TY - JOUR
T1 - A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading
AU - Wang, Xiaoming
AU - Zhao, Xinbo
AU - Ren, Jinchang
PY - 2019/3/24
Y1 - 2019/3/24
N2 - 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.
AB - 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.
KW - eye movement models
KW - recurrent neural network (RNN)
KW - gaze point prediction sequence
KW - long short-term memory networks (LSTM)
UR - http://www.scopus.com/inward/record.url?scp=85064414256&partnerID=8YFLogxK
U2 - 10.1155/2019/8641074
DO - 10.1155/2019/8641074
M3 - Article
AN - SCOPUS:85064414256
SN - 1076-2787
VL - 2019
JO - Complexity
JF - Complexity
M1 - 8641074
ER -