Attention, Perception, & Psychophysics

, Volume 81, Issue 8, pp 2798–2813 | Cite as

Detection of brake lights while distracted: Separating peripheral vision from cognitive load

  • Benjamin WolfeEmail author
  • Ben D. Sawyer
  • Anna Kosovicheva
  • Bryan Reimer
  • Ruth Rosenholtz


Drivers rarely focus exclusively on driving, even with the best of intentions. They are distracted by passengers, navigation systems, smartphones, and driver assistance systems. Driving itself requires performing simultaneous tasks, including lane keeping, looking for signs, and avoiding pedestrians. The dangers of multitasking while driving, and efforts to combat it, often focus on the distraction itself, rather than on how a distracting task can change what the driver can perceive. Critically, some distracting tasks require the driver to look away from the road, which forces the driver to use peripheral vision to detect driving-relevant events. As a consequence, both looking away and being distracted may degrade driving performance. To assess the relative contributions of these factors, we conducted a laboratory experiment in which we separately varied cognitive load and point of gaze. Subjects performed a visual 0-back or 1-back task at one of four fixation locations superimposed on a real-world driving video, while simultaneously monitoring for brake lights in their lane of travel. Subjects were able to detect brake lights in all conditions, but once the eccentricity of the brake lights increased, they responded more slowly and missed more braking events. However, our cognitive load manipulation had minimal effects on detection performance, reaction times, or miss rates for brake lights. These results suggest that, for tasks that require the driver to look off-road, the decrements observed may be due to the need to use peripheral vision to monitor the road, rather than due to the distraction itself.


Attention Scene perception Visual perception 



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Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  • Benjamin Wolfe
    • 1
    Email author
  • Ben D. Sawyer
    • 2
    • 3
  • Anna Kosovicheva
    • 4
  • Bryan Reimer
    • 2
  • Ruth Rosenholtz
    • 1
    • 5
  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.AgeLabMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Department of Industrial EngineeringUniversity of Central FloridaOrlandoUSA
  4. 4.Department of PsychologyNortheastern UniversityBostonUSA
  5. 5.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA

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