Eye-movement data: Cumulative fixation time and cluster analysis

Abstract

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 the k means clustering technique (MacQueen, 1967), and applications of variants of this technique to eye-movement data are discussed. Cluster analyses such as k 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 of k means for the same distribution. Finally, programs that implement these procedures on Macintosh computers are listed and offered on floppy disk. © 1988 Psychonomic Society, Inc.