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

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

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

30 Citations (Scopus)

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
Number of pages15
JournalJournal of eye movement research
Volume5
Issue number1
Publication statusPublished - 1 Jan 2012

Structured keywords

  • Cognitive Science
  • Visual Perception

Keywords

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

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