Getting more out of Area of Interest (AOI) analysis with SPLOT

Artem V. Belopolsky*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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To analyze eye-tracking data the viewed image is often divided into areas of interest (AOI). However, the temporal dynamics of eye movements towards the AOI is often lost either in favor of summary statistics (e.g., proportion of fixations or dwell time) or is significantly reduced by "binning" the data and computing the same summary statistic over each time bin. This paper introduces SPLOT: smoothed proportion of looks over time method for analyzing the eye movement dynamics across AOI. SPLOT comprises of a complete workflow, from visualization of the time-course to performing statistical analysis on it using cluster-based permutation testing. The possibilities of SPLOT are illustrated by applying it to an existing dataset of eye movements of radiologists diagnosing a chest X-ray.

Original languageEnglish
Title of host publicationETRA 2020 Short Papers
Subtitle of host publicationACM Symposium on Eye Tracking Research and Applications
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450371346
Publication statusPublished - Jun 2020
Event2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020 - Stuttgart, Germany
Duration: 2 Jun 20205 Jun 2020

Publication series

NameEye Tracking Research and Applications Symposium (ETRA)


Conference2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020


  • Area Of Interest analysis
  • Cluster-based permutation
  • Eye movement dynamics
  • Eye Tracking
  • Proportion of looks over time


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