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

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Describes a modeling study of the strategic variations in distracted driving and their effects on driver performance. Demonstrates how a constraint modeling approach can be applied to complex dynamic tasks.

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

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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

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