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Classifying the differences in gaze patterns of alphabetic and logographic L1 readers - A neural network approach

IFIP Advances in Information and Communication Technology (2011) 363 AICT(PART 1) 78-83
DOI: 10.1007/978-3-642-23957-1_9
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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. © 2011 International Federation for Information Processing.

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Krause, A. F., Essig, K., Essig-Shih, L. Y., & Schack, T. (2011). Classifying the differences in gaze patterns of alphabetic and logographic L1 readers - A neural network approach. In IFIP Advances in Information and Communication Technology (Vol. 363 AICT, pp. 78–83). Springer New York LLC. https://doi.org/10.1007/978-3-642-23957-1_9

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