# Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns

arXiv: https://arxiv.org/abs/2203.08085 · HTML: https://mlsanity.com/paper/2203.08085/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 15 Mar 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Human Sentence Processing: Recurrence or Attention?](https://mlsanity.com/paper/2005.09471/) (2020)
- [Multilingual Language Models Predict Human Reading Behavior](https://mlsanity.com/paper/2104.05433/) (2021)

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

- [Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models](https://mlsanity.com/paper/2210.09819/) (2022)
- [MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders](https://mlsanity.com/paper/2212.09855/) (2022)
- [(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification](https://mlsanity.com/paper/2212.09848/) (2022)
- [PsyAttention: Psychological Attention Model for Personality Detection](https://mlsanity.com/paper/2312.00293/) (2023)
- [Cross-Lingual Transfer of Cognitive Processing Complexity](https://mlsanity.com/paper/2302.12695/) (2023)
- [Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features](https://mlsanity.com/paper/2212.09465/) (2022)
- [Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation](https://mlsanity.com/paper/2403.15512/) (2024)
- [Humanity in AI: Detecting the Personality of Large Language Models](https://mlsanity.com/paper/2410.08545/) (2024)

## Abstract (arXiv)

There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading. While promising results have been obtained through the use of transformer-based language models, little work has been undertaken to relate the performance of such models to general text characteristics. In this paper we report on experiments with two eye-tracking corpora of naturalistic reading and two language models (BERT and GPT-2). In all experiments, we test effects of a broad spectrum of features for predicting human reading behavior that fall into five categories (syntactic complexity, lexical richness, register-based multiword combinations, readability and psycholinguistic word properties). Our experiments show that both the features included and the architecture of the transformer-based language models play a role in predicting multiple eye-tracking measures during naturalistic reading. We also report the results of experiments aimed at determining the relative importance of features from different groups using SP-LIME.