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Quiet Eye Affects Action Detection from Gaze More Than Context Length

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User Modeling, Adaptation and Personalization (UMAP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9146))

Abstract

Every purposive interactive action begins with an intention to interact. In the domain of intelligent adaptive systems, behavioral signals linked to the actions are of great importance, and even though humans are good in such predictions, interactive systems are still falling behind. We explored mouse interaction and related eye-movement data from interactive problem solving situations and isolated sequences with high probability of interactive action. To establish whether one can predict the interactive action from gaze, we 1) analyzed gaze data using sliding fixation sequences of increasing length and 2) considered sequences several fixations prior to the action, either containing the last fixation before action (i.e. the quiet eye fixation) or not. Each fixation sequence was characterized by 54 gaze features and evaluated by an SVM-RBF classifier. The results of the systematic evaluation revealed importance of the quiet eye fixation and statistical differences of quiet eye fixation compared to other fixations prior to the action.

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Authors and Affiliations

  1. University of Eastern Finland, PO 111, 80101, Joensuu, Finland

    Hana Vrzakova & Roman Bednarik

Authors
  1. Hana Vrzakova
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  2. Roman Bednarik
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Corresponding author

Correspondence to Hana Vrzakova .

Editor information

Editors and Affiliations

  1. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. University of Sheffield, Sheffield, United Kingdom

    Kalina Bontcheva

  3. Trinity College Dublin, Dublin, Ireland

    Owen Conlan

  4. Trinity College Dublin, Dublin, Ireland

    Séamus Lawless

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Vrzakova, H., Bednarik, R. (2015). Quiet Eye Affects Action Detection from Gaze More Than Context Length. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-20267-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20266-2

  • Online ISBN: 978-3-319-20267-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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