[en] We applied state-space modelling technique to estimate the cognitive workload of a speech-in-noise (SIN) recall task, based on participants’ oculo-motor responses to speech signals. We estimated common latent attention levels in 15 time bins and observed temporal changes between pupillary dilations and saccade frequencies, given that the both conditions were independent. We also compared two speech type factors (natural vs. synthetic) and three levels of signal-to-noise (-1dB, -3dB, and -5dB) using the estimated parameter distribution. The comparison of experimental factors provided us with insights into differences in participants’ processing of spoken information during a SIN recall task.
Disciplines :
Computer science
Author, co-author :
DUBIEL, Mateusz ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Nakayama, Minoru; Tokyo Institute of Technology
Wang, Xin; National Institute of Informatics - Tokyo, Japan
External co-authors :
yes
Language :
English
Title :
Modelling Attention Levels with Ocular Responses in a Speech-in-Noise Recall Task
Publication date :
30 May 2023
Event name :
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications
Event date :
from 30-05-2023 to 02-06-2023
Audience :
International
Main work title :
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications
Publisher :
Association for Computing Machinery, Tubingen, Germany
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