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

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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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.78-83, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_9〉
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André Krause, Kai Essig, Li-Ying Essig-Shih, Thomas Schack. Classifying the Differences in Gaze Patterns of Alphabetic and Logographic L1 Readers – A Neural Network Approach. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.78-83, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_9〉. 〈hal-01571323〉

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