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

Keywords: Eye tracking, visual attention, individual differences, art perception, architectural design, machine learning, classification, region of interest

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.

Author Biographies

Sangwon Lee, Yonsei University, Seoul, South Korea

Associate professor, Department of Human Environment and Design

Senior Software Engineer, Intel Corporation, Oregon, U.S.

Ph.D. in Computer Science, Norhtwestern University

M.S. in Computational Design, Carnegie Mellon University

B.S. in Architecture, Seoul National University

Sihyeong Ahn, Yonsei University, Seoul, South Korea
Department of Human Environment and Design
Jaewan Park, Yonsei University, Seoul, South Korea
Department of Human Environment and Design
Published
16-07-2019
How to Cite
Lee, S., Hwang, Y., Jin, Y., Ahn, S., & Park, J. (2019). Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning. Journal of Eye Movement Research, 12(2). https://doi.org/10.16910/jemr.12.2.4
Section
Articles