# Advancing NLP with Cognitive Language Processing Signals

arXiv: https://arxiv.org/abs/1904.02682 · HTML: https://mlsanity.com/paper/1904.02682/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 18 of 18)

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- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Multilingual Language Models Predict Human Reading Behavior](https://mlsanity.com/paper/2104.05433/) (2021)
- [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://mlsanity.com/paper/2302.05721/) (2023)
- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://mlsanity.com/paper/2102.08655/) (2021)
- [Can Brain Signals Reveal Inner Alignment with Human Languages?](https://mlsanity.com/paper/2208.06348/) (2022)
- [Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models](https://mlsanity.com/paper/2410.01532/) (2024)
- [CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals](https://mlsanity.com/paper/2106.05544/) (2021)
- [Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide](https://mlsanity.com/paper/2203.05300/) (2022)
- [WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset](https://mlsanity.com/paper/2303.17876/) (2023)
- [OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses](https://mlsanity.com/paper/2503.10927/) (2025)
- [A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models](https://mlsanity.com/paper/2212.09873/) (2022)
- [Teaching Human Behavior Improves Content Understanding Abilities Of LLMs](https://mlsanity.com/paper/2405.00942/) (2024)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction](https://mlsanity.com/paper/2110.04475/) (2021)
- [Team 'UFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models](https://mlsanity.com/paper/2204.04998/) (2022)
- [Can humans help BERT gain "confidence"?](https://mlsanity.com/paper/2309.06580/) (2023)
- [Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus](https://mlsanity.com/paper/2608.10688/) (2026)

## Abstract (arXiv)

When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP.