Advertisement

Density of Gaze Points Within a Fixation and Information Processing Behavior

  • Mina ShojaeizadehEmail author
  • Soussan Djamasbi
  • Andrew C. Trapp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9737)

Abstract

The use of eye movements to study cognitive effort is becoming increasingly important in HCI research. Eye movements are natural and frequently occurring human behavior. In particular fixations represent attention; people look at something when they want to acquire information from it. Users also tend to cluster their attention on informative regions of a visual stimulus. Thus, fixation duration is often used to measure attention and cognitive processing. Additionally, parameters such as pupil dilation and fixation durations have also been shown to be representative of information processing. In this study we argue that fixation density, defined as the number of gaze points divided by the total area of a fixation event, can serve as a proxy for information processing. As such, fixation density has a significant relationship with pupil data and fixation duration, which have been shown to be representative of cognitive effort and information processing.

Keywords

Eye tracking Cognitive effort Information processing Pupil dilation Pupil dilation variation Fixation density 

References

  1. 1.
    Djamasbi, S.: Eye tracking and web experience. AIS Trans. Hum.-Comput. Interact. 6(2), 37–54 (2014)Google Scholar
  2. 2.
    Bergstrom, J.C.R., Olmsted-Hawala, E.L., Bergstrom, H.C.: Older adults fail to see the periphery in a web site task. Univ. Access Inf. Soc. 1, 1–10 (2014)Google Scholar
  3. 3.
    Poole, A., Ball, L.J.: Eye tracking in HCI and usability research. Encycl. Hum. Comput. Interact. 1, 211–219 (2006)CrossRefGoogle Scholar
  4. 4.
    Pan, B., Hembrooke, H., Gay, G., Granka, L., Feusner, M., Newman, J.: The determinants of web page viewing behavior: an eye tracking study. In: Proceedings of the 2004 Symposium on Eye Tracking Research and Applications, pp. 147 – 154 (2004)Google Scholar
  5. 5.
    Klingner, J., Tversky, B., Hanrahan, P.: Effects of visual and verbal presentation on cognitive load in vigilance, memory, and arithmetic tasks. Psychophysiology 48(3), 323–332 (2011)CrossRefGoogle Scholar
  6. 6.
    Buettner, R., Sauer, S., Maier, C., Eckhardt, A.: Towards Ex ante prediction of user performance: a novel NeuroIS methodology based on real-time measurement of mental effort. In: 48th Hawaii International Conference on System Sciences (HICSS), pp. 533–542 (2015)Google Scholar
  7. 7.
    Shojaeizadeh, M., Djamasbi, S, Trapp, A.C.: Does pupillary data differ during fixations and saccades? Does it carry information about task demand? In: Proceedings of the Thirteenth Annual Workshop on HCI Research in MIS, Fort Worth, Texas, USA, 13 December 2015Google Scholar
  8. 8.
    Cyr, D., et al.: Exploring human images in website design: a multi-method approach. MIS Q. 33(3), 539–566 (2009)MathSciNetGoogle Scholar
  9. 9.
    Tullis, T., Siegel, M.: Does ad blindness on the web vary by age and gender? In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems, Paris, France, pp. 1833–1838. ACM (2013)Google Scholar
  10. 10.
    Eivazi, S., Bednarik, R.: Inferring problem solving strategies using eye-tracking: system description and evaluation. In: Proceedings of the 10th Koli Calling International Conference on Computing Education Research, Koli, Finland. ACM (2010)Google Scholar
  11. 11.
    Bixler, R., D’Mello, S.: Toward fully automated person-independent detection of mind wandering. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 37–48. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Simola, J., et al.: Using hidden Markov model to uncover processing states from eye movements in information search tasks. Cogn. Syst. Res. 9(4), 237–251 (2008)CrossRefGoogle Scholar
  13. 13.
    Kaller, C.P., et al.: Eye movements and visuospatial problem solving: identifying separable phases of complex cognition. Psychophysiology 46(4), 818–830 (2009)CrossRefGoogle Scholar
  14. 14.
    Kahneman, D., Beatty, J.: Pupil diameter and load on memory. Science 154(3756), 1583–1585 (1966)CrossRefGoogle Scholar
  15. 15.
    Rayner, K.: Eye movements in reading and information processing: 20 years of research. Psychol. Bull. 124(3), 372–422 (1998)CrossRefGoogle Scholar
  16. 16.
    Cowen, L., Ball, L.J., Delin, J.: An eye-movement analysis of web-page usability. In: Faulkner, X., Finlay, J., Détienne, F. (eds.) People and Computers XVI—Memorable yet Invisible: Proceedings of HCI 2002, pp. 317–335. Springer, London (2002)CrossRefGoogle Scholar
  17. 17.
    Just, M.A., Carpenter, P.A.: A capacity theory of comprehension: individual differences in working memory. Psychol. Rev. 99(1), 122–149 (1992)CrossRefGoogle Scholar
  18. 18.
    Djamasbi, S., Siegel, M., Tullis, T.: Visual hierarchy and viewing behavior: an eye tracking study. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2011. LNCS, vol. 6761, pp. 331–340. Springer, Heidelberg (2011)Google Scholar
  19. 19.
    Beatty, J.: Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychol. Bull. 91(2), 276–292 (1982)CrossRefGoogle Scholar
  20. 20.
    Palinko, O., Kun, A.: Exploring the effects of visual cognitive load and illumination on pupil diameter in driving simulators. In: Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara, California. ACM (2012)Google Scholar
  21. 21.
    Iqbal, S., Adamczyk, P.D., Zheng, X., Baily, B.P.: Towards an index of opportunity: understanding changes in mental workload during task execution. In: CHI 2005: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Portland, Oregon, USA. ACM (2005)Google Scholar
  22. 22.
    Lanata, A., Armato, A., Valenza, G., Scilingo, E.P.: Eye tracking and pupil size variation as response to affective stimuli: a preliminary study. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 78–84, 23–26 May 2011Google Scholar
  23. 23.
    Bailey, B., Iqbal, S.: Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Trans. Comput.-Hum. Interact. 14(4), 1–28 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

Personalised recommendations