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.
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Krause, A.F., Essig, K., Essig-Shih, LY., Schack, T. (2011). Classifying the Differences in Gaze Patterns of Alphabetic and Logographic L1 Readers – A Neural Network Approach. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_9
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DOI: https://doi.org/10.1007/978-3-642-23957-1_9
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