# Towards a Better Understanding Human Reading Comprehension with Brain Signals

arXiv: https://arxiv.org/abs/2108.01360 · HTML: https://mlsanity.com/paper/2108.01360/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering](https://mlsanity.com/paper/1607.06275/) (2016)
- [Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals](https://mlsanity.com/paper/1607.03502/) (2016)
- [Answer Interaction in Non-factoid Question Answering Systems](https://mlsanity.com/paper/1901.03491/) (2019)

## Built on by (most cited first, 8 of 8)

- [Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training](https://mlsanity.com/paper/2311.09198/) (2023)
- [Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias](https://mlsanity.com/paper/2405.20718/) (2024)
- [Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2208.00877/) (2022)
- [GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network](https://mlsanity.com/paper/2309.15515/) (2023)
- [Query Augmentation by Decoding Semantics from Brain Signals](https://mlsanity.com/paper/2402.15708/) (2024)
- [Individual Turing Test: A Case Study of LLM-based Simulation Using Longitudinal Personal Data](https://mlsanity.com/paper/2603.01289/) (2026)
- [How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study](https://mlsanity.com/paper/2605.16953/) (2026)
- [Encoding EEG Signals to Examine Human-Like Next-Word Prediction Behaviour in Language Models](https://mlsanity.com/paper/2607.16549/) (2026)

## Abstract (arXiv)

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.