Towards a Better Understanding Human Reading Comprehension with Brain Signals

From MaRDI portal
Publication:6374389

arXiv2108.01360MaRDI QIDQ6374389

Min Zhang, Shaoping Ma, Xiaohui Xie, Xuesong Chen, Yiqun Liu, Zhihong Wang, Ziyi Ye

Publication date: 3 August 2021

Abstract: Reading comprehension is a complex cognitive process involving many human brain activities. Plenty of works have studied the patterns and attention allocations of reading comprehension in information retrieval related scenarios. However, little is known about what happens in human brain during reading comprehension and how these cognitive activities can affect information retrieval process. Additionally, with the advances in brain imaging techniques such as electroencephalogram (EEG), it is possible to collect brain signals in almost real time and explore whether it can be utilized as feedback to facilitate information acquisition performance. In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension. Our findings show that neural responses vary with different types of reading contents, i.e., contents that can satisfy users' information needs and contents that cannot. We suggest that various cognitive activities, e.g., cognitive loading, semantic-thematic understanding, and inferential processing, underpin these neural responses at the micro-time scale during reading comprehension. From these findings, we illustrate several insights for information retrieval tasks, such as ranking models construction and interface design. Besides, we suggest the possibility of detecting reading comprehension status for a proactive real-world system. To this end, we propose a Unified framework for EEG-based Reading Comprehension Modeling (UERCM). To verify its effectiveness, we conduct extensive experiments based on EEG features for two reading comprehension tasks: answer sentence classification and answer extraction. Results show that it is feasible to improve the performance of two tasks with brain signals.




Has companion code repository: https://github.com/yeziyi1998/uercm








This page was built for publication: Towards a Better Understanding Human Reading Comprehension with Brain Signals

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6374389)