Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining eye movements and collaborative filtering for proactive information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
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To examine the feasibility of estimating the degree of strength of belief (SOB) of responses using eye movements, the scan paths of eye movements were analyzed while subjects reviewed their own responses to multiple choice tasks. All fixation points of eye movements were classified into visual areas, or cells, which corresponded with the positions of answers. Two estimation procedures are proposed using eye-movement data. The first one is identifying SOB using scan-path transitions. By comparing subject's reports of high and low SOB and eye-movement estimations, a significant correct rate of discrimination of SOB was observed. When the threshold of discrimination was controlled, a high rate of correct responses was obtained if it was set at a low level. The second procedure is conducting SOB discrimination using support vector machines (SVM) trained with features of fixations. Subject's gazing features were analyzed while they reviewed their own responses. A discrimination model for SOB was trained with several combinations of features to see whether performance of a significant level could be obtained. As a result, a trained model with 3 features (which consist of interval time, vertical difference, and length between fixations) can provide significant discrimination performance for SOB. These results provide evidence that strength of belief can be estimated using eye movements