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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.
Cited literature [15 references]
https://hal.inria.fr/hal-01571323
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Submitted on : Wednesday, August 2, 2017 - 11:41:16 AM
Last modification on : Thursday, March 5, 2020 - 5:42:36 PM
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Wednesday, August 2, 2017 - 11:41:16 AM
Last modification on : Thursday, March 5, 2020 - 5:42:36 PM
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