Towards a Better Understanding Human Reading Comprehension with Brain Signals

TL;DR

This paper investigates brain activities during reading comprehension using EEG, revealing neural response variations with content type and proposing a framework to enhance information retrieval tasks through brain signal analysis.

Contribution

It introduces a novel EEG-based framework for modeling reading comprehension and demonstrates its effectiveness in improving related information retrieval tasks.

Findings

Neural responses differ with content satisfaction levels.

Brain signals can improve answer classification and extraction tasks.

Proposed framework enhances reading comprehension modeling.

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.