1) The document discusses driver distraction and its effects on driving performance and crash risk. It focuses on two main types of distraction: visual and cognitive. 2) While visual distraction consistently impairs driving, the evidence on cognitive distraction is mixed. Some studies found impaired performance, while others found no effects or even improvements. 3) The document proposes the "zombie hypothesis" to explain these inconsistent findings. It suggests that cognitive distraction shifts drivers to a more automatic "zombie-like" state, relying more on overlearned routines. This preserves performance in familiar situations but impairs it in novel or difficult situations requiring cognitive control. 4) Several studies provide support for the zombie hypothesis by finding intact performance on routine

1. Zombies i trafiken: Effekter av
kognitiv distraktion på
körprestatation och olycksrisk
Johan Engström, Volvo Technology
Transportforum, Linköping 2011-01-13

2. Driver distraction
• Currently hot topic
– On top of the road safety agenda in the
US (see distraction.gov)
– Mobile phone debate in Sweden
• Recent NHTSA statistics
– 16% of fatal crashes and 20% of injury
crashes involved reports of distracted
driving (DOT HS 811 379)

4. What is driver distraction?
(US-EU Focus Group on Driver Distraction, Berlin, April, 2010)
• Two main types
– Visual: Diversion of visual attention and gaze
• Examples: Radio tuning, phone dialling, text messaging,
looking at roadside events
– Cognitive: Diversion of non-visual attention
• Examples: Daydreaming, phone/passenger conversation,
speech interface control

5. Effects of visual distraction (VTTI CVO study)
(Hanowski et al, in review)

6. Cognitive distraction – inconsistent results!
• Experimental studies
– Delayed event detection/response (Horrey and Wickens, 2005; Caird et al., 2004)
• However
– Many studies used artificial stimuli (e.g. PDT) (e.g. Swedish Mobile Phone Investigation, Patten et
al., 2003)
– Effect in lead vehicle braking studies depends on initial time headway (Engström, 2010)
– No effect when brake lights are turned off (Muttart et al., 2007)
– Lateral control
• Impairment found for artificial tracking/driving tasks (Briem and Hedman, 1995; Strayer et
al., 2001; Creem and Profitt, 2001; Just, Keller and Cynkar; 2008 )
• Improvement during cognitive task operation found for normal lane keeping performance
(Östlund et al., 2004; Törnros and Bolling, 2005; Engström et al., 2005; Jamson and
Merat, 2005; Mattes, Föhl and Schindhelm, 2007; Merat and Jamson, 2008).
• Crash risk
– Epidemiological studies: 4 times higher crash risk for mobile phone conversation
(Redelmeier and Tibshirami, 1997; McEvoy et al., 2005)
– Naturalistic driving: No increased crash/near crash risk (Dingus et al., 2006) or
lower risk (Olson et. al. 2009) for mobile phone conversation

7. ”Standard” information processing bottleneck models
(Pashler and Johnston, 1998; Salvucci and Taatgen, 2007)
• General prediction: Cognitive distraction -> general slow down in
cognitive functioning -> general impairment in driving (slower
response, impaired lateral control)
• Inconsistent with the data!
Auditory Vocal
Visual Manual
Cognition
(bottleneck)
Response
Perception

8. A possible alternative explanation: The zombie
hypothesis

9. ”Zombie” behaviour (Koch, 2004)
• The application of routine, overlearned and automated,
unconscious
• The ”default” case in everyday driving
• Includes basic actions (e.g., looking, braking, turning) as
well as more complex sequences of actions (e.g.,
negotiating intersections, overtaking)
• Driven top-down by contextual cues
• Fast but inflexible and stereotyped
• May involve implicit learning

10. Cognitive control
• May override zombie behaviour when needed/desired
• Deployed in novel or difficult situations that require
flexibility, or when one is motivated to optimise
performance
• Effortful and associated with conscious awareness
• Example:
– Stroop task: Green, Blue, Red
– Crossing the street in the UK

11. A model (Norman and Shallice,1986; Miller and Cohen,
2001; Engström, Markkula and Victor, 2010)
• Task context and
basic schemata Cognitive control
• Related schemata
may compete for
activation Task context Schemata
Task context
• Schemata selected
top-down ”Zombie
Top-down
(proactively) and/or system”
bottom-up
(reactively) Schemata
Schemata
Basic
Bottom-up
• Two types of top-
down selection Sensing Actuation
– Context-driven
(automatic,
unconscious,
inflexible)
– Cognitive control
(effortful, conscious
Environment

12. The zombie hypothesis
Cognitive control
• Cognitive distraction
involves working memory
load Schemata
• Working memory requires
cognitive control to sustain Phone ”Zombie
activation of schemata in conversation
system”
the absence of stimulus
input Lane keeing
• Lack of cognitive control
to other tasks -> zombie
Sensing Actuation
behaviour
Environment

13. Predictions
• General:
– Cognitive distraction leads to zombie behaviour (stereotyped, inflexible,
but still efficient in routine situations)
– Cognitive distraction affects only non-routine (non-zombie) behaviours –
i.e., those that rely on cognitive control
• Specific:
– Intact performance
• Normal lane keeping
• Basic avoidance responses to closing objects (looming)
• Basic visual orientation to salient objects
• Context-driven implicit learning
– Impaired performance
• Novel/difficult lateral control tasks
• Fast braking responses to brake lights
• Utilisation of non-routine predictive cues
• Semantic interpretation and encoding
• Flexible adaptation

14. Evidence
• Left intact
– Normal lane keeping (Östlund et al., 2004; Törnros and Bolling, 2005; Engström
et al., 2005; Jamson and Merat, 2005; Mattes, Föhl and Schindhelm, 2007; Merat
and Jamson, 2008)
– Basic avoidance responses to closing objects (looming) (Muttart et al., 2007;
Engström et al., in prep)
– Basic visual orientation to salient objects (Strayer et al., 2003; Engström et al., in
prep)
– Context-driven implicit learning (Chun and Jiang, 1998; Engström et al., 2010)
• Impaired
– Novel/difficult lateral control tasks (Briem and Hedman, 1995; Strayer et al.,
2001; Creem and Profitt, 2001)
– Speeded braking responses to brake lights (Alm & Nilsson, 1995; Brookhuis, de
Vries & de Ward, 1991; Lee et al., 2001; Salvucci and Beltowska, 2008; Strayer,
Drews & Johnston. 2003; Strayer & Drews, 2004; Engström, Ljung Aust and
Viström, 2010)
– Utilisation of non-routine predictive cues (Muttart et al., 2007; Baumann et al.,
2007)
– Semantic interpretation and encoding (Strayer et al., 2003; Engström et al, 2010)
– Flexible adaptation (Engström et al., in prep)

15. Example data: Glances to oncoming car
Zombie learning Zombie behaviour Flexible adaptation
3,5 3
3 2,5
2,5
2
2
No load No load
1,5
WM load WM load
1,5
1
Number of glances to car
1
Mean glance duration to car
0,5 0,5
0 0
1 2 3 4 5 6
1 2 3 4 5 6
Scenario exposure
Exposure
Number of glances towards car Mean duration of glances towards car
Implicit learning (”crude adaptation”) Flexible adaptation only for
non-loaded drivers

16. Example data: Brake onset time
1,2
No load
1 WM load
0,8
WM load
0,6 Flexible adaptation
Zombie
response
0,4
0,2
Brake response time
No load
0
1 2 3 4 5 6
-0,2
-0,4
Scenario exposure

17. Example data: Anticipatory braking
Proportion of Flexible adaptation
anticipatory
0,40
braking (before event
onset)
0,30
No load
0,20
0,10
WM load
0,00
1 2 3 4 5 6
Zombie response rep

18. Example data: Visual response time for first exposure
0,80
0,60
0,40
Mean RT
0,20
0,00
0 1
No load WM WM load

19. Does cognitive distraction increase crash risk?
• At least not through delayed last-second avoidance
responses or impaired lane keeping
• However, may contribute to the development of critical
situations when zombie behaviour is insufficient to deal
with the situation
• This would not be expected to show up in current
naturalistic data analyses – only analysed 5 seconds
prior to the event
• May explain discrepancy between naturalistic and
epidemiological studies…

20. Conclusions
• Cognitive distraction affects some aspects of driving
performance but leave others intact
• Zombie hypothesis: Should only affect non-routine
activities
• Generally supported by existing data
• Further experimental work is needed to further validate
the hypothesis