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Reading comprehension is a complex cognitive process involving many humanbrain activities. Plenty of works have studied the patterns and attentionallocations of reading comprehension in information retrieval relatedscenarios. However, little is known about what happens in human brain duringreading comprehension and how these cognitive activities can affect informationretrieval process. Additionally, with the advances in brain imaging techniquessuch as electroencephalogram (EEG), it is possible to collect brain signals inalmost real time and explore whether it can be utilized as feedback tofacilitate information acquisition performance. In this paper, we carefullydesign a lab-based user study to investigate brain activities during readingcomprehension. Our findings show that neural responses vary with differenttypes of reading contents, i.e., contents that can satisfy users' informationneeds and contents that cannot. We suggest that various cognitive activities,e.g., cognitive loading, semantic-thematic understanding, and inferentialprocessing, underpin these neural responses at the micro-time scale duringreading comprehension. From these findings, we illustrate several insights forinformation retrieval tasks, such as ranking models construction and interfacedesign. Besides, we suggest the possibility of detecting reading comprehensionstatus for a proactive real-world system. To this end, we propose a Unifiedframework for EEG-based Reading Comprehension Modeling (UERCM). To verify itseffectiveness, we conduct extensive experiments based on EEG features for tworeading comprehension tasks: answer sentence classification and answerextraction. Results show that it is feasible to improve the performance of twotasks with brain signals.

Towards a Better Understanding Human Reading Comprehension with Brain Signals