{"title":"AM-DCT:用于调查用户界面监控行为的视觉注意力建模数据捕获工具","authors":"S. Feuerstack, Bertram Wortelen","doi":"10.1145/2909132.2909276","DOIUrl":null,"url":null,"abstract":"Methods to get insights about users' monitoring behavior either depend on the expertise of Human Factor experts to model and predict stereotypic monitoring behavior or on performing eye tracking studies in simulated environments, which require subjects to be physically present and usually to be tested successively. AM-DCT is a tool that can be applied by domain experts without expertise in human factors and with limited training in parallel sessions to learn about a population's monitoring behavior. In an experiment 20 car drivers used the AM-DCT independently after watching a 15 minutes video tutorial. 19 subjects were able to model their monitoring behavior for a car overtaking scenario in 36 minutes on average. The identification of areas of interest for areas with clearly defined borders was very consistent among subjects. For those without clear borders an aggregated model of all participants seems surprisingly accurate to represent the real monitoring area.","PeriodicalId":250565,"journal":{"name":"Proceedings of the International Working Conference on Advanced Visual Interfaces","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"AM-DCT: A Visual Attention Modeling Data Capturing Tool for Investigating Users' Interface Monitoring Behavior\",\"authors\":\"S. Feuerstack, Bertram Wortelen\",\"doi\":\"10.1145/2909132.2909276\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Methods to get insights about users' monitoring behavior either depend on the expertise of Human Factor experts to model and predict stereotypic monitoring behavior or on performing eye tracking studies in simulated environments, which require subjects to be physically present and usually to be tested successively. AM-DCT is a tool that can be applied by domain experts without expertise in human factors and with limited training in parallel sessions to learn about a population's monitoring behavior. In an experiment 20 car drivers used the AM-DCT independently after watching a 15 minutes video tutorial. 19 subjects were able to model their monitoring behavior for a car overtaking scenario in 36 minutes on average. The identification of areas of interest for areas with clearly defined borders was very consistent among subjects. For those without clear borders an aggregated model of all participants seems surprisingly accurate to represent the real monitoring area.\",\"PeriodicalId\":250565,\"journal\":{\"name\":\"Proceedings of the International Working Conference on Advanced Visual Interfaces\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the International Working Conference on Advanced Visual Interfaces\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2909132.2909276\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Working Conference on Advanced Visual Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2909132.2909276","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AM-DCT: A Visual Attention Modeling Data Capturing Tool for Investigating Users' Interface Monitoring Behavior
Methods to get insights about users' monitoring behavior either depend on the expertise of Human Factor experts to model and predict stereotypic monitoring behavior or on performing eye tracking studies in simulated environments, which require subjects to be physically present and usually to be tested successively. AM-DCT is a tool that can be applied by domain experts without expertise in human factors and with limited training in parallel sessions to learn about a population's monitoring behavior. In an experiment 20 car drivers used the AM-DCT independently after watching a 15 minutes video tutorial. 19 subjects were able to model their monitoring behavior for a car overtaking scenario in 36 minutes on average. The identification of areas of interest for areas with clearly defined borders was very consistent among subjects. For those without clear borders an aggregated model of all participants seems surprisingly accurate to represent the real monitoring area.