A Cognitive Constraint Model of
Dual-Task Trade-offs in a
Highly Dynamic Driving Task
Duncan Brumby
Andrew Howes
Dario Salvucci
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

questions to address ...
• why do people interleave tasks rather than
completing one task before moving to another?
• when in a task are people likely to switch?
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

scope of behavioral adaptations are
bound by constraints
• deprived of regular attention driving
performance rapidly declines with
potentially disastrous consequences
• ... but switching between tasks carries costs
• benefits of frequently interleaving tasks play
against the costs of switching between them
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

overview of talk
• background
- the problem with doing more than one thing at once
• model
- a cognitive constraint model of distracted driving
• results
- a speed/accuracy trade-off
• conclusions
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

doing more than one thing at once
• people frequently use a mobile device while
doing something else ...
- we listen to our iPod while walking through the city
- we use a cell phone while we are driving
• there is clearly a problem with doing this ...
- “iPod oblivion” lead New York City to contemplate banning
pedestrian iPod use on city streets (toptechnews.com, Feb. 2007)
- driver distraction is a major contributing cause of traffic accidents
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

what’s the cause of the problem?
• psychological constraints limit task parallelism
- to drive we have to look at the road
- ... to write a SMS text message we have to look at the phone
- ... but the eyes have a limited field of effective view
- ... and this will lead to potential bottlenecks
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

how might limited resources be divided
between two or more continuous tasks?
• simple model
- at any given time task A or task B can be “active”
- model the information flow between tasks
- assume that switching between tasks carries a time cost
(Allport, Styles, & Hsieh, 1994, Attention & Performance XV)
Task A
Switch Cost
Task B
Time (s)
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

explore permutations ...
Task A
Switch Cost
Task B
Time (s)
Task A
Switch Cost
Task B
Time (s)
for a 9-key task there are 28 (or 256) possible strategic variations!
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

when should one switch between
tasks?
• Payne, Duggan, & Neth (in press, JEP:General)
- moment-to-moment decision to switch is dependent on
characteristics of the current task
- found that task switching behavior is explained by optimal
foraging theory (Green, 1984; Stepthens & Krebs, 1986)
• people are sensitive to the task environment
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

in a highly dynamic driving task
• ... safety clearly matters
• a common surrogate measure is lateral
deviation of vehicle from lane center
• aim to develop a model that predicts changes
in lateral deviation under dual-task conditions
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

model of distracted driving
Task A: dialing
switch cost
Task B: steering
Lateral Deviation (m)
Con
verg
rge e
e
v
Di
Center of
road
Time (s)
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

parameterizing the model
• analyze human steering data to estimate
basic driving model parameters
• express trends in data as functions of time
and the vehicle's lateral deviation
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

steering episodes
• episodes are defined as periods where the
angle of the steering wheel does not alter
• divergent steering episodes,
- when initial lateral deviation is less than at the end
• convergent steering episodes,
- when initial lateral deviation is greater than at the end
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

analysis of divergent steering episodes
Lateral Deviation = 0.2833 x Duration
with increasing time between steering updates,
deviation from lane center increases
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

analysis of convergent steering episodes
Lateral Velocity = 0.1756 x Lateral Deviation + 0.1034
d
where, v =
t
€
as the car gets further from the lane center,
velocity of correction to center increases
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

dial task (based on Salvucci, 2001, IJHCS)
• enter a 7-digit number
- (+ “power-on” and “send” key-presses = 9 key-presses in total)
• each key-press takes 310 ms
- 50 ms for recalling the digit
- 50 ms step of cognition, where the motor response is initiated
- 150 ms motor preparation and 60 ms motor execution for the key
press
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

dial task (based on Salvucci, 2001, IJHCS)
• have to move the hand to and from phone,
each taking 800 ms
• switch cost of 185 ms, representing movement
of eyes to and from phone, or vice versa
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

a systematic evaluation of the
strategy space
• every possible interleaving strategy was
evaluated, but also ...
• enumerated over durations of steering update
- updates 0.15 s to 1.5 s were explored at 0.15 s increments
- in total, 262,701 strategies evaluated
- each strategy was run 50 times and performance averaged
• interest in lateral deviation and dial task time
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off
FA = Fastest
C1F = fastest 3-4 chunking
C2F = fastest 3-2-2 chunking
C1S = safest 3-4 chunking
C2S = safest 3-2-2 chunking
SF = safest
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off
xxxxxxxxx
FA = Fastest
C1F = fastest 3-4 chunking
C2F = fastest 3-2-2 chunking
C1S = safest 3-4 chunking
C2S = safest 3-2-2 chunking
SF = safest
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off
x-x-x-x-x-x-x-x-x
vs.
x-x-x-x-x-x-x-xx
FA = Fastest
C1F = fastest 3-4 chunking
C2F = fastest 3-2-2 chunking
C1S = safest 3-4 chunking
C2S = safest 3-2-2 chunking
SF = safest
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off
x-xxx-xxxx-x
FA = Fastest
C1F = fastest 3-4 chunking
C2F = fastest 3-2-2 chunking
C1S = safest 3-4 chunking
C2S = safest 3-2-2 chunking
SF = safest
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off
x-xxx-xx-xx-x
FA = Fastest
C1F = fastest 3-4 chunking
C2F = fastest 3-2-2 chunking
C1S = safest 3-4 chunking
C2S = safest 3-2-2 chunking
SF = safest
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off
FA = Fastest
C1F = fastest 3-4 chunking
C2F = fastest 3-2-2 chunking
C1S = safest 3-4 chunking
C2S = safest 3-2-2 chunking
SF = safest
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

summary
• analysis explored implications of constraints
from environment and on cognition for behavior
• given these constraints, we analyzed the speed
and safety of the set of possible strategies
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

summary
• allows full evaluation of strategy space
• derive predictions of performance brackets
- fastest possible and slowest reasonable (c.f. Kieras & Meyer, 2000)
• rather than using model to fit data, we can
explain why people prefer one strategy over
another, in terms of speed/accuracy trade-off
Duncan Brumby, Drexel University | Brumby@cs.drexel.edu