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.

通过使用脑电波技术，研究了阅读理解的神经响应变化，发现各种认知活动在阅读理解中发挥作用，并构建了一个基于脑电波的阅读理解建模统一框架，用于提高阅读理解任务的性能。