (M=33, SD=3.4), with driving experience of at
least 5 years. Novice drivers were randomly assigned into two conditions: Fifteen novice drivers underwent
hazard perception training while 14 novice drivers used as a control and did not undergo any training. All
experienced drivers underwent hazard perception training.
3.2. Materials
Participants observed all video clips on a Laptop display (17" LCD) with a resolution of 1360*768. All
participants had a visual acuity of 6/9 or better. Participants were also connected to an SMI iView 125Hz
RED eye tracking system with an estimated accuracy of 1degree visual angle. This ETS model is a portable
system installed on a laptop with E-PRIME 2.0 software (Psychology Software Tools Inc., Pittsburgh, PA,
USA) Participants sat at an average distance of 65 cm from the screen. Participants were also requested to
press a response button every time they identified a hazard that required their response. All participants
received the same hazard definition according to the one used in Borowsky's et al. (2010) study.
3.3. Experimental design
Both driver groups who underwent training viewed a series of 16 short video clips. This set included four
videos of hazardous situations, each representing a different type of hazard; visible materialized hazards,
hidden materialized hazards, hidden unmaterialized hazards and visible unmaterialized hazards. Each of the
four videos was presented four times; In addition, 15 filler scenarios were also included. The filler scenarios
were added to reduce familiarity effects, and maintain the ecological validity of the implicit learning
procedure. Four alternate order forms of target movies were assigned randomly to the participants. The
unexperienced driver group that did not undergo training viewed a shorter control video set, containing the
four target video clips and four filer scenarios only once. Finally, in the test stage, all participants viewed a set
of three new hazardous video clips that did not appear in the training set and four new filers.
5. 4
3.4. Procedure
The experiment was granted ethical approval by the University of Bar Ilan in Israel. All participants first
signed a consent form, and then completed demographic and driving background questionnaire. Participants
were then asked to complete the hazard perception training set and testing set according to their
experimental conditions. The four alternate order forms of the hazard perception training and testing sets
were counterbalanced across the experimental groups (trained experienced drivers, trained novice drivers
and untrained novice drivers). All participants viewed the experiment instructions before the experiment
began. Next, participants in the trained groups viewed the training sets, and participants in the untrained
group viewed the control video set. Finally, all participants observed the post-training hazard perception test
and were then debriefed. The full procedure took about 40 min, without breaks.
3.5. Statistical Analysis
All analyses were performed using SPSS Statistics Version 22.0. Alpha was set at 5%. The dependent
variable hazard identification was measured by behavioral response (if the participant pressed the space
bar), reaction time (the frame number of the first space bar pressing), fixation latency (the frame number of
the first fixation toward the target), and fixation position dispersion (Sd. of the fixations location along the X
and Y axis during the appearance of the target). Learning slope was measured by changes in those variables
along the training stages and at the test stage. Analyses were conducted separately for each dependent
variable. The independent fixed variables were the between factor group (trained experienced drivers, trained
novice drivers, untrained novice drivers), repeated factor training (pre-training stage, three repetitions stages,
test stage), and the repeated factor hazard appearance sections (created by division of the time of the target
appearance to 3- 5 sections). Subjects were included as a random variable to represent performance
variability of each participant. We evaluated the fixed and random effects by carrying out a Generalized linear
mixed-model design analysis (GLMM), with binary logistic regression method when the dependent variable
was binary or with analysis of variance (ANOVA) when the dependent variable was continuous and normally
distributed.
4. RESULTS
4.1. Analysis of materialized hazards:
Considering the behavioral measures, throughout the training stage both trained groups (novice and
experienced) demonstrated high response rate (between 0.80-0.90), and a gradual decline in reaction time
toward hidden materialized hazard (π π,ππ = π. ππ, π = π. πππ). Eye movements analysis revealed that during
the beginning of the training stage the novice and experienced drivers had similar high identify rating but
novice drivers demonstrated grater identification probability (0.95 vs 0.86, π π,πππ = π. ππ, π = π. ππ) at later
stages of training, suggesting that although both groups reported that they identified the hazard the novice
group kept on focusing on the hazard throughout all training stages, while experienced drivers who identified
it at first were able to shift their attention to other areas of the scene as well.
6. 5
During the testing stage the experienced group demonstrated higher probability (M=0.8, SE=0.09), to identify
hidden materialized hazard (π πππ = π. ππ, π = π. ππ) compare with untrained novice drivers (M=0.18,
SE=0.11). Although the trained novice drivers achieved better results (M=0.49, SE=0.17) than the untrained
group, the differences between those two groups was not significant. Nevertheless, there was also no
difference between the experienced drivers and the trained novice drivers groups.
Results were consistent when we compared each group in terms of their fixations dispersion along the X axis
during the hazardous situation. Mixed Model analysis (LMM) with standard deviation of fixations location
toward visible materialized hazard, along the horizontal axis as a dependent variable revealed a gradual
decline in the horizontal dispersion of novice drivers' fixations, compared to an elevated dispersion for the
experienced group (π π,πππ = π. ππ, π = π. ππ). The differences between groups reached a significant level at
the final, fourth stage of training, when the experienced group showed a higher dispersion (M=127.8,
SE=85.34) compared with the novice group (M=79.98, SE=58.98) (p=0.009). Results are demonstrated in
table 1.
Novices
untrained
Novices
trained
Experienced
trained
N 15 16 26
training phase
mean (Sd) number of fixations toward
materialized visible hazard
repetition 1 2.42(0.97) 6.46(3.27) 5(2.11)
repetition 2 8.57(5.03) 5.46(2.67)
repetition 3 7.5(5.19) 5.4(2.81)
repetition 4 7.64(4.01) 4.64(2.43)
mean (Sd) number of fixations toward
materialized hidden hazard
repetition 1 5(4.03) 7.26(4.39) 6.07(4.05)
repetition 2 4.86(3.7) 5.48(3.54)
repetition 3 5.78(4.8) 5.92(4.38)
repetition 4 5.93(4.93) 5.23(3.37)
mean (Sd) first fixation start time (sec) toward
materialized visible hazard
repetition 1 4.20(0.51) 3.96(0.17) 4.03(0.24)
repetition 2 3.96(0.13) 4(0.23)
repetition 3 3.93(0.20) 4.08(0.26)
repetition 4 3.98(0.24) 4.04(0.28)
mean (Sd) first fixation start time (sec)toward
materialized hidden hazard
repetition 1 3.78(0.74) 3.6(0.4) 3.58(0.28)
repetition 2 3.83(1) 3.64(0.48)
repetition 3 3.53(0.36) 3.5(0.15)
7. 6
repetition 4 3.61(0.49) 3.77(0.66)
mean (Sd) Sd of the fixations location toward
materialized visible hazard
repetition 1 129.22(108.64) 138.88(78.78) 102.71(53.76)
repetition 2 111.03(66.71) 120.29(66.56)
repetition 3 85.38(77.96) 100.83(52.85)
repetition 4 79.98(58.98) 127.8(85.34)
mean (Sd) Sd of the fixations location toward
materialized hidden hazard
repetition 1 103.84(44.90) 67.30(58) 63.31(24.31)
repetition 2 63.14(21.84) 61.70(29)
repetition 3 54.26(16.11) 63.33(25.01)
repetition 4 72.73(48.85) 61.27(27.66)
Test phase (generalization)
materialized visible hazard
mean (Sd) number of fixations toward visible
materialized hazard
1.56(1.41) 4.13(4.24) 2.26)
mean (Sd) first fixation start time (sec)
10.84(0.22) 10.95(0.76) 10.92(0.54)
mean (Sd) (Sd) Sd of the fixations location
139.55(81) 138.80(78.93) 194.06(85.70)
materialized hidden hazard
mean (Sd) number of fixations toward hidden
materialized hazard
2.06(2.08) 3.4(3) 5.44(4.32)
mean (Sd) first fixation start time (sec)
15.68(0.62) 15.78(0.61) 15.60(0.28)
mean Sd (Sd) of the fixations location
90.81(29.4) 82.53(28.09) 74.73(25.32)
8. 7
Table. 1: Estimated mean (Sd) of the fixations measures toward materialized hazards by
groups, phases and repetitions stages
These results indicate that experienced drivers were more likely than the novice group to shift their focus
from the materialized hazard during the first and second presentations of the hazard to other elements within
the same traffic scene. Nevertheless, the fact that all participants were likely to report the hazard at the same
rate across all repetitions imply that young novice drivers were less willing to shift their attention to other
areas of the scene and kept focusing on the hazard instigator throughout repetitions.
4.2. Analysis of unmaterialized (potential) hazards:
Response analysis revealed that all groups responded at a similar rate which was much lower on average
compared with response to materialized hazards. Lower response rate was demonstrated both for the
training and testing sessions. Analyzing only those participants who responded to a given hazard revealed
that experienced drivers tended to respond approximately one second earlier than young-novice trained
drivers toward unmaterialized hidden hazard. This result was, however, non-significant. Additionally, on both
groups there was a gradual decline in reaction time toward unmaterialized hidden hazard (π π,π
= ππ. ππ, π =
π. ππ). Looking at the number of fixations that drivers allocated to the area from where the hazard might
appear revealed that experienced drivers tended to fixate on the hazard more often (M=0.22, SE=0.03) than
young-novice drivers (M=0.09, SE=0.02) ( π π,πππ = π. ππ, π = π. πππ). In-depth examination of the results
revealed that those fixation rate differences between experienced and young-novice trained drivers were
statistically significant only during the third repetition (π πππ = π. ππ, π = π. ππ) and the fourth repetition (π πππ =
π. ππ, π = π. πππ). Interestingly, Mixed Model analysis (LMM) with standard deviation of fixations location,
toward hidden unmaterialized hazard, along the horizontal axis as a dependent variable revealed a gradual
decline in the horizontal dispersion of novice drivers' fixations, compared to an elevated dispersion for the
experienced group (π π,ππ = π. ππ, π = π. πππ).Those results might indicate a learning slope among novice
group even toward unmaterialized hazard, along three out of four repetitions at the training stage, compare
to a stable dispersion among experienced drivers. Results are demonstrated in table 2.
Novices untrained Novices trained Experianced trained
N 15 16 26
training phase
mean (Sd) number of fixations
toward unmaterialized visible
hazard
repetition 1 0(0) 0.5(1.1) 0.80(1.32)
repetition 2 0.4(0.63) 0.57(0.75)
repetition 3 0.5(0.94) 1.23(1.70)
repetition 4 0.53(1.24) 1.44(1.41)
mean (Sd) number of fixations
toward unmaterialized hidden
hazard
9. 8
repetition 1 5.12(2.3) 5.64(4.04) 7.64(5.13)
repetition 2 5.66(3.71) 5.88(3.68)
repetition 3 3.93(4.16) 6.2(4.24)
repetition 4 5.78(4.15) 5.84(4.04)
mean (Sd) first fixation start time
(sec) toward unmaterialized visible
hazard
repetition 1 6.88(0.21) 6.75(0.55) 6.82(0.31)
repetition 2 6.7(0.29) 6.7(0.23)
repetition 3 6.68(0.22) 6.88(0.36)
repetition 4 7.08(0.78) 6.87(0.46)
mean (Sd) first fixation start time
(sec) toward unmaterialized hidden
hazard
repetition 1 17.27(2.65) 16.47(0.12) 16.67(0.35)
repetition 2 16.50(0.89) 16.58(0.15)
repetition 3 16.6(0.13) 16.66(0.51)
repetition 4 16.61(0.22) 16.66(0.36)
mean (Sd) Sd of the fixations
location toward unmaterialized
visible hazard
repetition 1 103.84(44.90) 67.30(58) 63.31(23.75)
repetition 2 93.02(35.53) 125.15(50.70)
repetition 3 100.64(63.75) 108.80(48.35)
repetition 4 113.66(55.23) 108.64(42.92)
mean (Sd) Sd of the fixations
location toward unmaterialized
hidden hazard
repetition 1 110.53(55.70) 82.28(50.62) 83.56(39.54)
repetition 2 69.04(27.25) 87(36.41)
repetition 3 68.75(40.87) 81.67(33.36)
repetition 4 97.20(45.07) 82.42(33)
Test phase (generalization)
unmaterialized visible hazard
mean (Sd) number of fixations
toward visible unmaterialized
hazard
7.62(5) 9(5.56) 8(4.75)
mean (Sd) first fixation start time
(sec)
6.06(0.4) 6.04(0.17) 6.25(0.5)
mean Sd (Sd) of the fixations
location
150.78(60.63) 153.07(57.55) 196.04(72.35)
Table 2: estimated mean (Sd.) of fixations measures toward unmaterialized hazards by groups,
phases and repetitions stages
In order to analyze the differences between novice untrained, novice trained and experienced groups during
the presentation of the unmaterialized hazard at the test stage, a GLMM model, with group as between factor
and sections of the hazards appearance as a within factor was applied. This analysis revealed higher
probability to identify by reaction among experienced drivers (M=0.35, SE=0.15) compared with untrained
10. 9
young-novice drivers (M=0, SE=0) (π‘122 = 2.37, π = 0.05). There was also a close to significant difference
between trained (M=0.32, SE=0.15, p=0.09) and untrained young-novice drivers.
5. DISCUSSION AND CONCLUSIONS
In this study, we examined the effect of implicit procedural learning, acquired through viewing video-clips that
simulate various driving situations, on participantsβ ability to identify driving hazards and their scanning
method of the road. Findings indicate that the measured indices improved over the course of repeated
viewing in the training process. Furthermore, differences were found between new and experienced drivers.
We found that the abilities of both novice and experienced drivers to identify both materialized and
unmaterialized hazards by pressing a button showed significant improvement along the training session. Both
experienced and novice driver identified the materialized hazards more often than unmaterialized hazard.
Eye tracking data reveal the differences between novice and experienced drivers. In the case of materialized
hazard, trained novice drivers had more fixations in the areas of interest than experienced drivers. Whereas,
in the case of unmaterialized hazards, experienced drivers clearly underwent a learning process while novice
drivers failed to identify these hazards at all. During the first two repetitions of the unmaterialized hazard
video-clip, both experienced and novice drivers showed very low fixation rate on the AOI, but on the third and
fourth repetitions experienced drivers fixate there more than novices. This indicates that these drivers
identified and attended to the unmaterialized hazard. Accordingly, during the test phase experienced drivers
showed higher probability to fixate on materialized hidden hazard. This finding is in line with Vlakveld [16]
findings that new drivers encounter difficulties in identifying unmaterialized and hidden (hidden) hazards.
These findings are especially interesting as they not only indicate that implicit learning is effective and
positively affects driving hazard identification skills, but also may shed light on differences in the attention and
implicit learning patterns of experienced versus novice drivers. Experienced drivers, who accumulated many
hours of driving experience respond automatically to the sight of materialized hazards they encounter on the
road. They need to allocate only little attention to the hazard (reflected in the small number of fixations
compared to new drivers, and wider dispersion of fixations location) in order to respond to the hazard quickly
and effectively. In contrast, while new drivers also identify materialized hazards, such identification has not
yet become a cognitively automated process for them. They need to direct their attention to the hazard (large
number of fixations). They respond to the hazard (measured button pressing response times), and their skills
show improvement along the video-clips repetitions, but they keep high levels of attention on the hazard the
entire time.
The results of learning, especially with respect to identification of materialized hazards, are also evident in the
generalization stage. Eye tracking data show that all drivers noticed these hazards, yet a significant
difference was found between the groups. Experienced drivers exhibited the greatest number of fixations
while novice untrained drivers exhibited the lowest number of fixations. Novice trained drivers exhibited a
number of fixations between these two groups. Namely, experienced drivers easily and efficiently identified
these hazards in new situations. Trained new drivers showed better identification skills compared to
untrained new drivers.
11. 10
The pattern in which drivers scan the road while driving is reflected in the distribution of the drivers' fixations
plotted on a two-dimensional space. In all types of hazards, it has been shown that among novice drivers the
spread of fixations on the X-axis decreased as the number of repetitions increased. It is noteworthy that the
spread of fixations of experienced drivers was wider than the spread of trained novice drivers.
These findings contradict findings of previous studies which show that experienced drivers adapt their
scanning pattern to their driving environment while new drivers fail to show similar sensitivity ([3]; [16]).
Nevertheless, the broad fixation spread of experienced drivers might indicates that even though they
identified and responded to the target hazard, and even showed a learning slope toward unmaterialized
hazard, they continue to scan the road to prevent being surprised by additional events on the road that
require their attention. They shift their attention. New drivers identified the hazard and responded to it but
they had difficulties shifting their attention from it to scan the entire surrounding. Novice drivers decline in
dispersion of the fixations location toward unmaterialized hazard, might also indicate a very subtle primary
learning process even among them, that didn't reach yet any visible expressions
The findings of the current study indicate that exposing drivers to video-clips of different types of hazards can
improve their skills in scanning the road and their ability to identify hazards. New drivers and experienced
drivers both can benefit from such training. The advantage in this type of training is that it is based on implicit
procedural learning that occurs automatically as driving experience increases in real world thus it is not so far
from reality and very convenient to apply. The significance of this finding is that the more strongly road
scanning and hazard identification skills are assimilated through implicit learning, the more accessible and
effective these skills will be for drivers, increasing their ability to identify and respond to hazards while driving.
This may improve driving safety and reduce the number of road accidents. Future research may examine the
effects of such learning over time, its persistence and the potential to reinforce the acquired skills through
additional periodic training sessions.
12. 11
6. REFERENCES
1. Borowsky, A., & Oron-Gilad, T. (2013). Exploring the effects of driving experience on hazard
awareness and risk perception via real-time hazard identification, hazard classification, and rating
tasks. Accident Analysis and Prevention, 59, 548-565.
2. Borowsky, A., Shinar, D., & Oron-Gilad, T. (2010b). Age and skill differences in driving related hazard
perception. Accident Analysis and Prevention, 42, 1240β1249.
3. Chapman, P., & Underwood, G. (1998). Visual search of driving situations: Danger and experience.
Perception, 27, 951β964.
4. Crundall, D., Andrews, B., Loon, E., & Chapman, P. (2010). Commentary training improves
responsiveness to hazards in a driving simulator. Accident Analysis & Prevention, 42, 2117-2124.
5. Crundall, D., Underwood, J., Chapman, P. (1999). Driving experience and the functional field of view.
Perception, 28, 1075-1087.
6. Frensch, P. A., & Runger, D. (2003). Implicit Learning. Current Directions in Psychological Science,
12-18.
7. Horswill, M. S., Falconer, E., Pachana, N., Wetton, M., & Hill, A. (2015). The longer-term effects of a
brief hazard perception training intervention in older drivers. Psychology and Aging, 30, 62-67.
8. Horswill, M. S., Hill, A. & Wetton, M. (2015), Can a video-based hazard perception test used for
driver licensing predict crash involvement?", Accident Analysis & Prevention, 82, 213-219.
9. Horswill, M. S., & McKenna, F. P. (2004). Driversβ hazard perception ability: Situation awareness on
the road. In S. Banbury & S. Tremblay (Eds.), A cognitive approach to situation awareness (pp. 155β
175). Aldershot, UK: Ashgate.
10. Meir A., Borowsky A, Oron-Gilad, T. (2015) Formation and Evaluation of Act and Anticipate hazard
perception training intervention for young novice drivers. Traffic Injury and Prevention, 15, 172-180
11. Mills, K.C., Parkman, K.M., Smith, G.A., Rosendahl, F., 1999. Prediction of driving performance
through computerized testing: high-risk driver assessment and training. Transportation Research
Record 1689, 18β24.
12. Pollatsek, A., Narayanaan, V., Pradhan, A., & Fisher, D. (2006). Using Eye Movements to Evaluate a
PC-Based Risk Awareness and Perception Training Program on a Driving Simulator. Hum Factors
Human Factors: The Journal of the Human Factors and Ergonomics Society, 48, 447-464.
13. Sagberg, F., BjΓΈrnskau, T., 2006. Hazard perception and driving experience among novice drivers.
Accident Analysis and Prevention 3, 407β414.
14. Seger, C. A. (1994). Implicit Learning. Psychological Bulletin, Vol 115, 163-196.
15. Stadler, M. A., & Frensch, P. A. (1994). Whither learning, whither memory? Behavioral and Brain
Sciences, 423-424.
16. Underwood, G. (2007). Visual attention and the transition from novice to advanced driver.
Ergonomics, 50(8), 1235-1249.
13. 12
17. Vlakveld, W.P.(2014). A comparative study of two desktop hazard perception tasks suitable for mass
testing in which scores are not based on response latencies. Transportation Research Part F , 22,
218β231.
18. Willingham DB, G.-E. K. (1999). The relation between implicit and explicit learning: Evidence for
parallel development. Psychol. Sci., 10:531β34.
.
View publication statsView publication stats
![1
1. INTRODUCTION
Recent evidence shows that novice drivers are more likely than experienced drivers to be involved in car
crashes, mainly due to their poor hazard anticipation abilities (e.g., [1]; [7]; [8]). Hazard anticipation or hazard
perception may be defined as drivers' ability to read the road and anticipate hazardous situations [11]. As any
skill, hazard perception improves with practice [9]. A large body of research has shown that novice drivers
can improve their abilities to anticipate hazards to some extent through training [8]. To date, most training
programs have been based on explicit, deliberate learning, in which novice drivers were explicitly told what
kind of hazards they should look for and where they should look for them (e.g., [4]; [12]). Considering the fact
that hazard anticipation improves as driving experience accumulates it may well be that learning to anticipate
hazards can be facilitated by using implicit-based, procedural training. The current study investigated whether
novice drivers and experienced drivers can learn to anticipate hazards through implicit procedural learning.
Implicit procedural learning is usually defined as the non-deliberate acquisition of dynamic patterns of
behavior that ultimately lead to implicit memory [14]. In procedural learning, it is difficult to explain precisely
when or how the specific knowledge is encoded into memory, but knowledge acquisition can be
demonstrated through the detection of an ability that did not exist prior to the learning process or through the
improvement of an existing ability. Procedural knowledge that is acquired implicitly is widely reflected in our
performance of everyday activities such as walking, running, and catching objects that are unexpectedly
thrown at us. Implicit procedural learning requires no special allocation of attention [6], and in at least several
cases, implicit learning is the foundation of automatic behavior [18]. This type of learning occurs passively,
incidentally, and automatically: No conscious effort is made to absorb the knowledge, and no comprehension
of its contents is required.
Implicit procedural learning has numerous advantages: it is not age- or IQ-dependent, and knowledge
acquired via this method is more stable and resilient than knowledge acquired through explicit learning [15].
Information conveyed implicitly to participants is converted into skills that are usually more easily retrievable
than skills acquired in an explicit learning process involving comprehension and memory [18]. Implicit
procedural learning also has better ecological validity than other training methods, as most everyday
activities encompass implicit and unconscious form of learning. Hence, implicit procedural training
methodology may be at preference when applied to the improvement of driving skills and hazard perception,
over existing training methods that teach hazard perception skills in an explicit manner, since driving skills,
and especially hazard anticipation skills are typically acquired implicitly.
A typical way to evaluate hazard perception abilities is to use short video clips of real world situations
presented on a computer screen where drivers are asked to press a response button each time they identify
a hazard (e.g., [13]; [2]; [4]; [10]). Those videos are usually taken from the perspective of a driver driving
down the road. The selected hazardous situations vary from one study to another but in general they are all
used to examine whether drivers identify the hazard (pressing a response button) and how fast they respond.
Some studies also use an eye tracking system to examine the way drivers scan the road and whether or not
they fixated on the hazard instigator ([5]; [2]).
Recently, based on their findings with regard to hazard that best differentiate between novice and
experienced drivers, Borowsky and his colleagues proposed a new taxonomy to describe road hazards that](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)