• DocumentCode
    2208003
  • Title

    Prediction of recall accuracy in a contextual understanding task using eye movement features

  • Author

    Nakayama, Minoru ; Hayashi, Yuko

  • Author_Institution
    Center for R & D of Educ. Technol., Tokyo Inst. of Technol., Tokyo, Japan
  • fYear
    2011
  • fDate
    11-15 April 2011
  • Firstpage
    98
  • Lastpage
    105
  • Abstract
    Contextual understanding, which consists of memory, reasoning and recall, is a key process of human-computer interactions and interfaces. To determine the possibility of predicting the recall accuracy of reading and memorizing tasks using features of eye movements for a targeted text, a contextual understanding task experiment was conducted. The relationship between eye movement during memorization and recall performance was hypothesized. Viewer´s eye-movements during the reading of definition statements were observed, and twelve sector features of these eye-movements across a two-dimensional space were measured. The prediction procedure was developed using Support Vector Regressions with Gaussian kernel. The correlational relationship between predicted and experimental recall accuracies was significant; therefore the hypothesis was supported. Also, it was found that prediction performance depended on a combination of features of eye movements.
  • Keywords
    Gaussian processes; cognition; eye; human computer interaction; regression analysis; support vector machines; Gaussian kernel; contextual understanding task; eye movement feature; human-computer interaction; human-computer interface; memorization; memorizing task; memory; prediction procedure; reading task; reasoning; recall accuracy; recall performance; support vector regression; targeted text; Accuracy; Analysis of variance; Buildings; Context; Tracking; Web sites;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), 2011 IEEE Symposium on
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4244-9913-7
  • Type

    conf

  • DOI
    10.1109/CIMSIVP.2011.5949240
  • Filename
    5949240