PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 102 references that are in the map. First posted on arXiv 1 Mar 2024.
Builds on
22 of 102 references.
References · 22
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2018Constituency Parsing with a Self-Attentive Encodercited by 196
- 2018Multilingual Constituency Parsing with Self-Attention and Pre-Trainingcited by 117
- 2020On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behaviorcited by 115
- 2019ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotationcited by 65
- 2020Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehensioncited by 29
- 2021Multilingual Language Models Predict Human Reading Behaviorcited by 29
- 2019Entity Recognition at First Sight: Improving NER with Eye Movement Informationcited by 26
- 2020GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Datasetcited by 20
- 2020Bridging Information-Seeking Human Gaze and Machine Reading Comprehensioncited by 20
- 2017Leveraging Cognitive Features for Sentiment Analysiscited by 19
10 more
- 2020Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gazecited by 16
- 2018Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviourcited by 9
- 2020Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviourcited by 9
- 2018A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movementscited by 8
- 2023Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understandingcited by 8
- 2022The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Textscited by 7
- 2023WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Datasetcited by 6
- 2023pymovements: A Python Package for Eye Movement Data Processingcited by 5
- 2023TURead: An eye movement dataset of Turkish readingcited by 3
- 2023Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Modelscited by 1
Built on by
Citing papers per year: 2024: 2; 2025: 1; 2026: 2.
5 papers.
- 2024On the Proper Treatment of Tokenization in Psycholinguisticscited by 12
- 2024EMTeC: A Corpus of Eye Movements on Machine-Generated Textscited by 5
- 2025OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responsescited by 3
- 2026Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Supportcited by 0
- 2026Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Readingcited by 0
- 2026Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading1 Jun 2026
- 2026Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support23 Feb 2026
- 2025OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses13 Mar 2025
- 2024On the Proper Treatment of Tokenization in Psycholinguistics3 Oct 2024
- 2024EMTeC: A Corpus of Eye Movements on Machine-Generated Texts8 Aug 2024