Publications

ZORA Publication List

Publications

• 2026

  • Müller, M.-L., Büth, C., Jakobi, D. N., Krakowczyk, D. G., Pavlinušic Vilus, E., Shaitarova, A., & Jäger, L. A. (2026). Preprocessing Eye-Tracking Data for Reading: A Community Survey on Practices, Challenges, and Needs (A. Chetouani & A. Vilanueva, Eds.; p. 97). ACM Digital library. https://doi.org/10.1145/3797246.3805480
  • Huber, M., Reich, D. R., & Jäger, L. A. (2026). Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection (A. Chetouani & A. Vilanueva, Eds.; p. 12). ACM Digital library. https://doi.org/10.1145/3797246.3803045
  • Kasperė, R., Bondar, A., Nisioi, S., Stegenwallner-Schütz, M., Knudsen, H. B. S., Matić, A., Pavlinušić Vilus, E., Klimek-Jankowska, D., Tschirner, C., Soliva, N. B., Jakobi, D. N., Ding, C., Romi, D. A., Acarturk, C., Agdler, M., Alexandru, A. M., Ansari, M. F., Arcidiacono, A., Barisa, E. A. V., … Jäger, L. A. (2026). The MultiplEYE Text Corpus: Towards a Diverse and Ever-Expanding Multilingual Text Corpus 6706–6721. https://doi.org/10.63317/42gkpf6a6x2x
  • Thouvenin, F., Jäger, L., Donzé, J., Ammann, V., Deutschmann, L., Hänni, L., Jobareth, M., & Marchewka, A. (2026). Training von KI-Modellen und Urheberrecht: Technische Grundlagen, Stand der Diskussion und Lösungsansätze sic! : Zeitschrift für Immaterialgüter-, Informations- und Wettbewerbsrecht, 30, 105–143. https://www.legalis.ch/de/sic/sic-artikel/?training-von-ki-modellen-und-urheberrecht-technische-grundlagen-stand-der-diskussion-und-loesungsansaetze&id=22631

• 2025

  • Haller, P., Ding, C., Stegenwallner-Schütz, M., Reich, D. R., Koncic, I., Makowski, S., & Jäger, L. A. (2025). Replicate Me if You Can: Assessing Measurement Reliability of Individual Differences in Reading Across Measurement Occasions and Methods Cognitive Science, 50, e70121. https://doi.org/10.1111/cogs.70121
  • Shubi, O., Reich, D. R., Grutke Klein, K., Angel, Y., Prasse, P., Jäger, L., & Berzak, Y. (2025). EyeBench: Predictive Modeling from Eye Movements in Reading (No. 39). online.
  • Ding, C., Yin, Y., Jäger, L., & Wilcox, E. (2025). Modeling Bottom-up Information Quality during Language Processing (C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng, Eds.; pp. 11709–11721). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.592
  • Bolliger, L. S., & Jäger, L. (2025). Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading Behavior (C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng, Eds.; pp. 7470–7487). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.379
  • Huber, M., Schnider, P., & Kalisnik Hintz, S. (2025). AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm Transactions on Machine Learning Research, 5280. https://openreview.net/forum?id=Qd7H5mAbzV
  • Haller, P., Vamvas, J., Sennrich, R., & Jäger, L. (2025). Leveraging In-Context Learning for Political Bias Testing of LLMs (W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar, Eds.; No. 63; pp. 24718–24738). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-long.1205
  • Siegelman, N., Schroeder, S., Bao, Y. B., Acartürk, C., Agrawal, N., Bolliger, L. S., Brasser, J., Campos-Rojas, C., Drieghe, D., Filipović Đurđević, D., Goldina, S., Ibáñez Orellana, R., Jäger, L., Jóhannesson, Ó. I., Khare, A., Kharlamov, N., Knudsen, H. B. S., Kristjánsson, Á., Lee, C. E., … et al. (2025). Wave 2 of the Multilingual Eye-Movement Corpus (MECO): New text reading data across languages Scientific Data, 12, 1183. https://doi.org/10.1038/s41597-025-05453-3
  • Wilcox, E., Ding, C., Acampa, G., Pimentel, T., Warstadt, A., & Regev, T. I. (2025). Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent In W. Che, J. Nakatumba-Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics: Vol. 1: Long Papers (pp. 24439–24451). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-long.1192
  • Bolliger, L. S., Haller, P., Cretton, I. C. R., Reich, D. R., Kew, T., & Jäger, L. (2025). EMTeC: A Corpus of Eye Movements on Machine-Generated Texts Behavior Research Methods, 57, 189. https://doi.org/10.3758/s13428-025-02677-4
  • Dziemian, S., Bujia, G., Prasse, P., Barańczuk-Turska, Z., Jäger, L. A., Kamienkowski, J. E., & Langer, N. (2025). Saliency Models Reveal Reduced Top-Down Attention in Attention-Deficit/Hyperactivity Disorder: A Naturalistic Eye-Tracking Study Journal of the American Academy of Child and Adolescent Psychiatry, 3, 192–204. https://doi.org/10.1016/j.jaacop.2024.03.001
  • Jakobi, D. N., Reich, D. R., Prasse, P., & Jäger, L. A. (2025). Neural Additive Models Uncover Predictive Gaze Features in Reading Proceedings of the Symposium on Eye Tracking Research and Applications, 1–10. https://doi.org/10.1145/3715669.3723117
  • Reich, D. R., Ding, C., Bolliger, L. S., Haller, P., Prasse, P., & Jäger, L. A. (2025). Proxy-Based Pre-Training for Eye-Tracking Applications (Y. Sugano, M. Khamis, A. Chetouani, L. Sidenmark, & A. Bruno, Eds.; p. 26). ACM Digital library. https://doi.org/10.1145/3715669.3723113
  • Bondar, A., Reich, D. R., & Jäger, L. A. (2025). CoLAGaze: A Corpus of Eye Movements for Linguistic Acceptability (Y. Sugano, M. Khamis, A. Chetouani, & et al, Eds.; No. 9; p. 9). ACM Digital library. https://doi.org/10.1145/3715669.3723120
  • Krakowczyk, D. G., Reich, D. R., Säuberli, A., Škrjanec, I., Cretton, I. C. R., Jakobi, D. N., Nisioi, S., Prasse, P., & Jäger, L. A. (2025). The More the Merrier: Boost Your Dataset Visibility and Discover Eye-Tracking Datasets with pymovements (Y. Sugano, M. Khamis, A. Chetouani, L. Sidenmark, & A. Bruno, Eds.; p. 63). ACM Digital library. https://doi.org/10.1145/3715669.3726810
  • Jakobi, D. N., Stegenwallner-Schütz, M., Hollenstein, N., Ding, C., Kasperė, R., Matić Škorić, A., Pavlinušić Vilus, E., Frank, S., Müller, M.-L., Jensen De López, K. M., Kharlamov, N., Knudsen, H. B. S., Berzak, Y., Lion, E., Sekerina, I., Acarturk, C., ANSARI, M. F., Harezlak, K., Kasprowski, P., … Jäger, L. (2025). MultiplEYE: Creating a Multilingual Eye-Tracking-While-Reading Corpus (Y. Sugano, M. Khamis, A. Chetouani, L. Sidenmark, & A. Bruno, Eds.; p. 111). ACM Digital library. https://doi.org/10.1145/3715669.3726843
  • Prasse, P., Reich, D. R., Chwastek, J., Makowski, S., Jäger, L. A., & Scheffer, T. (2025). Detection of Alcohol Inebriation from Eye Movements using Remote and Wearable Eye Trackers Proceedings of the Symposium on Eye Tracking Research and Applications, 1–10. https://doi.org/10.1145/3715669.3723109
  • Bolliger, L. S., Reich, D. R., & Jäger, L. A. (2025). ScanDL 2.0: A Generative Model of Eye Movements in Reading Synthesizing Scanpaths and Fixation Durations Proceedings of the ACM on Human-Computer Interaction, 9, ETRA05. https://doi.org/10.1145/3725830
  • Reich, D. R., Prasse, P., & Jäger, L. A. (2025). Evaluating Gaze Event Detection Algorithms: Impacts on Machine Learning-based Classification and Psycholinguistic Statistical Modeling Proceedings of the ACM on Human-Computer Interaction, 9, ETRA10. https://doi.org/10.1145/3725835
  • Kuperman, V., Schroeder, S., Acartürk, C., Agrawal, N., Alexandre, D. M., Bolliger, L. S., Brasser, J., Campos-Rojas, C., Drieghe, D., et al, Reich, D. R., & Jäger, L. (2025). New data on text reading in English as a second language: The Wave 2 expansion of the Multilingual Eye-Movement Corpus (MECO) Studies in Second Language Acquisition, 47, 677–695. https://doi.org/10.1017/S0272263125000105
  • Brasser, J., Tschirner, C., Stegenwallner-Schütz, M., Jakobi, D. N., & Jäger, L. (2025). Predicting Children’s Reading Comprehension Through Eye Movements: Insights from Visual Search and Interpretable Machine Learning (Y. Sugano, M. Khamis, A. Chetouani, L. Sidenmark, & A. Bruno, Eds.; p. 116). ACM Digital library. https://doi.org/10.1145/3715669.3726844
  • Bondar, A., Reich, D. R., & Jäger, L. (2025). AlEYEgnment: Leveraging Eye-Tracking-While-Reading to Align Language Models with Human Preferences (C. Acarturk, J. Nasir, B. Can, & Ç. Çöltekin, Eds.; No. 1; pp. 58–70). Association for Computational Linguistics. https://aclanthology.org/2025.gaze4nlp-1.8/
  • Oğuz, M., Ding, C., Wilcox, E. G., & Fuchs, Z. (2025). Using MoTR to Probe Agreement Processing in Russian Open Mind, 9, 1682–1710. https://doi.org/10.1162/OPMI.a.35
  • Jakobi, D. N., Kern, T., Reich, D. R., Haller, P., & Jäger, L. (2025). PoTeC: a German Naturalistic Eye-Tracking-While-Reading Corpus Behavior Research Methods, 57, 211. https://doi.org/10.3758/s13428-024-02536-8

• 2024

  • Bolliger, L. S., Haller, P., & Jäger, L. A. (2024). On the alignment of LM language generation and human language comprehension 217–231. https://doi.org/10.18653/v1/2024.blackboxnlp-1.14
  • Wilcox, E. G., Ding, C., Sachan, M., & Jäger, L. A. (2024). Mouse Tracking for Reading (MoTR): A new naturalistic incremental processing measurement tool Journal of Memory and Language, 138, 104535. https://doi.org/10.1016/j.jml.2024.104534
  • Haller, P., Vamvas, J., & Jäger, L. A. (2024). Yes, no, maybe? Revisiting language models’ response stability under paraphrasing for the assessment of political leaning. online. https://openreview.net/forum?id=7xUtka9ck9
  • Haller, P., Bolliger, L. S., & Jäger, L. A. (2024). Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences (No. 62). 7878–7892. https://doi.org/10.18653/v1/2024.findings-acl.469
  • Wu, G., Bolliger, L. S., Reich, D. R., & Jäger, L. A. (2024). An Eye Opener Regarding Task-Based Text Gradient Saliency (T. Kuribayashi, G. Rambelli, E. Takmaz, & et al, Eds.; pp. 255–263). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.cmcl-1.22
  • Haller, P., Bolliger, L. S., & Jäger, L. A. (2024). On language models’ cognitive biases in reading time prediction online. https://openreview.net/forum?id=io5QAglkER
  • Kiegeland, S., Reich, D., Cotterell, R., Jäger, L., & Wilcox, E. (2024). The Pupil Becomes the Master: Eye-Tracking Feedback for Tuning LLMs online. https://openreview.net/pdf?id=8oLUcBgKua
  • Alakmeh, T., Reich, D., Jäger, L., & Fritz, T. (2024). Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks (Vol. 1, No. FSE, Article 88). 1982–2004. https://doi.org/10.1145/3660795
  • Jakobi, D. N., Krakowczyk, D. G., & Jäger, L. A. (2024). Reporting Eye-Tracking Data Quality: Towards a New Standard 1–3. https://doi.org/10.1145/3649902.3655658
  • Reich, D. R., Deng, S., Björnsdóttir, M., Jäger, L. A., & Hollenstein, N. (2024). Reading Does Not Equal Reading: Comparing, Simulating and Exploiting Reading Behavior Across Populations 13586–13594. https://aclanthology.org/2024.lrec-main.1187/
  • Säuberli, A., Holzknecht, F., Haller, P., Deilen, S., Schiffl, L., Hansen-Schirra, S., & Ebling, S. (2024). Digital Comprehensibility Assessment of Simplified Texts among Persons with Intellectual Disabilities (CHI ’24). 527. https://doi.org/10.1145/3613904.3642570
  • Prasse, P., Reich, D. R., Makowski, S., Scheffer, T., & Jäger, L. A. (2024). Improving cognitive-state analysis from eye gaze with synthetic eye-movement data Computers & Graphics, 119, 103901. https://doi.org/10.1016/j.cag.2024.103901
  • Müller, M.-L., Kasperė, R., Jakobi, D. N., Bradshaw, L., Hollenstein, N., & Jäger, L. A. (2024). MultiplEYE Data Sharing Policy PsychArchives. https://doi.org/10.23668/psycharchives.14137
  • Müller, M.-L., Jakobi, D. N., Kasperė, R., Hollenstein, N., Jäger, L. A., & Stegenwallner-Schütz, M. (2024). Data Management Plan (DMP) for COST Action “MultiplEYE” (CA21131). Leibniz-Institut für Psychologie. https://doi.org/10.23668/psycharchives.14081
  • Deng, S., Prasse, P., Reich, D. R., Scheffer, T., & Jäger, L. A. (2024). Fine-Tuning Pre-Trained Language Models with Gaze Supervision 217–224. https://doi.org/10.18653/v1/2024.acl-short.21

• 2023

  • Bolliger, L. S., Reich, D. R., Haller, P., Jakobi, D. N., Prasse, P., & Jäger, L. A. (2023). ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts (H. Bouamor, J. Pino, & K. Bali, Eds.; pp. 15513–15538). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.960
  • Palmović, M., Jäger, L. A., & Hollenstein, N. (2023). Editorial: Eye-Tracking while Reading for Psycholinguistic and Computational Models of Language Comprehension Frontiers in Psychology, 14, 1326408. https://doi.org/10.3389/fpsyg.2023.1326408
  • Ding, C. (2023). Mouse Tracking for Reading (MoTR): A New Incremental Processing Paradigm (Master’s thesis, University of Zurich) https://doi.org/10.5167/uzh-259944
  • Prasse, P., Reich, D. R., Makowski, S., Ahn, S., Scheffer, T., & Jäger, L. A. (2023). SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks Proceedings of the Symposium on Eye Tracking Research and Applications, 18. https://doi.org/10.1145/3588015.3588410
  • Krakowczyk, D. G., Prasse, P., Reich, D. R., Lapuschkin, S., Scheffer, T., & Jäger, L. A. (2023). Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models (E. Kasneci, F. Shic, & M. Khamis, Eds.; p. 3). ACM Digital library. https://doi.org/10.1145/3588015.3588412
  • Deng, S., Reich, D. R., Prasse, P., Haller, P., Scheffer, T., & Jäger, L. A. (2023). Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading Proceedings of the ACM on Human-Computer Interaction, 7, 162. https://doi.org/10.1145/3591131
  • Nehring, J., Ahmed, A., & Jäger, L. A. (2023). Module Selection: A new Task for Dialog Systems https://www.dfki.de/web/forschung/projekte-publikationen/publikation/13137
  • Hollenstein, N., Tröndle, M., Plomecka, M., Kiegeland, S., Özyurt, Y., Jäger, L. A., & Langer, N. (2023). The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data. Frontiers in Psychology, 13, 1028824. https://doi.org/10.3389/fpsyg.2022.1028824
  • Deng, S., Prasse, P., Reich, D. R., Scheffer, T., & Jäger, L. A. (2023). Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding (H. Bouamor, J. Pino, & K. Bali, Eds.; pp. 6500–6507). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.400
  • Makowski, S., Prasse, P., Jäger, L. A., & Scheffer, T. (2023). Detection of Drowsiness and Impending Microsleep from Eye Movements NeuRIPS 2023 Workshop on Gaze Meets ML, New Orleans. https://openreview.net/forum?id=TaLDynXjZa
  • Krakowczyk, D. G., Reich, D. R., Chwastek, J., Jakobi, D. N., Prasse, P., Süss, A., Turuta, O., Kasprowski, P., & Jäger, L. A. (2023). pymovements: A Python Package for Eye Movement Data Processing (E. Kasneci, F. Shic, & M. Khamis, Eds.; p. 53). ACM Digital library. https://doi.org/10.1145/3588015.3590134

• 2022

  • Krakowczyk, D., Reich, D. R., Prasse, P., Lapuschkin, S., Scheffer, T., & Jäger, L. A. (2022). Selection of XAI methods matters: Evaluation of feature attribution methods for oculomotoric biometric identification (210:66-97). 1–31. https://proceedings.mlr.press/v210/krakowczyk23a
  • Makowski, S., Prasse, P., Jäger, L. A., & Scheffer, T. (2022). Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue 10007970. https://doi.org/10.1109/ijcb54206.2022.10007970
  • Bradshaw, L., Chodroff, E., Jäger, L., & Dellwo, V. (2022). Fundamental Frequency Variability over Time in Telephone Interactions (H. Ko & J. H. L. Hansen, Eds.; pp. 101–105). ISCA. https://doi.org/10.21437/interspeech.2022-10669
  • Deng, S., Prasse, P., Reich, D. R., Dziemian, S., Stegenwallner-Schütz, M., Krakowczyk, D. G., Makowski, S., Langer, N., Scheffer, T., & Jäger, L. A. (2022). Detection of ADHD based on eye movements during natural viewing 403–418. https://doi.org/10.1007/978-3-031-26422-1_25
  • Reich, D. R., Prasse, P., Tschirner, C., Haller, P., Goldhammer, F., & Jäger, L. A. (2022). Inferring Native and Non-Native Human Reading Comprehension and Subjective Text Difficulty from Scanpaths in Reading 23. https://doi.org/10.1145/3517031.3529639
  • Prasse, P., Reich, D. R., Makowski, S., Jäger, L. A., & Scheffer, T. (2022). Fairness in Oculomotoric Biometric Identification (F. Shic, E. Kasneci, M. Khamis, & et al, Eds.; p. 22). ACM Digital library. https://doi.org/10.1145/3517031.3529633
  • Haller, P., Säuberli, A., Kiener, S. E., Pan, J., Yan, M., & Jäger, L. A. (2022). Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models (S. Štajner, H. Saggion, D. Ferrés, & et al, Eds.; pp. 111–118). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.tsar-1.10
  • Hollenstein, N., Gonzalez-Dios, I., Beinborn, L., & Jäger, L. A. (2022). Patterns of text readability in human and predicted eye movements Proceedings of the Workshop on Cognitive Aspects of the Lexicon, 1–15. https://doi.org/10.18653/v1/2022.cogalex-1.1

• 2021

  • Meister, C., Pimentel, T., Haller, P., Jäger, L. A., Cotterell, R., & Levy, R. (2021). Revisiting the Uniform Information Density Hypothesis (M.-F. Moens, X. Huang, L. Specia, & S. W. Yih, Eds.; pp. 963–980). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.74
  • Thouvenin, F., Christen, M., Bernstein, A., Braun Binder, N., Burri, T., Donnay, K., Jäger, L. A., Jaffé, M., Krauthammer, M., Lohmann, M., Mätzener, A., Mützel, S., Obrecht, L., Ritter, N., Spielkamp, M., & Volz, S. (2021). Positionspapier: Ein Rechtsrahmen für Künstliche Intelligenz 1–7. https://www.dsi.uzh.ch/dam/jcr:3a0cb402-c3b3-4360-9332-f800895fdc58/dsi-strategy-lab-21-de.pdf
  • Makowski, S., Prasse, P., Reich, D. R., Krakowczyk, D., Jäger, L. A., & Scheffer, T. (2021). DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection using Deep Neural Networks IEEE Transactions on Biometrics, Behavior, and Identity Science, 3, 506–518. https://doi.org/10.1109/tbiom.2021.3116875
  • Hollenstein, N., Pirovano, F., Zhang, C., Jäger, L. A., & Beinborn, L. (2021). Multilingual Language Models Predict Human Reading Behavior (K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou, Eds.; pp. 106–123). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.10