Frequent Pattern Mining of Eye-Tracking Records Partitioned into Cognitive Chunks. (23rd November 2014)

• Record Type: Journal Article
• Authors: Matsuda, Noriyuki; Takeuchi, Haruhiko
• Other Names: Yang Yongqing Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2014(2014)
• Publication Date: 2014-11-23
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2014/101642
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : 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 5 × 5 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.