Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Characterizing Task Performance Via Eye Tracking
Author Affiliations & Notes
  • Sydney Walker
    Columbia University, New York, New York, United States
  • Ye Tian
    Columbia University, New York, New York, United States
  • Eric Seemiller
    Air Force Research Laboratory 711th Human Performance Wing, Wright-Patterson AFB, Ohio, United States
  • Marc Winterbottom
    Air Force Research Laboratory 711th Human Performance Wing, Wright-Patterson AFB, Ohio, United States
  • Kaveri Thakoor
    Columbia University, New York, New York, United States
    Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
  • Footnotes
    Commercial Relationships   Sydney Walker None; Ye Tian None; Eric Seemiller None; Marc Winterbottom None; Kaveri Thakoor Topcon, Code F (Financial Support)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 3332. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Sydney Walker, Ye Tian, Eric Seemiller, Marc Winterbottom, Kaveri Thakoor; Characterizing Task Performance Via Eye Tracking. Invest. Ophthalmol. Vis. Sci. 2024;65(7):3332.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : Eye-tracking is a useful tool to determine the feasibility of telerobotic surgery given that it can be used to evaluate impairments in the performance of surgeons when stereoscopic vision is altered. We analyzed data from a simulated, stereoscopic distortion experiment to uncover ocular motility adaptations in response to vision-distortions.

Methods : Based on eye movement data collected in a simulated experiment (38 subjects were asked to manipulate a joystick to drop a ball into a receptacle that was randomly placed in a field before them 20 to 40 feet ahead, +/- 5 feet horizontally and 7 feet above), a principal component analysis (PCA) was performed to determine which eye metrics most explained the variance in task performance, as measured through error from target. Additionally, the trials were sorted into early, middle, and late intervals, and a time-binned statistical analysis was conducted to evaluate how the subjects’ performance varied throughout the course of the experiment. Beyond this, a 1D Multilayer Perceptron (MLP) was used to regress (predict) error and classify the time bin of a trial using saccade duration, saccade amplitude, and average saccade velocity as features.

Results : The PCA analysis indicated that saccade amplitude and duration explained most of the variance in task performance, with average loadings of .688 and .634, respectively. A two tailed t-test of the time-binned analysis revealed evidence of improved performance in the middle trials but a decline during the late trials as measured by average error. The difference in task error between each of the time bins (early-middle and middle-late) were statistically significant with a p-value of less than 0.0001. The MLP model used to regress error and classify time bins achieved 0.71 accuracy on classifying early groups from middle and late groups and found that saccade amplitude was the most predictive feature. Saccade duration enhanced model performance towards imbalanced data and improved AUC to 0.65. Using all three features to regress the error, the mean absolute error (MAE) decreased to 0.04.

Conclusions : The analyses showed evidence of various adaptations in response to vision-distortions. Subjects showed a learning effect coupled with a fatigue effect, improving performance in the middle trials but worsening performance in the late trials. The analyses also indicated that saccade amplitude is most insightful in understanding variation in ocular motility.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×