Subjective Workload Ratings and Eye Movement Activity Measures
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Subjective Workload Ratings and Eye Movement Activity Measures

Filetype[PDF-461.03 KB]


English

Details:

  • Creators:
  • Corporate Creators:
  • Subject/TRT Terms:
  • Publication/ Report Number:
  • Resource Type:
  • Geographical Coverage:
  • Corporate Publisher:
  • Abstract:
    In the present study, we evaluated the possibility of using eye movement activity measures as a correlate of cognitive workload. Using data from a high-fidelity human-in-the-loop weather simulation, we explored eye activity measures like pupil diameter, blink duration, and saccade distance, and assessed their relationship to subjective workload ratings. In our initial analysis, we established that although there was no significant effect of weather tool use on subjective workload ratings, there was a significant relationship between subjective workload ratings and our task load variable aircraft density. We found a linear increase in workload ratings with an increasing number of aircraft in the sector. In a subsequent analysis, we assessed the relationship between eye movement activity measures and aircraft density. We found that the mean blink duration and the mean saccade distance decreased as aircraft density increased, while the mean pupil diameter increased with an increasing number of aircraft in the sector. After establishing the relationship between these eye activity metrics and subjective workload, we evaluated whether we could use changes in eye movement activity along with other system state variables, like distance to weather from the outer marker, to measure ongoing controller workload. We developed both individual controller models and a general model (across controllers) to assess whether it was possible to predict the minute-by-minute number of aircraft in the sector. Using both multiple regression modeling and neural network models, we were able to produce individual controller models and general models with good prediction performances. We discuss possible applications for these findings in future air traffic control (ATC) research, in adaptive automation, and in ATC interface design.
  • Format:
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at rosap.ntl.bts.gov