Johan Engström, Volvo Technology

Transportforum, Linköping 2011-01-13

Zombies i trafiken: Effekter av

kognitiv distraktion på

körprestatation och olycksrisk

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)

What is driver distraction?

•Two m

ain 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

(US-EU Focus Group on Driver Distraction, Berlin, April, 2010)

Effects of visual distraction (VTTI CVO study)

(Hanowski et al, in review)

Cognitive distraction –inconsistent results!

•Experimental studies

–Delayed event detection/response (Horrey and W

ickens, 2005; Caird et al., 2004)

•However

–Many studies used artificial stim

uli (e.g. PDT) (e.g. Swedish Mobile Phone Investigation, P

atten 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 norm

al lane keeping perform

ance

(Östlund et al., 2004; T

örnros 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; McE

voy 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

”Standard” inform

ation processing bottleneck m

odels

(Pashler and Johnston, 1998; Salvucci and Taatgen, 2007)

•General prediction: C

ognitive distraction -> general slow down in

cognitive functioning -> general impairment in driving (slower

response, impaired lateral control)

•Inconsistent w

ith the data!

Perception

Cognition

(bottleneck)

Response

Visual

Auditory

Manual

Vocal

A possible alternative explanation: The zombie

hypothesis

”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 im

plicit learning

Cognitive control

•May override zombie behaviour when needed/desired

•Deployed in novel or difficult situations that require

flexibility, or when one is m

otivated to optim

ise

perform

ance

•Effortful and associated with conscious awareness

•Example:

–Stroop task: Green, Blue, Red

–Crossing the street in the UK

A m

odel (Norm

an and Shallice,1986; Miller and Cohen,

2001; Engström, Markkula and Victor, 2010)

Schemata

Environment

Sensing

Actuation

Schemata

Basic

Task context

Cognitive control

Schemata

Task context

Top-down

Bottom-up

•Task context and

basic schemata

•Related schemata

may compete for

activation

•Schemata selected

top-down

(proactively) and/or

bottom-up

(reactively)

•Two types of top-

down selection

–Context-driven

(automatic,

unconscious,

inflexible)

–Cognitive control

(effortful, conscious

”Zombie

system”

The zombie hypothesis

•Cognitive distraction

involves working m

emory

load

•Working m

emory requires

cognitive control to sustain

activation of schemata in

the absence of stimulus

input

•Lack of cognitive control

to other tasks -> zombie

behaviour

Environment

Sensing

Actuation

Lane keeing

Cognitive control

Schemata

”Zombie

system”

Phone

conversation

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 perform

ance

•Norm

al lane keeping

•Basic avoidance responses to closing objects (looming)

•Basic visual orientation to salient objects

•Context-driven im

plicit learning

–Im

paired perform

ance

•Novel/difficult lateral control tasks

•Fast braking responses to brake lights

•Utilisation of non-routine predictive cues

•Semantic interpretation and encoding

•Flexible adaptation

Evidence

•Left intact

–Norm

al lane keeping (Östlund et al., 2004; Törnros and Bolling, 2005; Engström

et al., 2005; Jamson and M

erat, 2005; Mattes, Föhl and Schindhelm, 2007; Merat

and Jamson, 2008)

–Basic avoidance responses to closing objects (looming) (M

uttart 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 im

plicit learning (Chun and Jiang, 1998; Engström et al., 2010)

•Im

paired

–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 W

ard, 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 (M

uttart 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)

Example data: Glances to oncoming car

0

0,51

1,52

2,53

3,5

12

34

56

Scenario exposure

Number of glances to car

No load

WM load

0

0,51

1,52

2,53

12

34

56

Exposure

Mean glance duration to car

No load

WM load

Number of glances towards car

Mean duration of glances towards car

Implicit learning (”crude adaptation”)

Flexible adaptation only for

non-loaded drivers

Zombie behaviour

Flexible adaptation

Zombie learning

-0,4

-0,20

0,2

0,4

0,6

0,81

1,2

12

34

56

Scenario exposure

Brake response time

No load

WM load

Example data: Brake onset tim

e

Zombie

response

Flexible adaptation

No loadW

M load

Example data: Anticipatory braking

65

43

21

rep

0,40

0,30

0,20

0,10

0,00

Proportion of

anticipatory

braking (before event

onset)

Zombie response

Flexible adaptation

No load

WM load

Example data: Visual response time for first exposure

10

WM

0,80

0,60

0,40

0,20

0,00

Mean RT

WM load

No load

Does cognitive distraction increase crash risk?

•At least not through delayed last-second avoidance

responses or impaired lane keeping

•However, m

ay 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…

Conclusions

•Cognitive distraction affects some aspects of driving

perform

ance but leave others intact

•Zombie hypothesis: Should only affect non-routine

activities

•Generally supported by existing data

•Further experimental w

ork is needed to further validate

the hypothesis