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.
Original language | English |
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Article number | 8641074 |
Number of pages | 12 |
Journal | Complexity |
Volume | 2019 |
DOIs | |
Publication status | Published - 24 Mar 2019 |
Funding
This work was supported by the National Natural Science Foundation of China (Grants nos. 61231016 and 61871326) and the General Project of Humanities and Social Sciences Research of Ministry of Education of China (Grant no. 18YJCZH180).
Keywords
- eye movement models
- recurrent neural network (RNN)
- gaze point prediction sequence
- long short-term memory networks (LSTM)