a cognitive constraint model of dual-task trade-offs in a highly dynamic driving task

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

A Cognitive Constraint Model of Dual-Task Trade-offs in a

Highly Dynamic Driving Task

Duncan BrumbyAndrew HowesDario 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

• 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

doing more than one thing at once

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

Task BSwitch Cost

Time (s)

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

explore permutations ...

Task A

Task BSwitch Cost

Time (s)

Task A

Task BSwitch Cost

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

Task B: steeringswitch cost

Time (s)Late

ral D

evia

tion

(m)

Diverge

Converge

Center of road

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

• analyze human steering data to estimate basic driving model parameters

• express trends in data as functions of time and the vehicle's lateral deviation

parameterizing the model

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 episodesLateral 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

as the car gets further from the lane center, velocity of correction to center increases

€

v =dt

Lateral Velocity = 0.1756 x Lateral Deviation + 0.1034 where,

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 = FastestC1F = fastest 3-4 chunking

C2F = fastest 3-2-2 chunkingC1S = 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 = FastestC1F = fastest 3-4 chunking

C2F = fastest 3-2-2 chunkingC1S = safest 3-4 chunking

C2S = safest 3-2-2 chunking SF = safest

xxxxxxxxx

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off

FA = FastestC1F = fastest 3-4 chunking

C2F = fastest 3-2-2 chunkingC1S = safest 3-4 chunking

C2S = safest 3-2-2 chunking SF = safest

x-x-x-x-x-x-x-x-xx-x-x-x-x-x-x-xx

vs.

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off

FA = FastestC1F = fastest 3-4 chunking

C2F = fastest 3-2-2 chunkingC1S = safest 3-4 chunking

C2S = safest 3-2-2 chunking SF = safest

x-xxx-xxxx-x

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off

FA = FastestC1F = fastest 3-4 chunking

C2F = fastest 3-2-2 chunkingC1S = safest 3-4 chunking

C2S = safest 3-2-2 chunking SF = safest

x-xxx-xx-xx-x

Duncan Brumby, Drexel University | Brumby@cs.drexel.edu

results: a speed/accuracy trade-off

FA = FastestC1F = fastest 3-4 chunking

C2F = fastest 3-2-2 chunkingC1S = 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