A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading
Wang, Xiaoming and Zhao, Xinbo and Ren, Jinchang (2019) A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading. Complexity, 2019. 8641074. ISSN 1099-0526 (https://doi.org/10.1155/2019/8641074)
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Abstract
Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.
ORCID iDs
Wang, Xiaoming, Zhao, Xinbo and Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194;-
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Item type: Article ID code: 68096 Dates: DateEvent24 March 2019Published27 February 2019AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 29 May 2019 10:37 Last modified: 09 Oct 2024 00:29 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/68096