{"title":"基于眼动追踪的图像分类决策支持系统评价","authors":"Holly Zelnio, Mary E. Frame, Mary E. Fendley","doi":"10.1109/ICHMS49158.2020.9209524","DOIUrl":null,"url":null,"abstract":"This research project examines the effectiveness of Decision Support Systems (DSS) to improve object classification performance of imagery from three different image sensor types. Eye tracking analyses provide evidence that individuals are able to focus on information that is most crucial to classification while ignoring information that is less diagnostic within a DSS, without jeopardizing performance. This analysis augments previous work on this problem that addressed accuracy, confidence, and trust.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Eye Tracking to Evaluate Decision Support Systems of Imagery Classification\",\"authors\":\"Holly Zelnio, Mary E. Frame, Mary E. Fendley\",\"doi\":\"10.1109/ICHMS49158.2020.9209524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research project examines the effectiveness of Decision Support Systems (DSS) to improve object classification performance of imagery from three different image sensor types. Eye tracking analyses provide evidence that individuals are able to focus on information that is most crucial to classification while ignoring information that is less diagnostic within a DSS, without jeopardizing performance. This analysis augments previous work on this problem that addressed accuracy, confidence, and trust.\",\"PeriodicalId\":132917,\"journal\":{\"name\":\"2020 IEEE International Conference on Human-Machine Systems (ICHMS)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Human-Machine Systems (ICHMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHMS49158.2020.9209524\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHMS49158.2020.9209524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Eye Tracking to Evaluate Decision Support Systems of Imagery Classification
This research project examines the effectiveness of Decision Support Systems (DSS) to improve object classification performance of imagery from three different image sensor types. Eye tracking analyses provide evidence that individuals are able to focus on information that is most crucial to classification while ignoring information that is less diagnostic within a DSS, without jeopardizing performance. This analysis augments previous work on this problem that addressed accuracy, confidence, and trust.
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