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Lightbulb assignment

Evaluate the findings of the assigned paper in light of what you know about topics in memory and attention. Do not simply summarize the results (or the methods)–Rather, your task is to evaluate and comment on the idea of automaticity presented in this paper, relating it to what you’ve been learning in class.**Double spaced, 1 page MAX**

Accident Analysis and Prevention 57 (2013) 80–86
Contents lists available at SciVerse ScienceDirect
Accident Analysis and Prevention
journal homepage: www.elsevier.com/locate/aap
Route familiarity breeds inattention: A driving simulator study
Matthew R. Yanko ∗ , Thomas M. Spalek
Simon Fraser University, Canada
a r t i c l e
i n f o
Article history:
Received 17 July 2012
Received in revised form 2 April 2013
Accepted 3 April 2013
Keywords:
Driving simulator
Route-familiarity
Mind-wandering
a b s t r a c t
Inattention is a major cause of traffic accidents. Here, we show that, contrary to common-sense expectation, familiarity with a route is itself a source of driving impairment. This effect may be attributed to
increased mind-wandering along familiar routes. In the present work, participants followed a vehicle
along a route with which they were either familiar or unfamiliar. During the experimental session, the
lead-vehicle braked at random locations, forcing participants to brake to avoid a collision. Participants
were also required to respond with a button press when they noticed pedestrians heading toward the
road from a sidewalk. In Experiment 1 we found that familiar drivers follow the lead vehicle more closely
and are slower to notice approaching pedestrians. In Experiment 2, with following distance held constant, reaction times to central and peripheral events were longer for familiar drivers. Consistent with
the mind-wandering hypothesis, all these effects were eliminated in Experiment 3 when drivers were
made to focus on the driving task.
© 2013 Elsevier Ltd. All rights reserved.
1. Introduction
Repeatedly engaging in a task, often results in a gradual transition from initially needing to consciously control one’s actions, to a
state where our actions are governed by more automatic processes
(Schneider and Shiffrin, 1977). This transition from controlled to
automatic processing is thought to be accompanied by a reduced
demand on attentional resources. Neuroanatomical evidence consistent with this position is found in a study showing reduced
brain activation patterns in participants practiced in completing a
word generation task versus unpracticed participants (Raichle et al.,
1994).
Posner and Snyder (1975) characterized the difference between
controlled and automatic processes as follows. Controlled processes
are those that are under top-down control (i.e., are volitional),
can be modified based on task demands, and require attentional
resources in order to be carried out. Automatic processes, on the
other hand, occur without conscious awareness, are ballistic, and
do not interfere with separate processes that require attentional
resources.
One task that most of us engage in daily that illustrates this practice effect is the task of driving a car. When we first started driving,
we had to devote a lot of attention to checking our mirrors, having our hands in the 10 and 2 positions, watching the speedometer,
etc. (this is even more evident if the car has a manual transmission).
∗ Corresponding author at: Department of Psychology, Simon Fraser University,
8888 University Drive, Burnaby, BC V5A 1S6, Canada. Tel.: +1 778 782 5359.
E-mail address: myanko@sfu.ca (M.R. Yanko).
0001-4575/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.aap.2013.04.003
Over time, however, the task of driving that car becomes easier. In
addition to learning the general aspects associated with driving a
vehicle, similar learning also occurs with respect to learning the
route between two locations. Initially we have to pay a lot of attention to road signs, etc., but as we become familiar with the route,
these aspects, as well as more subtle things like the curves in the
road and intersection locations, no longer have to be sought out,
but rather are provided to us through our memories. That is, we
shift from a more controlled, effortful, processing of the route, to
a more automatic one. This shift to more automatic processing of
the route should free up resources (Posner and Snyder, 1975) that
could be allocated to some other task, like hazard detection. As a
result, one might predict that familiar drivers should be more efficient at executing an appropriate response to a hazardous event
than unfamiliar drivers. Although it makes intuitive sense that the
development of efficient route processing should aid driving performance, this possibility has not been previously explored.
On the other hand, the idea that route-familiarity might promote a delay in hazard response is indirectly supported by evidence
in the driving literature. For example, compared to novice drivers,
drivers with extensive experience are less likely to check their mirrors and to follow a lead vehicle at an adequate distance (Duncan
et al., 1991). These findings could be an indication that experienced
drivers are less likely to successfully monitor the environment for
hazards, thereby limiting the ability to respond promptly when
needed. In addition, it has been shown that as one becomes familiar
with a route, there is a decrease in the amount of time spent looking at peripheral items, and drivers are less likely to notice changes
in the environment (Charlton and Starkey, 2011; Martens and Fox,
2007). In fact, Martens and Fox (2007) demonstrated that route
M.R. Yanko, T.M. Spalek / Accident Analysis and Prevention 57 (2013) 80–86
familiarity can promote a state of inattentional blindness, where
drivers are less likely to notice a critical stimulus in the environment even when the driver fixates on that stimulus.
One possible explanation as to why route familiarity promotes a
form of inattentional blindness comes from the literature on mind
wandering. The reasoning goes something like this. Mind wandering is a state where the thought processes that occupy the mind are
on topics that are unrelated to the task(s) at hand (Smallwood and
Schooler, 2006). The incidence of mind wandering has been shown
to increase as a task becomes more practiced (Cunningham et al.,
2000; Mason et al., 2007; Teasdale et al., 1995). An important consideration for the present work is that mind wandering can occur
spontaneously and is thought to utilize the same resources as goal
directed thought (Christoff et al., 2004; Smith et al., 2006; Teasdale
et al., 1995). Thus any other task that requires these resources, like
the encoding of sensory information from the external environment, would be impaired (Smallwood and Schooler, 2006). This
conjecture is supported by the common phenomenological experience that, having driven along a familiar route, a driver can hardly
remember any of the specifics associated with the drive. These ‘time
gaps’ (Chapman et al., 1999) that are often experienced by drivers
provide a clear indication that, during those periods, the vehicle
operator was driving without full awareness of the environment. To
the extent that executive attention is necessary to respond appropriately to a hazard, familiar drivers should perform worse than
unfamiliar drivers when encountering a hazard.
The results obtained by Martens and Fox (2007) may be
explained by the mind wandering hypothesis as follows. As familiarity with the route is increased, drivers may have been more likely
to let their minds wander, thereby making it less likely for them to
successfully process incoming sensory information – inattentional
blindness – because the system is otherwise preoccupied. This possibility is supported by anecdotal evidence provided by Charlton
and Starkey (2011) who noted that many participants found themselves starting to ‘daydream’ as they excessively practiced a route.
Based on these findings, it is reasonable to expect that when driving along a familiar route, drivers might take longer to notice an
emergency event, and hence would be expected to take longer to
respond than if they were driving along an unfamiliar route.
There are two opposite theoretical predictions concerning the
effect that route familiarity has on hazard avoidance. Familiarity
might lead to the route being processed automatically and thus
freeing up resources that could be used to process other stimuli
in the environment, like potential hazards. From this it follows
that reaction time (RT) to avoid a hazardous stimulus should be
faster in familiar than in unfamiliar route conditions. On the other
hand, given the previous evidence linking automaticity and route
familiarity to a reduced likelihood of successfully monitoring the
environment (Charlton and Starkey, 2011; Martens and Fox, 2007),
one might predict the opposite pattern of results. For example, it
could be that as the route becomes familiar, the incidence of inattentional blindness might increase, and thus the driver would be
less able to deal with the hazardous stimulus. In this case, it follows that RT to the hazardous stimulus should be slower in familiar
than in unfamiliar routes. The present experiments were designed
to test these two competing theories.
2. Experiment 1
The objective of Experiment 1 was to investigate whether
familiarity with the route will affect driving performance, such
as responding to emergencies, in a positive or a negative way.
If familiarity with the route leads to the route being processed more automatically, the extra attentional resources made
available should improve driving performance. In contrast, if
81
route-familiarity promotes a form of inattentional blindness
(Martens and Fox, 2007) then driving performance should be
impaired relative to when unfamiliar with the route. This issue was
explored in the present experiment using a simple car-following
paradigm (see Strayer et al., 2003), where participants followed a
pace car through a route that they had either previously been made
familiar with or not. Responses to a series of unexpected events
were assessed, along with other measures of driving performance.
2.1. Methods
2.1.1. Participants
Fifteen female and five male undergraduate students (mean
age = 20.9 years, SD = 1.66) from Simon Fraser University participated either for class credit or for payment. All had self-reported
normal or corrected to normal vision. All had a valid British
Columbia driver’s license (class 5) and reported driving on average
5.6 times per week. Before starting the experiment, participants
filled out a modified ‘Simulator Sickness Questionnaire’ with questions such as “are you taking any medications” or “are suffering
from any ailments that might make you prone to motion sickness.” In order to minimize the incidence of simulator sickness we
excluded any participants who answered yes to any such questions
(see Kennedy et al., 1993; for an overview).
2.1.2. Materials
A DriveSafety high-fidelity driving simulator (model DS-600c)
was used. Examining driving performance with a driving simulator
grants several advantages over real-world on-road tests. Driving
simulators not only provide a safer environment, but also allows for
complete control over the driving conditions. In addition, driving
simulators allow consistent and reliable data to be collected over a
broad range of variables.
Participants were seated in a modified Ford Focus cab equipped
with a windshield, driver and passenger seats, dash board, instrument panels, and a central console, as well as all the devices
needed to operate a car (accelerator and brake pedal, turn signal switch, a steering wheel etc.). The simulated environment was
generated using HyperDrive Authoring Suite and was displayed
using DriveSafety’s Vection Simulation software (Version 1.9.35:
http://www.drivesafety.com). The simulator is also equipped with
an automatic gearbox.
2.1.3. Driving routes
Five freeway driving routes were developed for this experiment
(routes 1–5). Each route was approximately 12 km in length and
included a series of overpasses (where the roadway passes over
another), underpasses (where the roadway passes under another)
and cloverleaf intersections (on and off ramps: where the roadway gradually corners to merge with a new roadway). Each route
was designed to have the same number of cloverleaf intersections
and each route had the same number of left and right turns. The
five routes were programmed to look very similar to one another,
consisting mainly of long stretches of rural freeway. However, the
exact location for each cloverleaf intersection was different for each
route. Consequently, the specific sequence of exits that participants
were required to take to get to the end was different for each route.
These routes were driven in daytime conditions with good visibility.
There were three lanes of traffic going in each direction (separated
by a cement median). For all routes, a pace car and the participant’s
vehicle were the only two cars on the road. The participants were
instructed to follow the pace car, and the pace car was programmed
to maintain its position in the right lane.
82
M.R. Yanko, T.M. Spalek / Accident Analysis and Prevention 57 (2013) 80–86
Table 1
Means and standard deviations for each dependent measure for Experiments 1–3.
Dependent measures
Central
response RT (s)
Peripheral
response RT (s)
Headway
distance (m)
Velocity (m/s)
Lane position
(RMS)
Experiment 1
Familiar
Unfamiliar
.760 (0.10)
.910 (0.16)
1.20 (0.23)
1.02 (0.11)
25.2 (5.1)
32.6 (4.7)
20.7 (0.37)
20.5 (0.43)
0.60 (0.16)
0.57 (0.09)
1.3 (2.11)
0.40 (0.97)
1.7 (0.95)
2.62 (0.92)
Experiment 2
Familiar
Unfamiliar
.919 (0.17)
.811 (0.05)
1.18 (0.23)
1.01 (0.14)
31.2 (0.56)
31.5 (0.77)
19.4 (0.92)
19.7(0.89)
0.62 (0.17)
0.60 (0.13)
0.08 (0.28)
0 (0.00)
3.0 (0.30)
2.7 (1.3)
Experiment 3
Familiar
Unfamiliar
.930 (0.11)
.963 (0.14)
1.28 (0.42)
1.12 (0.35)
29.6 (0.26)
29.5 (0.24)
19.7 (0.30)
19.6 (0.19)
0.54 (0.13)
0.57 (0.09)
0 (0.00)
0 (0.00)
2.7 (0.48)
2.6 (0.84)
2.1.4. Procedure
The experiment comprised three sessions: Acclimatization,
Training, and Testing. In the Acclimatization session, participants
who passed the Simulator Sickness Questionnaire took part in a
3-min session to get used to the equipment and to adapt themselves to the physical sensations involved in driving the simulator.
The acclimatization session consisted of one short driving scenario
where participants followed a pace car down a stretch of a simulated freeway. The freeway consisted of a long straight stretch and
two cloverleaf intersections. At each intersection, the participants
practiced exiting from and merging onto the freeway. The Training
session followed directly after the Acclimatization session.
In the Training session, participants were randomly assigned
to the familiar group or the unfamiliar group. Participants assigned
to the Familiar group drove down ‘Route 1’ a total of four times
to become familiar with the route. Participants assigned to the
Unfamiliar group drove down routes 2–5 in chronological order.
All participants (from both familiar and unfamiliar groups) were
instructed to follow the pace car during each driving session. The
pace car drove at a constant speed of approximately 72 km/h and
led the participants from the beginning to the end of each route.
Participants were instructed to follow the pace car at a reasonable
distance that they felt comfortable with, while abiding by all traffic laws. However, if the participants’ vehicle fell more than 60 m
behind the pace car, a tone was administered to prompt the driver
to maintain a closer distance. Once the route was finished, the route
was restarted (for the Familiar group) or the next successive route
was started (for the Unfamiliar group). The Testing session followed
directly after the Training session.
During the Testing session, all participants drove through ‘Route
1’. This means that the route was known to the Familiar group, but
was unknown to the Unfamiliar group. During this phase, the pace
car was programmed to brake at 20 randomly selected locations
(which we refer to as the Central event). The position of each braking event was determined prior to the study, and was the same for
each participant. During this event, the pace car instantly reduced
speed to 33 km/h, and the brake lights were illuminated. The pace
car then gradually accelerated back to 72 km/h at a rate of acceleration that was approximately 2 m/s2 , and the brake lights stayed
illuminated for 3 s. To avoid a collision during these events, the
driver was required to respond by stepping on the brake pedal.
RTs to activate the brakes were recorded, as well as the degree of
brake depression, sampled at a rate of 60 Hz. Brake depression was
defined by the position of the brake pedal, and was given as a proportion from 0 to 1 (a value of zero indicates that the brake is not
being pressed, and any value above indicates that the brake is being
pressed).
In addition to the lead vehicle braking events, 19 women, each
wearing a red dress, were programmed to stand on the side of
the road (approximately 5 m from the center of the far right lane)
Collisions (pace
car)
Collisions
(pedestrian)
at randomly chosen locations in the testing phase. Five of those
women were programmed to walk toward the road when the participant was 50 m away (which we refer to as the Peripheral event).
The participant was instructed to make a push button response
when they noticed a woman start to walk toward the road. The button was located on the backside of the steering wheel on the right
hand side. RTs to make the button response were recorded. The
depression state of the button was sampled at a rate of 60 Hz. Each
walking woman eventually entered the roadway, and participants
were told not to worry about avoiding a collision with the woman.
This was done to try to get an accurate measure of when they
noticed the movement rather than that measure being confounded
with trying to make an evasive maneuver. The other fourteen
women remained motionless as the participant approached, and
participants were not required to make a response in that situation.
2.1.5. Dependent measures
There were a total of six dependent measures associated with
this experiment. Central Response RT was defined as the interval of
time between the braking of the pace car (i.e., the instant velocity
change and illumination of the brake lights) and the instant that the
participant depressed the brake pedal. Peripheral Response RT was
defined as the interval of time between the onset of the woman’s
movement and the initial depression of the steering wheel button.
Headway distance was defined as the distance in meters between
the pace car and the participant’s car at the beginning of each central event. In other words, a measure of distance was taken at the
beginning of each central event epoch, and averaged over the entire
test session. Lateral Position was defined as the root mean square
(RMS) of the lane position in meters between the center of the
lane, and the center of the participant’s car. The lateral position
was sampled continuously throughout the entire testing session.
Velocity was defined as the average velocity (in m/s) that the participants were traveling at the beginning of each central event. In
other words, a measure of velocity was taken at the beginning of
each central event epoch, and averaged over the entire test session.
Collisions was the total number of collisions that the participant had
with either the car or the woman.
2.2. Results and discussion
The results for all of the driving performance measures are
shown in Table 1. Independent samples t-tests were conducted on
each measure of interest throughout the paper. Familiar drivers
took significantly longer to respond to the peripheral event as
compared to unfamiliar drivers, t(18) = 2.18, p = 0.04, but were
significantly faster at the central response, t(18) = 2.48, p = 0.02.
Headway distance was also different between the two groups,
with …

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