A simple way to estimate similarity between pairs of eye movement sequences

Sebastiaan Mathot*, Filipe Cristino, Iain D. Gilchrist, Jan Theeuwes

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

30 Citations (Scopus)
110 Downloads (Pure)

Abstract

We propose a novel algorithm to estimate the similarity between a pair of eye movement sequences. The proposed algorithm relies on a straight-forward geometric representation of eye movement data. The algorithm is considerably simpler to implement and apply than existing similarity measures, and is particularly suited for exploratory analyses. To validate the algorithm, we conducted a benchmark experiment using realistic artificial eye movement data. Based on similarity ratings obtained from the proposed algorithm, we defined two clusters in an unlabelled set of eye movement sequences. As a measure of the al gorithm's sensitivity, we quantified the extent to which these data-driven clusters matched two pre-defined groups (i.e., the 'real' clusters). The same analysis was performed using two other, commonly used similarity measures. The results show that the proposed algorithm is a viable similarity measure.

Original languageEnglish
Article number4
Pages (from-to)1-15
Number of pages15
JournalJournal of Eye Movement Research
Volume5
Issue number1
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Eye movements
  • Distance
  • Similarity
  • Scanpaths
  • Methodology
  • VISUAL-ATTENTION
  • SCANPATHS
  • SEARCH
  • PERCEPTION
  • FIXATIONS
  • PATTERNS
  • IMAGERY
  • SCENE

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