# PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus

arXiv: https://arxiv.org/abs/2403.00506 · HTML: https://mlsanity.com/paper/2403.00506/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 102 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension](https://mlsanity.com/paper/2010.06396/) (2020)
- [Multilingual Language Models Predict Human Reading Behavior](https://mlsanity.com/paper/2104.05433/) (2021)
- [Entity Recognition at First Sight: Improving NER with Eye Movement Information](https://mlsanity.com/paper/1902.10068/) (2019)
- [GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset](https://mlsanity.com/paper/2009.06171/) (2020)
- [Bridging Information-Seeking Human Gaze and Machine Reading Comprehension](https://mlsanity.com/paper/2009.14780/) (2020)
- [Leveraging Cognitive Features for Sentiment Analysis](https://mlsanity.com/paper/1701.05581/) (2017)
- [Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze](https://mlsanity.com/paper/2011.04592/) (2020)
- [Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour](https://mlsanity.com/paper/1810.04839/) (2018)
- [Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour](https://mlsanity.com/paper/2005.12078/) (2020)
- [A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements](https://mlsanity.com/paper/1809.08031/) (2018)
- [Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding](https://mlsanity.com/paper/2310.14676/) (2023)
- [The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts](https://mlsanity.com/paper/2204.13311/) (2022)
- [WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset](https://mlsanity.com/paper/2303.17876/) (2023)
- [pymovements: A Python Package for Eye Movement Data Processing](https://mlsanity.com/paper/2304.09859/) (2023)
- [TURead: An eye movement dataset of Turkish reading](https://mlsanity.com/paper/2312.01114/) (2023)
- [Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models](https://mlsanity.com/paper/2304.13536/) (2023)

## Built on by (most cited first, 5 of 5)

- [On the Proper Treatment of Tokenization in Psycholinguistics](https://mlsanity.com/paper/2410.02691/) (2024)
- [EMTeC: A Corpus of Eye Movements on Machine-Generated Texts](https://mlsanity.com/paper/2408.04289/) (2024)
- [OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses](https://mlsanity.com/paper/2503.10927/) (2025)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)
- [Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading](https://mlsanity.com/paper/2606.01964/) (2026)

## Abstract (arXiv)

The Potsdam Textbook Corpus (PoTeC) is a naturalistic eye-tracking-while-reading corpus containing data from 75 participants reading 12 scientific texts. PoTeC is the first naturalistic eye-tracking-while-reading corpus that contains eye-movements from domain-experts as well as novices in a within-participant manipulation: It is based on a 2x2x2 fully-crossed factorial design which includes the participants' level of study and the participants' discipline of study as between-subject factors and the text domain as a within-subject factor. The participants' reading comprehension was assessed by a series of text comprehension questions and their domain knowledge was tested by text-independent background questions for each of the texts. The materials are annotated for a variety of linguistic features at different levels. We envision PoTeC to be used for a wide range of studies including but not limited to analyses of expert and non-expert reading strategies. The corpus and all the accompanying data at all stages of the preprocessing pipeline and all code used to preprocess the data are made available via GitHub: https://github.com/DiLi-Lab/PoTeC.