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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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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. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.78-83, ⟨10.1007/978-3-642-23957-1_9⟩. ⟨hal-01571323⟩

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