Eye-movement data: Cumulative fixation time and cluster analysis

This paper describes procedures for the visual presentation and cluster analysis of eye-movement data. Methods for two- and three-dimensional representation of cumulative fixation time (CFT) and ways of enhancing the peaks in CFT distributions are outlined and illustrated by reference to examples from eye-movement studies of cognitive processes. CFT distributions may also be partitioned using thek means clustering technique (MacQueen, 1967), and applications of variants of this technique to eye-movement data are discussed. Cluster analyses such ask means require the user to make initial estimates of the number and value of the means. One classification procedure (Wallace & Boulton, 1968a, 1968b), based on information theory, avoids these initial assumptions. This procedure is applied to a CFT distribution and has its solution compared with that ofk means for the same distribution. Finally, programs that implement these procedures on Macintosh computers are listed and offered on floppy disk.

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