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Classifying the Differences in Gaze Patterns of Alphabetic and Logographic L1 Readers – A Neural Network Approach

  • André Frank Krause
  • Kai Essig
  • Li-Ying Essig-Shih
  • Thomas Schack
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)

Abstract

Using plain, but large multi-layer perceptrons, temporal eye-tracking gaze patterns of alphabetic and logographic L1 readers were successfully classified. The Eye-tracking data was fed directly into the networks, with no need for pre-processing. Classification rates up to 92% were achieved using MLPs with 4 hidden units. By classifying the gaze patterns of interaction partners, artificial systems are able to act adaptively in a broad variety of application fields.

Keywords

Hide Layer Reading Comprehension Hide Unit Perceptual Span Handwritten Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • André Frank Krause
    • 1
    • 3
  • Kai Essig
    • 1
    • 3
  • Li-Ying Essig-Shih
    • 2
  • Thomas Schack
    • 1
    • 3
  1. 1.Faculty of Sport Science, Dept. Neurocognition & ActionBielefeld UniversityBielefeldGermany
  2. 2.cultureblend IIT GmbHBielefeld UniversityBielefeldGermany
  3. 3.Cognitive Interaction Technology, Center of ExcellenceBielefeld UniversityBielefeldGermany

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