Lena A. Jäger

EDBT 2026 Demo / reviewers in the wild / expert

dblp:198/0994 · also Lena Ann Jäger · DBLP profile ↗

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38ranked-venue papers

1as first author

33since 2021 · last 2026

0000-0001-9018-9713ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 20 · 19 since 2021Artificial intelligence and machine learning · 19 · 1 first-author · 14 since 2021Graphics, computer vision, multimedia, augmented reality and games · 19 · 18 since 2021Databases, data management, data science and information retrieval · 3 · 1 first-author · 1 since 2021Security and privacy · 2 · 1 since 2021

Year	Publication	Venue	Position
2026	Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection abstract Persistent homology, a method from topological data analysis, extracts robust, multi-scale features from data. It produces stable representations of time series by applying varying thresholds to their values (a process known as a filtration). We develop novel filtrations for time series and introduce topological methods for the analysis of eye-tracking data, by interpreting fixation sequences as time series, and constructing “hybrid models” that combine topological features with traditional statistical features. We empirically evaluate our method by applying it to the task of dyslexia detection from eye-tracking-while-reading data using the Copenhagen Corpus, which contains scanpaths from dyslexic and non-dyslexic L1 and L2 readers. Our hybrid models outperform existing approaches that rely solely on traditional features, showing that persistent homology captures complementary information encoded in fixation sequences. The strength of these topological features is further underscored by their achieving performance comparable to established baseline methods. Importantly, our proposed filtrations outperform existing ones. Marius Huber, David R. Reich, Lena A. Jäger	ETRA	3
2026	Preprocessing Eye-Tracking Data for Reading: A Community Survey on Practices, Challenges, and Needs abstract Preprocessing eye-tracking data is a critical yet challenging step in eye-tracking research, particularly in reading studies. Current practices are characterized by limited standardization, a strong reliance on proprietary or inflexible tools, and heterogeneous documentation routines, which hinder reproducibility, comparability, and data reuse. To systematically assess community practices and needs, we conducted a large-scale survey (N = 108) with a focus on reading research. The survey examined preprocessing workflows, tool usage, perceived challenges, data sharing and documentation strategies, and expectations regarding preprocessing tools and outputs. The findings indicate a strong demand for transparent, interoperable, and well-documented preprocessing workflows, as well as practical guidance such as tutorials and user support. Participants emphasized the importance of standardized terminology, flexible automation, and reusable output structures. These results provide an empirical basis for tool development, methodological guidelines, and infrastructure initiatives aimed at improving transparency, standardization, and long-term reusability in eye-tracking research. Marie-Luise Müller, Carlson Moses Büth, Deborah N. Jakobi, Daniel Krakowczyk, Eva Pavlinusic Vilus, Anastassia Shaitarova, Lena A. Jäger	ETRA	7
2026	The MultiplEYE Text Corpus: Towards a Diverse and Ever-Expanding Multilingual Text Corpus Ramune Kaspere, Anna Bondar, Sergiu Nisioi, Maja Stegenwallner-Schütz, Hanne B. Søndergaard Knudsen, Ana Matic Skoric, Eva Pavlinusic Vilus, Dorota Klimek-Jankowska, Chiara Tschirner, Not Battesta Soliva, Deborah N. Jakobi, Cui Ding, Dima Abu Romi, Cengiz Acartürk, Matilda Agdler, Anton Marius Alexandru, Mohd Faizan Ansari, Annalisa Arcidiacono, Elizabete Ausma Velta Barisa, Ana Bautista, Lisa Beinborn, Yevgeni Berzak, Nedeljka Bjelanovic, Anna Isabelle Bothmann, Jan Brasser, Caterina Cacioli, Anila Çepani, Ilze Ceple, Adelina Çerpja, Dalí Chirino, Jan Chromý, Alessandro Corona Mendozza, Iria de-Dios-Flores, Nazik Dinçtopal Deniz, Ana Dosen, Kristian Elersic, Inmaculada Fajardo, Zigmunds Freibergs, Angelina Ganebnaya, Jessica Gomes, Annjo Klungervik Greenall, Alba Haveriku, Anamaria Hodivoianu, Yu-Yin Hsu, Amanda Isaksen, Andreia Janeiro, Kristine M. Jensen de López, Aleksandar Jevremovic, Vojislav Jovanovic, Hanna Kedzierska, Nik Kharlamov, Sara Kosutar, Nelda Kote, Vanja Kovic, Izabela Krejtz, Thyra Krosness, Oleksandra Kuvshynova, Eilam Lavy, Ella Lion, Marta Lockiewicz, Kaidi Lõo, Paula Luegi, Mircea Mihai Marin, Clara Martin, Svitlana Matvieieva, Diane C. Mézière, Xavier Mínguez-López, Valeriia Modina, Jurgita Motiejuniene, Marie-Luise Müller, Tolgonai Nasipbek kyzy, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Patrizia Paggio, Marijan Palmovic, Maria Christina Panagiotopoulou, Alberto Parola, Helena Pérez, Klaudia Petersen, Anja Podlesek, Eva Pospísilová, Marta Praulina, Mikulás Preininger, Loredana Punga, Diego Rossini, Spela Rot, Habib Sani Yahaya, Irina A. Sekerina, Anne Gabija Skadina, Jordi Solé i Casals, Lonneke van der Plas, Saara M. Varjopuro, Spyridoula Varlokosta, João Veríssimo, Oskari Juhapekka Virtanen, Nemanja Vracar, Mila Dimitrova-Vulchanova, Ahmad Mustapha Wali, Peizheng Wu, Nilgün Yücel, Stefan Frank, Nora Hollenstein, Lena A. Jäger, Somayeh Bakhtiari	LREC	106
2025	Leveraging In-Context Learning for Political Bias Testing of LLMs abstract A growing body of work has been querying LLMs with political questions to evaluate their potential biases.However, this probing method has limited stability, making comparisons between models unreliable.In this paper, we argue that LLMs need more context.We propose a new probing task, Questionnaire Modeling (QM), that uses human survey data as incontext examples.We show that QM improves the stability of question-based bias evaluation, and demonstrate that it may be used to compare instruction-tuned models to their base versions.Experiments with LLMs of various sizes indicate that instruction tuning can indeed change the direction of bias.Furthermore, we observe a trend that larger models are able to leverage in-context examples more effectively, and generally exhibit smaller bias scores in QM.Data and code are publicly available.1 Patrick Haller 0001, Jannis Vamvas, Rico Sennrich, Lena A. Jäger	ACL (1)	4
2025	Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading Behavior abstract The type of a text profoundly shapes reading behavior, yet little is known about how different text types interact with word-level features and the properties of machine-generated texts and how these interactions influence how readers process language.In this study, we investigate how different text types affect eye movements during reading, how neural decoding strategies used to generate texts interact with text type, and how text types modulate the influence of word-level psycholinguistic features such as surprisal, word length, and lexical frequency.Leveraging EMTeC (Bolliger et al., 2025), the first eye-tracking corpus of LLM-generated texts across six text types and multiple decoding algorithms, we show that text type strongly modulates cognitive effort during reading, that psycholinguistic effects induced by word-level features vary systematically across genres, and that decoding strategies interact with text types to shape reading behavior.These findings offer insights into genre-specific cognitive processing and have implications for the human-centric design of AI-generated texts.Our code is publicly available at https://github.com/DiLi- Lab/Genre-Matters. Lena S. Bolliger, Lena A. Jäger	EMNLP	2
2025	Modeling Bottom-up Information Quality during Language Processing abstract Contemporary theories model language processing as integrating both top-down expectations and bottom-up inputs.One major prediction of such models is that the quality of the bottom-up inputs modulates ease of processing-noisy inputs should lead to difficult and effortful comprehension.We test this prediction in the domain of reading.First, we propose an information-theoretic operationalization for the "quality" of bottom-up information as the mutual information (MI) between visual information and word identity.We formalize this prediction in a mathematical model of reading as a Bayesian update.Second, we test our operationalization by comparing participants' reading times in conditions where words' information quality has been reduced, either by occluding their top or bottom half, with full words.We collect data in English and Chinese.We then use multimodal language models to estimate the mutual information between visual inputs and words.We use these data to estimate the specific effect of reduced information quality on reading times.Finally, we compare how information is distributed across visual forms.In English and Chinese, the upper half contains more information about word identity than the lower half.However, the asymmetry is more pronounced in English, a pattern which is reflected in the reading times. Cui Ding, Yanning Yin, Lena A. Jäger, Ethan Wilcox	EMNLP	3
2025	CoLAGaze: A Corpus of Eye Movements for Linguistic Acceptability abstract We present CoLAGaze, the first broad-coverage eye-tracking-while-reading corpus on grammatical and ungrammatical sentences sourced from CoLA — a Natural Language Processing (NLP) benchmark for evaluating the grammatical knowledge of language models (LMs). CoLAGaze provides eye-tracking data from native English speakers in different formats including the raw eye-tracking signal, gaze event data, and reading measures computed at the character, word, and sentence levels alongside comprehensive meta-data and data quality documentation. CoLAGaze enables psycholinguistic research on the processing of diverse (un)grammatical structures, allows the training of generative models of eye-movements-in-reading capable of generalizing to ungrammatical stimuli, facilitates the alignment of LMs to human language processing, and supports gaze-augmented NLP applications for grammatical error detection. CoLAGaze and the preprocessing code, is available at OSF and GitHub. We have also integrated it into the pymovements Python package. Anna Bondar, David R. Reich, Lena A. Jäger	ETRA	3
2025	Predicting Children's Reading Comprehension Through Eye Movements: Insights from Visual Search and Interpretable Machine Learning abstract Early identification of children at risk of reading difficulties is paramount for promoting educational success and equity, as earlier interventions are more effective. Traditional assessment methods of early reading abilities, however, are resource-intensive, and often require basic reading abilities. Predicting reading acquisition from non-reading tasks, particularly during the early stages of formal reading instruction, overcomes these limitations. In this paper, we investigate to what extent eye movements recorded during a visual search task that is hypothesized to correlate with reading ability allows to predict children’s reading comprehension scores at the time of recording as well as one year later. Using machine learning methods that allow for an evaluation of feature importance, namely Neural Additive Models and Random Forests, we explore what eye movement features obtained from a visual search task are predictive of reading comprehension, thus laying the groundwork for future research in early assessment systems based on eye movements. Jan Brasser, Chiara Tschirner, Maja Stegenwallner-Schütz, Deborah N. Jakobi, Lena A. Jäger	ETRA	5
2025	Neural Additive Models Uncover Predictive Gaze Features in Reading Deborah N. Jakobi, David R. Reich, Paul Prasse, Lena A. Jäger	ETRA	4
2025	MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus abstract Contains fulltext : 326363.pdf (Publisher’s version ) (Open Access) Deborah N. Jakobi, Maja Stegenwallner-Schütz, Nora Hollenstein, Cui Ding, Ramune Kaspere, Ana Matic Skoric, Eva Pavlinusic Vilus, Stefan Frank, Marie-Luise Müller, Kristine M. Jensen de López, Nik Kharlamov, Hanne B. Søndergaard Knudsen, Yevgeni Berzak, Ella Lion, Irina A. Sekerina, Cengiz Acartürk, Mohd Faizan Ansari, Katarzyna Harezlak, Pawel Kasprowski, Ana Bautista, Lisa Beinborn, Anna Bondar, Antonia Boznou, Leah Bradshaw, Jana Mara Hofmann, Thyra Krosness, Not Battesta Soliva, Anila Çepani, Kristina Cergol, Ana Dosen, Marijan Palmovic, Adelina Çerpja, Dalí Chirino, Jan Chromý, Vera Demberg, Iza Skrjanec, Nazik Dinçtopal Deniz, Inmaculada Fajardo, Mariola Giménez-Salvador, Xavier Mínguez-López, Maros Filip, Zigmunds Freibergs, Jessica Gomes, Andreia Janeiro, Paula Luegi, João Veríssimo, Sasho Gramatikov, Jana Hasenäcker, Alba Haveriku, Nelda Kote, Muhammad Mohsin Kamal, Hanna Kedzierska, Dorota Klimek-Jankowska, Sara Kosutar, Daniel Krakowczyk, Izabela Krejtz, Marta Lockiewicz, Kaidi Lõo, Jurgita Motiejuniene, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Mikulás Preininger, Loredana Punga, David R. Reich, Chiara Tschirner, Spela Rot, Andreas Säuberli, Jordi Solé i Casals, Ekaterina Strati, Igor Svoboda, Evis Trandafili, Spyridoula Varlokosta, Mila Dimitrova-Vulchanova, Lena A. Jäger	ETRA	75
2025	The More the Merrier: Boost Your Dataset Visibility and Discover Eye-Tracking Datasets with pymovements Daniel Krakowczyk, David R. Reich, Andreas Säuberli, Iza Skrjanec, Isabelle Caroline Rose Cretton, Deborah N. Jakobi, Sergiu Nisioi, Paul Prasse, Lena A. Jäger	ETRA	9
2025	Detection of Alcohol Inebriation from Eye Movements using Remote and Wearable Eye Trackers abstract This OSF contains the data for the paper 'Detection of Alcohol Inebriation from Eye Movements using Remote and Wearable Eye Trackers'. The raw data can be found in the folder raw_data (each zip file contains recorded data up- /downsampled to 1,000 Hz as csv-files). - Each csv file contains the recording (remote and wearable) for one subject for one PVT trial. - Each csv file contains the following columns: trial_id: trial-id for current recording block_id: block-id for current recording x_pix_eyelink: x-pixel coordinates using eyelink remote eye-tracker y_pix_eyelink: y-pixel coordinates using eyelink remote eye-tracker eyelink_timestamp: timestamp or recording in ms x_pix_pupilcore_interpolated: x-pixel coordinates using pupil-core eye-tracker upsampled to 1,000 Hz y_pix_pupilcore_interpolated: y-pixel coordinates using pupil-core eye-tracker upsampled to 1,000 Hz pupil_size_eyelink: pupil-size of pupil using eyelink remote eye-tracker target_distance: distance to eyelink remote eye-tracker (screen) in mm pupil_size_pupilcore_interpolated: pupil-size of pupil pupil-core eye-tracker upsampled to 1,000 Hz pupil_confidence_interpolated: pupil detection confidence of pupil pupil-core eye-tracker upsampled to 1,000 Hz time_to_prev_bac: elapsed time from previous BAC testing in ms time_to_next_bac: remaining time for next BAC testing in ms prev_bac: previous BAC concentration next_bac: next BAC concentration For more details see: https://github.com/aeye-lab/etra-potsdam-binge-pvt Paul Prasse, David R. Reich, Jakob Chwastek, Silvia Makowski, Lena A. Jäger, Tobias Scheffer	ETRA	5
2025	Proxy-Based Pre-Training for Eye-Tracking Applications David R. Reich, Cui Ding, Lena S. Bolliger, Patrick Haller 0001, Paul Prasse, Lena A. Jäger	ETRA	6
2025	EyeBench: Predictive Modeling from Eye Movements in Reading abstract We present EyeBench, the first benchmark designed to evaluate machine learning models that decode cognitive and linguistic information from eye movements during reading. EyeBench offers an accessible entry point to the challenging and underexplored domain of modeling eye tracking data paired with text, aiming to foster innovation at the intersection of multimodal AI and cognitive science. The benchmark provides a standardized evaluation framework for predictive models, covering a diverse set of datasets and tasks, ranging from assessment of reading comprehension to detection of developmental dyslexia. Progress on the EyeBench challenge will pave the way for both practical real-world applications, such as adaptive user interfaces and personalized education, and scientific advances in understanding human language processing. The benchmark is released as an open-source software package which includes data downloading and harmonization scripts, baselines and state-of-the-art models, as well as evaluation code, publicly available at https://github.com/EyeBench/eyebench. Omer Shubi, David R. Reich, Keren Gruteke Klein, Yuval Angel, Paul Prasse, Lena A. Jäger, Yevgeni Berzak	NeurIPS	6
2025	ScanDL 2.0: A Generative Model of Eye Movements in Reading Synthesizing Scanpaths and Fixation Durations abstract Eye movements in reading have become a vital tool for investigating the cognitive mechanisms involved in language processing. They are not only used within psycholinguistics but have also been leveraged within the field of NLP to improve the performance of language models on downstream tasks. However, the scarcity of real eye-tracking data and its limited generalizability at inference time present challenges for data-driven approaches. In response, synthetic scanpaths have emerged as a promising alternative. Despite advances, however, existing machine learning-based methods, including the state-of-the-art ScanDL [9], fail to incorporate fixation durations into the generated scanpaths, which are crucial for a complete representation of reading behavior. We therefore propose a novel model, denoted ScanDL 2.0, which synthesizes both fixation locations and durations. It sets a new benchmark in generating human-like synthetic scanpaths, demonstrating superior performance across various evaluation settings. Furthermore, psycholinguistic analyses confirm its ability to emulate key phenomena in human reading. Our code as well as pre-trained model weights are available via https://github.com/DiLi-Lab/ScanDL-2.0. Lena S. Bolliger, David R. Reich, Lena A. Jäger	Proc. ACM Hum. Comput. Interact.	3
2025	Evaluating Gaze Event Detection Algorithms: Impacts on Machine Learning-based Classification and Psycholinguistic Statistical Modeling abstract Eye movements offer valuable, non-invasive insights into cognitive processes and are widely used in both psycholinguistic research and machine-learning applications, such as assessing reading comprehension and cognitive load. These applications typically rely on fixations and saccades detected through gaze event algorithms, which may be either proprietary or open-source. The impact of different gaze event detection algorithms on subsequent analysis is underexplored and often overlooked. This study investigates how two threshold-based algorithms, I-DT and I-VT, influence both machine-learning classification tasks and psycholinguistic statistical modeling. Using diverse datasets-including stationary, remote, and VR eye-tracking data across multiple sampling frequencies-our findings show significant differences in downstream performance. For ML tasks, I-DT generally outperforms I-VT, with I-VT being highly sensitive to threshold choices. In psycholinguistic analysis, results confirm established findings only when thresholds align with established fixation metrics, emphasizing the importance of appropriate threshold selection for meaningful analysis. Our code is publicly available: https://github.com/aeye-lab/eye-movement-preprocessing. David R. Reich, Paul Prasse, Lena A. Jäger	Proc. ACM Hum. Comput. Interact.	3
2024	Reading Does Not Equal Reading: Comparing, Simulating and Exploiting Reading Behavior across Populations abstract Eye-tracking-while-reading corpora play a crucial role in the study of human language processing, and, more recently, have been leveraged for cognitively enhancing neural language models. A critical limitation of existing corpora is that they often lack diversity, comprising primarily native speakers. In this study, we expand the eye-tracking-while-reading dataset CopCo, which initially included only Danish L1 readers with and without dyslexia, by incorporating a new dataset of L2 readers with diverse L1 backgrounds. Thus, the extended CopCo corpus constitutes the first eye-tracking-while-reading dataset encompassing neurotypical L1 and L1 readers with dyslexia as well as L2 readers, all reading the same materials. We first provide extensive descriptive statistics of the extended CopCo corpus. Second, we investigate how different degrees of diversity of the training data affect a state-of-the-art generative model of eye movements in reading. Finally, we use this scanpath generation model for gaze-augmented language modeling and investigate the impact of diversity in the training data on the model’s performance on a range of NLP downstream tasks. The code can be found here: https://github.com/norahollenstein/copco-processing. David R. Reich, Shuwen Deng, Marina Björnsdóttir, Lena A. Jäger, Nora Hollenstein	LREC/COLING	4
2024	Reporting Eye-Tracking Data Quality: Towards a New Standard abstract Eye-tracking datasets are often shared in the format used by their creators for their original analyses, usually resulting in the exclusion of data considered irrelevant to the primary purpose. In order to increase re-usability of existing eye-tracking datasets for more diverse and initially not considered use cases, this work advocates a new approach of sharing eye-tracking data. Instead of publishing filtered and pre-processed datasets, the eye-tracking data at all pre-processing stages should be published together with data quality reports. In order to transparently report data quality and enable cross-dataset comparisons, we develop data quality reporting standards and metrics that can be automatically applied to a dataset, and integrate them into the open-source Python package pymovements (https://github.com/aeye-lab/pymovements). Deborah N. Jakobi, Daniel Krakowczyk, Lena A. Jäger	ETRA	3
2024	Improving cognitive-state analysis from eye gaze with synthetic eye-movement data abstract Eye movements can be used to analyze a viewer’s cognitive capacities or mental state. Neural networks that process the raw eye-tracking signal can outperform methods that operate on scan paths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We therefore develop SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human ocular micro- and macro-movements. We explore the use of these synthetic eye movements for pre-training neural networks using contrastive learning. We find that pre-training on synthetic data does not help for biometric identification, while results are inconclusive for the detection of ADHD and gender classification. However, for the eye movement-based assessment of higher-level cognitive skills such general reading comprehension, text comprehension, and the distinction of native from non-native readers, pre-training on synthetic eye-gaze data improves the models’ performance and even advances the state-of-the-art for reading comprehension. The SP-EyeGAN model, pre-trained on GazeBase, along with the code for developing your own raw eye-tracking machine learning model with contrastive learning, is available at https://github.com/aeye-lab/sp-eyegan. Paul Prasse, David R. Reich, Silvia Makowski, Tobias Scheffer, Lena A. Jäger	Comput. Graph.	5
2023	ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts abstract Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing.More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language models, as well as the inference of reader-and text-specific properties.However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research.Initially, this problem was tackled by resorting to cognitive models for synthesizing eye movement data.However, for the sole purpose of generating humanlike scanpaths, purely data-driven machinelearning-based methods have proven to be more suitable.Following recent advances in adapting diffusion processes to discrete data, we propose SCANDL, a novel discrete sequence-tosequence diffusion model that generates synthetic scanpaths on texts.By leveraging pretrained word representations and jointly embedding both the stimulus text and the fixation sequence, our model captures multi-modal interactions between the two inputs.We evaluate SCANDL within-and across-dataset and demonstrate that it significantly outperforms state-of-the-art scanpath generation methods.Finally, we provide an extensive psycholinguistic analysis that underlines the model's ability to exhibit human-like reading behavior.Our implementation is made available at https://github.com/DiLi-Lab/ScanDL. Lena S. Bolliger, David R. Reich, Patrick Haller 0001, Deborah N. Jakobi, Paul Prasse, Lena A. Jäger	EMNLP	6
2023	Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding abstract Human gaze data offer cognitive information that reflects natural language comprehension.Indeed, augmenting language models with human scanpaths has proven beneficial for a range of NLP tasks, including language understanding.However, the applicability of this approach is hampered because the abundance of text corpora is contrasted by a scarcity of gaze data.Although models for the generation of humanlike scanpaths during reading have been developed, the potential of synthetic gaze data across NLP tasks remains largely unexplored.We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data.Since the model's error gradient can be propagated throughout all parts of the model, the scanpath generator can be fine-tuned to downstream tasks.We find that the proposed model not only outperforms the underlying language model, but achieves a performance that is comparable to a language model augmented with real human gaze data.Our code is publicly available.1 Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer, Lena A. Jäger	EMNLP	5
2023	Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models abstract Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. These methods provide saliency maps to highlight important input features of a specific eye gaze sequence. However, to date, its localization analysis has been lacking a quantitative approach across entire datasets. In this work, we employ established gaze event detection algorithms for fixations and saccades and quantitatively evaluate the impact of these events by determining their concept influence. Input features that belong to saccades are shown to be substantially more important than features that belong to fixations. By dissecting saccade events into sub-events, we are able to show that gaze samples that are close to the saccadic peak velocity are most influential. We further investigate the effect of event properties like saccadic amplitude or fixational dispersion on the resulting concept influence. Daniel Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger	ETRA	6
2023	pymovements: A Python Package for Eye Movement Data Processing abstract We introduce pymovements: a Python package for analyzing eye-tracking data that follows best practices in software development, including rigorous testing and adherence to coding standards. The package provides functionality for key processes along the entire preprocessing pipeline. This includes parsing of eye tracker data files, transforming positional data into velocity data, detecting gaze events like saccades and fixations, computing event properties like saccade amplitude and fixational dispersion and visualizing data and results with several types of plotting methods. Moreover, pymovements also provides an easily accessible interface for downloading and processing publicly available datasets. Additionally, we emphasize how rigorous testing in scientific software packages is critical to the reproducibility and transparency of research, enabling other researchers to verify and build upon previous findings. Daniel Krakowczyk, David R. Reich, Jakob Chwastek, Deborah N. Jakobi, Paul Prasse, Assunta Süss, Oleksii Turuta, Pawel Kasprowski, Lena A. Jäger	ETRA	9
2023	SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks abstract Neural networks that process the raw eye-tracking signal can outperform traditional methods that operate on scanpaths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We, therefore, present SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human micro- and macro-movements. We demonstrate how the generated synthetic data can be used to pre-train a model using contrastive learning. This model is fine-tuned on labeled human data for the task of interest. We show that for the task of predicting reading comprehension from eye movements, this approach outperforms the previous state-of-the-art. Paul Prasse, David R. Reich, Silvia Makowski, Seoyoung Ahn, Tobias Scheffer, Lena A. Jäger	ETRA	6
2023	Detection of Alcohol Inebriation from Eye Movements abstract In this repository we provide the Binge / PVT data set, extracted gaze features for the data set and the code to replicate the results presented in the paper "Detection of Alcohol Inebriation from Eye Movements ". The raw data set contains binocular gaze recordings and eye closure signals of 44 subjects, aged 18 to 47 with mean age 24. Each participant is recorded over 3 experimental sessions, with a time lag of at least one week between two sessions. [Entire raw data set will be added upon acceptance.] Silvia Makowski, Annika Bätz, Paul Prasse, Lena A. Jäger, Tobias Scheffer	KES	4
2023	Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading abstract Eye movements during reading offer insights into both the reader's cognitive processes and the characteristics of the text that is being read. Hence, the analysis of scanpaths in reading have attracted increasing attention across fields, ranging from cognitive science over linguistics to computer science. In particular, eye-tracking-while-reading data has been argued to bear the potential to make machine-learning-based language models exhibit a more human-like linguistic behavior. However, one of the main challenges in modeling human scanpaths in reading is their dual-sequence nature: the words are ordered following the grammatical rules of the language, whereas the fixations are chronologically ordered. As humans do not strictly read from left-to-right, but rather skip or refixate words and regress to previous words, the alignment of the linguistic and the temporal sequence is non-trivial. In this paper, we develop Eyettention, the first dual-sequence model that simultaneously processes the sequence of words and the chronological sequence of fixations. The alignment of the two sequences is achieved by a cross-sequence attention mechanism. We show that Eyettention outperforms state-of-the-art models in predicting scanpaths. We provide an extensive within- and across-data set evaluation on different languages. An ablation study and qualitative analysis support an in-depth understanding of the model's behavior. Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller 0001, Tobias Scheffer, Lena A. Jäger	Proc. ACM Hum. Comput. Interact.	6
2022	Fairness in Oculomotoric Biometric Identification abstract Gaze patterns are known to be highly individual, and therefore eye movements can serve as a biometric characteristic. We explore aspects of the fairness of biometric identification based on gaze patterns. We find that while oculomotoric identification does not favor any particular gender and does not significantly favor by age range, it is unfair with respect to ethnicity. Moreover, fairness concerning ethnicity cannot be achieved by balancing the training data for the best-performing model. Paul Prasse, David R. Reich, Silvia Makowski, Lena A. Jäger, Tobias Scheffer	ETRA	4
2022	Inferring Native and Non-Native Human Reading Comprehension and Subjective Text Difficulty from Scanpaths in Reading abstract Eye movements in reading are known to reflect cognitive processes involved in reading comprehension at all linguistic levels, from the sub-lexical to the discourse level. This means that reading comprehension and other properties of the text and/or the reader should be possible to infer from eye movements. Consequently, we develop the first neural sequence architecture for this type of tasks which models scan paths in reading and incorporates lexical, semantic and other linguistic features of the stimulus text. Our proposed model outperforms state-of-the-art models in various tasks. These include inferring reading comprehension or text difficulty, and assessing whether the reader is a native speaker of the text’s language. We further conduct an ablation study to investigate the impact of each component of our proposed neural network on its performance. David R. Reich, Paul Prasse, Chiara Tschirner, Patrick Haller 0001, Frank Goldhammer, Lena A. Jäger	ETRA	6
2022	Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue abstract Patterns of micro- and macro-movements of the eyes are highly individual and can serve as a biometric characteristic. It is also known that both alcohol inebriation and fatigue can reduce saccadic velocity and accuracy. This prompts the question of whether changes of gaze patterns caused by alcohol consumption and fatigue impact the accuracy of oculomotoric biometric identification. We collect an eye tracking data set from 66 participants in sober, fatigued and alcohol-intoxicated states. We find that after enrollment in a rested and sober state, identity verification based on a deep neural embedding of gaze sequences is significantly less accurate when probe sequences are taken in either an inebriated or a fatigued state. Moreover, we find that fatigue and intoxication appear to randomize gaze patterns: when the model is fine-tuned for invariance with respect to inebriation and fatigue, and even when it is trained exclusively on inebriated training person, the model still performs significantly better for sober than for sleep-deprived or intoxicated subjects. Silvia Makowski, Paul Prasse, Lena A. Jäger, Tobias Scheffer	IJCB	3
2022	Fundamental Frequency Variability over Time in Telephone Interactions abstract Speech signals contain substantial fundamental frequency (f0) variability. Even within a single utterance, speakers modify f0 to create different intonational patterns. Previous studies have identified markers of increased f0 variability, such as the introduction of a new topic or greetings, but these are limited in the scope of their analyses. In the present study, we investigate f0 variability over the course of a telephone conversation, with a focus on the initial and medial utterances within the exchange. We examined f0 standard deviation of each utterance in over 2000 telephone conversations from 509 American English speakers from the Switchboard corpus. Findings showed that on average, speakers exhibit more f0 variability in the opening compared to mid-conversation utterances. Further, findings suggest that the inclusion of a greeting word in an initial turn, e.g., "hello” or "hi”, corresponds to an increase in f0 standard deviation. These results suggest that speakers employed more variable f0 in the initial few turns of a telephone conversation. The interpretation of this finding is multifaceted and may be linked to several communicative goals, including the placement of identity markers in conversation or the attraction of attention, or the role of openings as boundary markers. Leah Bradshaw, Eleanor Chodroff, Lena A. Jäger, Volker Dellwo	INTERSPEECH	3
2022	Detection of ADHD Based on Eye Movements During Natural Viewing Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, Lena A. Jäger	ECML/PKDD (6)	10
2021	Revisiting the Uniform Information Density Hypothesis abstract The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal.While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well.Further, it is unclear how uniformity in a linguistic signal-or lack thereof-should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold.Here we investigate these facets of the UID hypothesis using reading time and acceptability data.While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID's predictions.For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability.We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted.The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document-a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel. 1 Clara Meister, Tiago Pimentel, Patrick Haller 0001, Lena A. Jäger, Ryan Cotterell, Roger Levy	EMNLP (1)	4
2021	Multilingual Language Models Predict Human Reading Behavior abstract Nora Hollenstein, Federico Pirovano, Ce Zhang, Lena Jäger, Lisa Beinborn. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021. Nora Hollenstein, Federico Pirovano, Ce Zhang 0001, Lena A. Jäger, Lisa Beinborn	NAACL-HLT	4
2020	Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes abstract We study involuntary micro-movements of both eyes, in addition to saccadic macro-movements, as biometric characteristic. We develop a deep convolutional neural network that processes binocular oculomotoric signals and identifies the viewer. In order to be able to detect presentation attacks, we develop a model in which the movements are a response to a controlled stimulus. The model detects replay attacks by processing both the controlled but randomized stimulus and the ocular response to this stimulus. We acquire eye movement data from 150 participants, with 4 sessions per participant. We observe that the model detects replay attacks reliably; compared to prior work, the model attains substantially lower error rates. Silvia Makowski, Lena A. Jäger, Paul Prasse, Tobias Scheffer	IJCB	2
2020	Discriminative Viewer Identification using Generative Models of Eye Gaze abstract We study the problem of identifying viewers of arbitrary images based on their eye gaze. Psychological research has derived generative stochastic models of eye movements. In order to exploit this background knowledge within a discriminatively trained classification model, we derive Fisher kernels from different generative models of eye gaze. Experimentally, we find that the performance of the classifier strongly depends on the underlying generative model. Using an SVM with Fisher kernel improves the classification performance over the underlying generative model. Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, Tobias Scheffer	KES	2
2020	On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification abstract Distributional properties of fixations and saccades are known to constitute biometric characteristics. Additionally, high-frequency micro-movements of the eyes have recently been found to constitute biometric characteristics that allow for faster and more robust biometric identification than just macro-movements. Micro-movements of the eyes occur on scales that are very close to the precision of currently available eye trackers. This study therefore characterizes the relationship between the temporal and spatial resolution of eye tracking recordings on one hand and the performance of a biometric identification method that processes micro-and macro-movements via a deep convolutional network. We find that that the deteriorating effects of decreasing both, the temporal and spatial resolution are not cumulative. We observe that on low-resolution data, the network reaches performance levels above chance and outperforms statistical approaches. Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil, Tobias Scheffer	KES	2
2019	Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer	ECML/PKDD (2)	1
2018	A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements Silvia Makowski, Lena A. Jäger, Ahmed AbdelWahab, Niels Landwehr, Tobias Scheffer	ECML/PKDD (1)	2