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Measuring Focused Attention Using Fixation Inner-Density

  • Wen Liu
  • Soussan Djamasbi
  • Andrew C. Trapp
  • Mina Shojaeizadeh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10916)

Abstract

Examining user reactions via the unobtrusive method of eye tracking is becoming increasingly popular in user experience studies. A major focus of this type of research is accurately capturing user attention to stimuli, which is typically established by translating raw eye movement signals into fixations, that is, ocular events characterized by relatively stable gaze over a specific stimulus. Grounded in the argument that inner-density of gaze points within a fixation represents focused attention, a recent study has developed the fixation-inner-density (FID) methodology, which identifies fixations based on the compactness of individual gaze points. In this study we compare the FID filter with a widely used method of fixation identification, namely the I-VT filter. To do so we use a set of measures that investigate the distribution of gaze points at a micro-level, that is, the patterns of individual gaze points within each fixation. Our results show that in general fixations identified by the FID filter are significantly denser and more compact around their fixation center. They are also more likely to have randomly distributed gaze points within the square box that spatially bounds a fixation. Our results also show that fixation duration is significantly different between the two methods. Because fixation is a major unit of analysis in behavioral studies and fixation duration is a major representation of the intensity of attention, awareness, and effort, our results suggest that the FID filter is likely to increase the sensitivity of such eye tracking investigations into behavior.

Keywords

Eye tracking Fixation identification Fixation-inner-density Fixation micro-patterns 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Wen Liu
    • 1
  • Soussan Djamasbi
    • 1
  • Andrew C. Trapp
    • 1
  • Mina Shojaeizadeh
    • 1
  1. 1.User Experience and Decision Making Research LaboratoryWorcester Polytechnic InstituteWorcesterUSA

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