Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning

Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

Research output: Contribution to journalArticle

Abstract

Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. Using its capability to identify distinguishing patterns among multi-dimensional data, we investigated the impact of three factors on the observation of architectural scenes: individuality, education, and image stimuli. An analysis of the eye-tracking data revealed that (1) a velocity histogram was unique to individuals, (2) students of architecture and other disciplines could be distinguished via endogenous parameters, but (3) they were more distinct in terms of seeking structural versus symbolic elements. Because of the reverse nature of the classification algorithms that automatically learn from data, we could identify relevant parameters and distinguishing eye-tracking patterns that have not been reported in previous studies.

Original languageEnglish
Article number4
JournalJournal of Eye Movement Research
Volume12
Issue number2
DOIs
Publication statusPublished - 2019 Jan 1

All Science Journal Classification (ASJC) codes

  • Ophthalmology
  • Sensory Systems

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