{"title":"利用组合融合检测基于眼球运动的偏好","authors":"Christina Schweikert, S. Shimojo, D. F. Hsu","doi":"10.1109/ICCI-CC.2016.7862057","DOIUrl":null,"url":null,"abstract":"When tasked with comparing two images on a screen, a subject's eye movement can be captured and analyzed in order to understand the process of preference formation. The process of comparing two images and developing a preference is analyzed based on a sample dataset. Although it is known in general that our preferences are shaped by our past experiences, a systemic understanding of the factors which lead to preference decision making remains a challenging problem. In this paper, we propose a set of five attributes which are extracted from the temporal eye movement sequence: last duration, total duration, gaze count, interest sustainability, and region change. Each of these five attributes is a scoring system (ranking system). We then use the combinatorial fusion algorithm (CFA) framework to combine pairs of attributes using the rank-score characteristic (RSC) function and cognitive diversity (CD). Our results demonstrate that combination of two attributes can improve individual attributes if the attribute pair has a higher cognitive diversity. Our work represents a new paradigm to use combinatorial fusion for preference detection based on eye movement.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Detecting preferences based on eye movement using combinatorial fusion\",\"authors\":\"Christina Schweikert, S. Shimojo, D. F. Hsu\",\"doi\":\"10.1109/ICCI-CC.2016.7862057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When tasked with comparing two images on a screen, a subject's eye movement can be captured and analyzed in order to understand the process of preference formation. The process of comparing two images and developing a preference is analyzed based on a sample dataset. Although it is known in general that our preferences are shaped by our past experiences, a systemic understanding of the factors which lead to preference decision making remains a challenging problem. In this paper, we propose a set of five attributes which are extracted from the temporal eye movement sequence: last duration, total duration, gaze count, interest sustainability, and region change. Each of these five attributes is a scoring system (ranking system). We then use the combinatorial fusion algorithm (CFA) framework to combine pairs of attributes using the rank-score characteristic (RSC) function and cognitive diversity (CD). Our results demonstrate that combination of two attributes can improve individual attributes if the attribute pair has a higher cognitive diversity. Our work represents a new paradigm to use combinatorial fusion for preference detection based on eye movement.\",\"PeriodicalId\":135701,\"journal\":{\"name\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCI-CC.2016.7862057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2016.7862057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting preferences based on eye movement using combinatorial fusion
When tasked with comparing two images on a screen, a subject's eye movement can be captured and analyzed in order to understand the process of preference formation. The process of comparing two images and developing a preference is analyzed based on a sample dataset. Although it is known in general that our preferences are shaped by our past experiences, a systemic understanding of the factors which lead to preference decision making remains a challenging problem. In this paper, we propose a set of five attributes which are extracted from the temporal eye movement sequence: last duration, total duration, gaze count, interest sustainability, and region change. Each of these five attributes is a scoring system (ranking system). We then use the combinatorial fusion algorithm (CFA) framework to combine pairs of attributes using the rank-score characteristic (RSC) function and cognitive diversity (CD). Our results demonstrate that combination of two attributes can improve individual attributes if the attribute pair has a higher cognitive diversity. Our work represents a new paradigm to use combinatorial fusion for preference detection based on eye movement.