文章基本信息

标题：Frequent Pattern Mining of Eye-Tracking Records Partitioned into Cognitive Chunks
本地全文：下载
作者：Noriyuki Matsuda ; Haruhiko Takeuchi
期刊名称：Applied Computational Intelligence and Soft Computing
印刷版ISSN：1687-9724
电子版ISSN：1687-9732
出版年度：2014
卷号：2014
DOI：10.1155/2014/101642
出版社：Hindawi Publishing Corporation
摘要：Assuming that scenes would be visually scanned by chunking information, we partitioned fixation sequences of web page viewers into chunks using isolate gaze point(s) as the delimiter. Fixations were coded in terms of the segments in a mesh imposed on the screen. The identified chunks were mostly short, consisting of one or two fixations. These were analyzed with respect to the within- and between-chunk distances in the overall records and the patterns (i.e., subsequences) frequently shared among the records. Although the two types of distances were both dominated by zero- and one-block shifts, the primacy of the modal shifts was less prominent between chunks than within them. The lower primacy was compensated by the longer shifts. The patterns frequently extracted at three threshold levels were mostly simple, consisting of one or two chunks. The patterns revealed interesting properties as to segment differentiation and the directionality of the attentional shifts.