Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning
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 ar...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Journal of Eye Movement Research |
| Subjects: | |
| Online Access: | https://bop.unibe.ch/JEMR/article/view/4559 |
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| _version_ | 1849726979748659200 |
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| author | Sangwon Lee Yongha Hwang Yan Jin Sihyeong Ahn Jaewan Park |
| author_facet | Sangwon Lee Yongha Hwang Yan Jin Sihyeong Ahn Jaewan Park |
| author_sort | Sangwon Lee |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-249d7f1465414a48abc26bf80e74a2b3 |
| institution | DOAJ |
| issn | 1995-8692 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-249d7f1465414a48abc26bf80e74a2b32025-08-20T03:09:59ZengMDPI AGJournal of Eye Movement Research1995-86922019-07-0112210.16910/jemr.12.2.4Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learningSangwon Lee0Yongha Hwang1Yan Jin2Sihyeong Ahn3Jaewan Park4Yonsei University, Seoul, South KoreaSpace Information and Planning, University of Michigan, 2800 Plymouth Rd Ann Arbor, MI 48109Qingdao University of Technology Qingdao, ChinaYonsei University, Seoul, South KoreaYonsei University, Seoul, South KoreaMachine 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.https://bop.unibe.ch/JEMR/article/view/4559Eye trackingvisual attentionindividual differencesart perceptionarchitectural designmachine learning |
| spellingShingle | Sangwon Lee Yongha Hwang Yan Jin Sihyeong Ahn Jaewan Park Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning Journal of Eye Movement Research Eye tracking visual attention individual differences art perception architectural design machine learning |
| title | Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning |
| title_full | Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning |
| title_fullStr | Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning |
| title_full_unstemmed | Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning |
| title_short | Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning |
| title_sort | effects of individuality education and image on visual attention analyzing eye tracking data using machine learning |
| topic | Eye tracking visual attention individual differences art perception architectural design machine learning |
| url | https://bop.unibe.ch/JEMR/article/view/4559 |
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