This document summarizes a study that examined how explicit information and implicit learning influence drivers' decisions to overtake other vehicles. The study found that overtaking decisions were made more frequently when drivers were told the performance of their vehicle was higher (explicit information) or experienced higher performance during practice drives (implicit learning). Both types of learning independently impacted the drivers' internal model of the vehicle's capabilities and affected their judgments of whether an overtaking opportunity was safe. The researchers concluded that real-world driver decisions are influenced not just by situational factors but also by how the performance of their vehicle is represented in their mind.

1. Copyright: Shell Brands International AG 2008
Projects & Technology
8/29/2013
Decisions to overtake are affected by implicit learning of the performance
of the car
Stephen Skippon, Nick Reed & Ryan Robbins
6th International Conference on Driver Behaviour & Training, Helsinki, August 2013

2. Decisions to overtake
Decisions on whether or not to overtake depend on the
resolution of a conflict between two types of goals:
•Progress: e.g. “Get to destination as fast as
possible”
•Safety: e.g. “Avoid harm to myself”
The first motivates overtaking behaviour, but the
second motivates the driver not to overtake

3. Interactive activation and competition model of
decisions to overtake
•Alternative behaviours are mentally
represented
•Their representations receive excitatory and
inhibitory signals from the goals, and inhibit
each other
•Net activation of each behaviour is a weighted
sum of these inputs
•Behaviour with the highest net activation
executes
GOAL M: Get to
destination as fast as
possible
GOAL N: Avoid harm
BEHAVIOUR X:
Overtake
BEHAVIOUR Y:
Follow
Excitatory signal Inhibitory signal

4. Excitatory and inhibitory signals from competing goals
depend on the net activation levels of those goals
comparison signal
in behavioural
action feedback
loop
Net Activation
level of goal
Net excitation and
inhibition from
other goals
These inputs are
multiplied together
Excitation and
Inhibition
signals from
other goals
These inputs
are summed
together
Activation decay: activation is gradually
lost in the absence of new inputs
Inter-goal dynamics processes
(e.g. Kruglanski et al. (002)
Goal-pursuit
processes (e.g.
Carver &
Scheier, 1998)

5. Self regulatory control:
the behavioural action feedback loop for a goal
R: Mental
representation of
intended state of
the world
Behaviour
P: Perception of
state of the world
Comparison
E = R - P
State of the
world External
events
Internal
working
models
Sensory inputs
Overtaking decisions require access to an internal
working model of the performance of the car
Magnitude of comparison signal E depends on how far
the goal is from fulfilment (i.e. R – P)

6. Internal working model of vehicle performance
Humans use internal working models to
predict the behaviour of entities in our
perceptual worlds:
• e.g. internal working models of other
people
To assess whether a particular road situation
represents an opportunity to overtake safely,
the driver must combine:
• information about the scene situation
(speeds of vehicles, distance available,
etc.)
• knowledge of the way his/her own
vehicle will respond to his/her control
actions
This knowledge is contained in an internal
working model of the response of the vehicle
to control actions

7. Internal working model dynamics
Drivers may use different vehicles, and
the performance of the same vehicle
may vary depending on load, engine
temperature, altitude, weather, and fuel
So to maintain its utility the internal
working model is likely to be able to
adapt in the light of new information
•Explicit learning: information
acquired directly via an external
source (e.g. observation that car is
heavily loaded)
AND/OR
•Implicit learning (Frensch &
Runger, 2003): Information
acquired indirectly, through
experience of the vehicle’s
response to control actions
Hypotheses:
Decisions to overtake will be made
more frequently and/or in more
challenging circumstances if the
driver has been exposed to:
•Explicit information that the
performance of the vehicle is
higher
•Implicit learning that that the
performance of the vehicle is
higher

8. TRL DigiCar Driving simulator
DigiCar simulator at TRL: Honda Civic
simulator vehicle; Renault vehicle
dynamics model; OKTAL SCANeR II
Vehicle dynamics updated at 100Hz;
visuals refreshed at 60Hz; data recorded at
20 Hz
Three projection screens, 210o forward field
of view; one rear screen, 60o rear field of
view
Visual scene resolution1280 × 1024 pixels
per screen
Motion system with three degrees of
freedom (pitch; roll and heave)
Participants walked up to and entered the
car as they would a real vehicle,
encouraging expectations of a close-to-
normal driving experience

9. Study design
2 x 2 x 2 within-participants design with 48 participants
Each participant completed 4 experimental drives in TRL
DigiCar simulator
3 independent variables, each with 2 levels:
Explicit prior knowledge – participants briefed that
simulated vehicle has:
•Average performance
•Higher performance
Implicit learning condition: simulator vehicle performance:
•Normal
•Higher (8% higher acceleration)
Overtaking difficulty: two experimental overtaking
opportunities per drive (of different levels of difficulty)
•String of 4 vehicles following each other closely
•String of 7 vehicles following each other closely
Independent variable: Frequency of positive decisions to
overtake (move into overtaking lane AND accelerate)
Practice
overtaking
Experimental
overtaking
Familiarisation
driving
Experimental route: Rural single
carriageway with short village
sections, and sections of dual
carriageway for overtaking

10. Overtaking 4 vehicles

11. Results: Frequency of overtaking vs. explicit learning
and implicit learning – overtaking 4 vehicles
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Briefed low, actual
low
Briefed low, actual
high
Briefed high, actual
low
Briefed high, actual
high
Frequencyofpositivedecisionstoovertake
Available acceleration

12. Results: Frequency of overtaking vs. explicit learning
and implicit learning – overtaking 7 vehicles
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Briefed low, actual
low
Briefed low, actual
high
Briefed high, actual
low
Briefed high, actual
high
Frequencyofpositivedecisionstoovertake
Available acceleration

13. Results summary
Significant main effects of:
•explicit learning
• implicit learning
No significant interactions
Supports hypotheses that:
Decisions to overtake will be made more frequently if the driver has been
exposed to explicit information that the performance of the vehicle is
higher
AND
Decisions to overtake will be made more frequently if the driver has been
exposed to implicit learning that that the performance of the vehicle is
higher

14. Discussion: role of learning processes in overtaking
decisions
Both explicit information and implicit learning can independently
change the internal working model of vehicle performance
Situational perception when faced with a decision whether of not
to overtake
Relative activation levels of competing goals
Relative strengths of excitatory and inhibitory signals to mental
representations of alternative behaviours
Decision: enacted behaviour
which affects
which affects
which affects
which affects

15. Conclusions
•Drivers’ decisions on whether to overtake in a given situation draw on an internal working
model of the performance of their vehicle
•The internal working model is not constant , but adaptable
•The internal working model can be influenced by information provided explicitly to the
driver. Real-world examples might include: knowledge that the engine has recently been
tuned/serviced; knowledge that the vehicle is lightly/heavily loaded; marketing
communications
•The internal working model is also influenced through implicit learning of the vehicle’s
actual response to control actions in recent driving
•These processes are independent

16. Acknowledgments
Thank you for your attention!
The authors would like to acknowledge the contribution of Lena
Weaver of TRL for running the driving simulator experiment, and
thank our participants for helping us carry out the study

17. References
Carver, C.S. & Scheier, M.F. (1998). On the Self-Regulation of Behavior.
Cambridge, England: Cambridge University Press.
Frensch, P. A. & Runger, D. (2003). Implicit learning. Current Directions in
Psychological Science 12 (1), 13-18.
Kruglanski, A.W., Shah, J.Y., Fishbach, A., Friedman, R., Chun, W.Y. & Sleeth-
Keppler, W. (2002). A theory of goal systems. In M.P. Zanna (Ed.) Advances in
Experimental Social Psychology, Vol. 34, pp. 331-378. San Diego, CA:
Academic Press.