Classification framework to identify similar visual scan paths using multiple similarity metrics
Analyzing visual scan paths, the time-ordered sequence of eye fixations and saccades, can help us understand how operators visually search the environment before making a decision. To analyze and compare visual scan paths, prior studies have used metrics such as string edit similarity, which conside...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Series: | Journal of Eye Movement Research |
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| Online Access: | https://bop.unibe.ch/JEMR/article/view/11207 |
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| _version_ | 1849768529380769792 |
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| author | Ricardo Palma Fraga Ziho Kang Jerry Crutchfield |
| author_facet | Ricardo Palma Fraga Ziho Kang Jerry Crutchfield |
| author_sort | Ricardo Palma Fraga |
| collection | DOAJ |
| description | Analyzing visual scan paths, the time-ordered sequence of eye fixations and saccades, can help us understand how operators visually search the environment before making a decision. To analyze and compare visual scan paths, prior studies have used metrics such as string edit similarity, which considers the order used to inspect areas of interest (AOIs), as well as metrics that consider the AOIs shared between visual scan paths. However, to identify similar visual scan paths, particularly in tasks and environments in which operators may apply variations of a common underlying visual scanning behavior, using solely one similarity metric might not be sufficient. In this study, we introduce a classification framework using a combination of the string edit algorithm and the Jaccard coefficient similarity. We applied our framework to the visual scan paths of nine tower controllers in a high-fidelity simulator when a “clear-to-take-off” clearance was issued. The classification framework was able to provide richer and more meaningful classifications of the visual scan paths compared to the results when using either the string edit algorithm or Jaccard coefficient similarity.
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| format | Article |
| id | doaj-art-8f5ff3b4681f44df835ef1b41737dfd7 |
| institution | DOAJ |
| issn | 1995-8692 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-8f5ff3b4681f44df835ef1b41737dfd72025-08-20T03:03:45ZengMDPI AGJournal of Eye Movement Research1995-86922024-08-0117310.16910/jemr.17.3.4Classification framework to identify similar visual scan paths using multiple similarity metricsRicardo Palma Fraga0Ziho Kang1Jerry Crutchfield2University of OklahomaUniversity of OklahomaFederal Aviation AdministrationAnalyzing visual scan paths, the time-ordered sequence of eye fixations and saccades, can help us understand how operators visually search the environment before making a decision. To analyze and compare visual scan paths, prior studies have used metrics such as string edit similarity, which considers the order used to inspect areas of interest (AOIs), as well as metrics that consider the AOIs shared between visual scan paths. However, to identify similar visual scan paths, particularly in tasks and environments in which operators may apply variations of a common underlying visual scanning behavior, using solely one similarity metric might not be sufficient. In this study, we introduce a classification framework using a combination of the string edit algorithm and the Jaccard coefficient similarity. We applied our framework to the visual scan paths of nine tower controllers in a high-fidelity simulator when a “clear-to-take-off” clearance was issued. The classification framework was able to provide richer and more meaningful classifications of the visual scan paths compared to the results when using either the string edit algorithm or Jaccard coefficient similarity. https://bop.unibe.ch/JEMR/article/view/11207eye movementscan pathgazeeye trackingair traffic controltower control |
| spellingShingle | Ricardo Palma Fraga Ziho Kang Jerry Crutchfield Classification framework to identify similar visual scan paths using multiple similarity metrics Journal of Eye Movement Research eye movement scan path gaze eye tracking air traffic control tower control |
| title | Classification framework to identify similar visual scan paths using multiple similarity metrics |
| title_full | Classification framework to identify similar visual scan paths using multiple similarity metrics |
| title_fullStr | Classification framework to identify similar visual scan paths using multiple similarity metrics |
| title_full_unstemmed | Classification framework to identify similar visual scan paths using multiple similarity metrics |
| title_short | Classification framework to identify similar visual scan paths using multiple similarity metrics |
| title_sort | classification framework to identify similar visual scan paths using multiple similarity metrics |
| topic | eye movement scan path gaze eye tracking air traffic control tower control |
| url | https://bop.unibe.ch/JEMR/article/view/11207 |
| work_keys_str_mv | AT ricardopalmafraga classificationframeworktoidentifysimilarvisualscanpathsusingmultiplesimilaritymetrics AT zihokang classificationframeworktoidentifysimilarvisualscanpathsusingmultiplesimilaritymetrics AT jerrycrutchfield classificationframeworktoidentifysimilarvisualscanpathsusingmultiplesimilaritymetrics |