Eye Tracking to Evaluate the Effects of Interruptions and Workload in a Complex Task

Hum Factors. 2022 Nov;64(7):1168-1180. doi: 10.1177/0018720821990487. Epub 2021 Feb 8.

Abstract

Objective: To use eye tracking to understand the effects of interruptions in different workload conditions as part of a monitoring and change detection task.

Background: Interruptions are detrimental to performance in complex, multitasking domains. There is a need for better display design techniques that help users overcome interruptions regardless of their workload level. This requires understanding a user's attentional state immediately after an interruption in order to determine what type of display adjustments are most suitable.

Method: An emergency dispatching simulator was developed with a visual primary task and auditory interruptive task. Two levels of workload were induced by varying the number of emergency vehicles to monitor for changes and the rate of changes to monitor. Eye tracking, performance, and subjective measures (NASA-Task Load Index) were collected and analyzed for 41 participants.

Results: As expected, high workload interacted with interruptions to further degrade primary task performance and alter participants' attention allocation immediately after the interruption. Participants in the high workload condition had more narrowed, slower scan patterns immediately after the interruption as compared to before the interruption, as evidenced by lower scanpath length per second and mean saccade amplitude. However, this change was not observed in low workload.

Conclusion: High workload modulates the effects of interruptions on performance and eye movements. Users in the high workload condition struggle to quickly scan the display in the seconds following an interruption.

Application: The results can provide insight into the type of display adjustments needed right after an interruption in a high-workload environment.

Keywords: eye tracking; interruptions; monitoring; workload.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Attention
  • Eye-Tracking Technology*
  • Humans
  • Task Performance and Analysis
  • Workload*