Research

SCIoI Publications

2024

Battaje, A., Godinez, A., Hanning, N., Rolfs, M., & Brock, O. (2024). An Information Processing Pattern from Robotics Predicts Unknown Properties of the Human Visual System. bioRxiv. https://doi.org/10.1101/2024.06.20.599814
Baum, J., Eiserbeck, A., Maier, M., & Abdel Rahman, R. (2024). The evaluation of presumed deepfakes with different basic emotional expressions depends on valence. Psychologie und Gehirn.
Baum, M., Schattenhofer, L., Rössler, T., Osuna-Mascaro, A., Auersperg, A., Kacelnik, A., & Brock, O. (2024). Mechanical Problem Solving in Goffin’s Cockatoos - Towards Modeling Complex Behavior. Adaptive Behavior. https://doi.org/10.1177/10597123241270764
Blume, F., Qu, R., Bideau, P., Maier, M., Abdel Rahman, R., & Hellwich, O. (2024). How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal Context by Modeling Mental Representations. GCPR 2024.
Bolenz, F., & Pachur, T. (2024). Older adults select different but not simpler strategies than younger adults in risky choice. PLoS Computational Biology, 20(6), e1012204. https://doi.org/10.1371/journal.pcbi.1012204
Boon, M. N., Andresen, N., Traverso, S., Meier, S., Schuessle, F., Hellwich, O., Lewejohann, L., Thöne-Reineke, C., Sprekeler, H., & Hohlbaum, K. (2024). Mechanical problem solving in mice. bioRxiv. https://doi.org/10.1101/2024.07.29.605658
Breen, A., & Deffner, D. (2024). Risk-sensitive learning is a winning strategy for leading an urban invasion. Elife. https://doi.org/10.7554/eLife.89315.3
Brock, O. (2024). Intelligence as Computation. arXiv. https://doi.org/10.48550/arXiv.2405.16604
Burns, A. L., Licht, M., Heathcote, R. J., Krause, J., & Hansen, M. J. (2024). Rapid color change in a group-hunting pelagic predator attacking schooling prey. Current Biology, 34, R131–R132. https://doi.org/10.1016/j.cub.2023.12.040
Carlsson, O., Gerken, J. E., Linander, H., Spiess, H., Ohlsson, F., Petersson, C., & Persson, D. (2024). HEAL-SWIN: A Vision Transformer On The Sphere. CVPR. https://doi.org/10.48550/arXiv.2307.07313
Chidambaram, S., Wintergerst, S., & Kacelnik, A. (2024). Serial reversal learning in nectar-feeding bats. Animal Cognition, 27.
Clarenau, V. C. von, Appelhoff, S., Pachur, T., & Spitzer, B. (2024). Over- and Underweighting of Extreme Values in Decisions From Sequential Samples. Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0001530
Coelho Mollo, D. (2024). AI-as-exploration: Navigating intelligence space. ArXiv. https://doi.org/10.48550/arXiv.2401.07964
Dallabetta, M., Dobberstein, C., Breiding, A., & Akbik, A. (2024). Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions. ACL 2024. https://doi.org/10.48550/arXiv.2403.15279
Deffner, D., Mezey, D., Kahl, B., Schakowski, A., Romanczuk, P., Wu, C. M., & Kurvers, R. (2024). Collective incentives reduce over-exploitation of social information in unconstrained human groups. Nature Communications. https://doi.org/10.1038/s41467-024-47010-3
Ehlman, S., Scherer, U., Bierbach, D., Stärk, L., Beese, M., & Wolf, M. (2024). Developmental arcs of plasticity in whole movement repertoires of a clonal fish. bioRxiv. https://doi.org/10.1101/2023.12.07.570540
Eiserbeck, A., Wudarczyk, O., Kuhlen, A., Hafner, V., Haynes, J.-D., & Rasha, A. R. (2024). Communicative context enhances emotional word processing with human speakers but not with robots. ASSC27.
Eiserbeck, A., Enge, A., Rabovsky, M., & Abdel Rahman, R. (2024). Distrust before first sight? Examining knowledge- and appearance-based effects of trustworthiness on the visual consciousness of faces. Consciousness and Cognition, 117, 103629. https://doi.org/10.1016/j.concog.2023.103629
Frenkel, J., Cajar, A., Engbert, R., & Lazarides, R. (2024). Exploring the Impact of Nonverbal Social Behavior on Learning Outcomes in Instructional Video Design. Scientific Reports. https://doi.org/10.1038/s41598-024-63487-w
Garbaciauskas, L., Ploner, M., & Akbik, A. (2024). Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks. ACL 2024.
Golde, J., Hamborg, F., & Akbik, A. (2024). Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition. EACL 2024. https://doi.org/10.48550/arXiv.2403.14222
Golde, J., Haller, P., Hamborg, F., Risch, J., & Akbik, A. (2024). Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs. EMNLP 2023. https://doi.org/10.18653/v1/2023.emnlp-demo.1
Govoni, P., & Romanczuk, P. (2024). Fundamental Visual Navigation Algorithms: Indirect Sequential, Biased Diffusive, & Direct Pathing. arXiv. https://doi.org/10.48550/arXiv.2407.13535
Guo, S., & Gallego, G. (2024). Event-based Mosaicing Bundle Adjustment. European Conference on Computer Vision (ECCV).
Guo, S., & Gallego, G. (2024). CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization. IEEE Transactions on Robotics. https://doi.org/10.1109/TRO.2024.3378443
Halawa, M., Blume, F., Bideau, P., Maier, M., Abdel Rahman, R., & Hellwich, O. (2024). Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition. IEEE Computer Vision and Pattern Recognition Conference Workshops (CVPRW) 2024. https://doi.org/10.48550/arXiv.2404.10904
Halawa, M., Blume, F., Bideau, P., Maier, M., Abdel Rahman, R., & Hellwich, O. (2024). Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition. CVPR Workshop. https://doi.org/10.48550/arXiv.2404.10904
Haller, P., Aynetdinov, A., & Akbik, A. (2024). OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs. NAACL 2024. https://doi.org/10.18653/v1/2024.naacl-demo.8
Haller, P., Golde, J., & Akbik, A. (2024). PECC: Problem Extraction and Coding Challenges. COLING 2024. https://doi.org/10.48550/arXiv.2404.18766
Hamann, F., Ghosh, S., Martínez, I. J., Hart, T., Kacelnik, A., & Gallego, G. (2024). Low-power, Continuous Remote Behavioral Localization with Event Cameras. CVPR. https://doi.org/10.48550/arXiv.2312.03799
Hamann, F., Wang, Z., Asmanis, I., Chaney, K., Gallego, G., & Daniilidis, K. (2024). Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation. European Conference on Computer Vision (ECCV).
Hohlbaum, K., Andresen, N., Mieske, P., Kahnau, P., Lang, B., Diederich, K., Palme, R., Mundhenk, L., Sprekeler, H., Hellwich, O., Thöne-Reineke, C., & Lewejohann, L. (2024). Lockbox enrichment facilitates manipulative and cognitive activities for mice. Open Research Europe. https://doi.org/10.12688/openreseurope.17624.2
Hübert, H., & Yun, H. S. (2024). Sobotify: A Framework for Turning Robots into Social Robots. Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA.
Leonhardt, A., Maier, M., & Rahman, R. A. (2024). Effects of moral-emotional behavior and intentionality on mind attribution and evaluation of social robots. OSF. https://doi.org/10.31219/osf.io/6k487
Licht, M., Burns, A., Pacher, K., Krause, S., Bartashevich, P., Romanczuk, P., Hansen, M., Then, A., & Krause, J. (2024). Sailfish generate foraging opportunities for seabirds in multi-species predator aggregations. Biology Letters, 20(7). https://doi.org/10.1098/rsbl.2024.0177
Lindström, A. D., Methnani, L., Krause, L., Ericson, P., Martínez de Rituerto de Troya, Í., Coelho Mollo, D., & Dobbe, R. (2024). AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations. ArXiv. https://doi.org/10.48550/arXiv.2406.18346
Maier, M., & Abdel Rahman, R. (2024). The neural dynamics of sudden insight in social perception. Annual Meeting of the Cognitive Science Society 2024.
Maier, M., Leonhardt, A., Blume, F., Bideau, P., Hellwich, O., & Rahman, R. A. (2024). Brain dynamics of mental state attribution during perception of social robot faces. OSF. https://doi.org/10.31219/osf.io/2rxy9
Mellmann, H., Arbuzova, P., Kontogiorgos, D., Yordanova, M., Haensel, J. X., Hafner, V. V., & Bryson, J. J. (2024). Effects of Transparency in Humanoid Robots - A Pilot Study. HRI ’24 Companion. https://doi.org/10.1145/3610978.3640613
Mezey, D., Bastien, R., Zheng, Y., McKee, N., Stoll, D., Hamann, H., & Romanczuk, P. (2024). Purely vision-based collective movement of robots. arXiv. https://doi.org/10.48550/arXiv.2406.17106
Mezey, D., Deffner, D., Kurvers, R. H., & Romanczuk, P. (2024). Visual social information use in collective foraging. PLoS Computational Biology, 20, 1–24.
Musiolek, L., Bierbach, D., Weimar, N., Hamon, M., Krause, J., & Hafner, V. V. (2024). Evolving Artificial Neural Networks for Simulating Fish Social Interactions. EvoStar2024. https://doi.org/10.1007/978-3-031-56852-7_10
Oksuz, H. Y., Molinari, F., Sprekeler, H., & Raisch, J. (2024). Boosting Fairness and Robustness in Over-the-Air Federated Learning. IEEE Control Systems Letters (LCSS), 8, 682–687. https://doi.org/10.1109/lcsys.2024.3402123
Pacher, K., Hernández-Román, N., Juarez-Lopez, A., Jiménez-Jiménez, J. E., Lukas, J., Sevinchan, Y., Krause, J., Arias-Rodríguez, L., & Bierbach, D. (2024). Thermal tolerance in an extremophile fish from Mexico is not affected by environmental hypoxia. Biology Open. https://doi.org/10.1242/bio.060223
Pacher, K., Krause, J., Bartashevich, P., Romanczuk, P., Bideau, P., Pham, D., Burns, A., Deffner, D., Dhellemmes, F., Binder, B., Boswell, K., Galván-Magaña, F., Domenici, P., & Hansen, M. (2024). Evidence for a by-product mutualism in a group hunter depends on prey movement state. Functional Ecology. https://doi.org/10.1111/1365-2435.14638
Pham, D., Hansen, M., Dhellemmes, F., Krause, J., & Bideau, P. (2024). Watching Swarm Dynamics from Above: A Framework for Advances Object Tracking in Drone Videos. IEEE Computer Vision and Pattern Recognition Conference Workshops (CVPRW) 2024.
Ploner, M., & Akbik, A. (2024). Parameter-Efficient Fine-Tuning: Is There An Optimal Subset of Parameters to Tune? EACL 2024.
Puy, A., Gimeno, E., Torrents, J., Bartashevich, P., Miguel, M. C., Pastor-Satorras, R., & Romanczuk, P. (2024). Selective social interactions and speed-induced leadership in schooling fish. Proceedings National Academy of Sciences. https://doi.org/10.1073/pnas.2309733121

2023

2022

Ajuwon, V., Ojeda, A., Murphy, R., Monteiro, T., & Kacelnik, A. (2022). Paradoxical choice and the reinforcing value of information. Animal Cognition, 26. https://doi.org/10.1007/s10071-022-01698-2
Battaje, A., & Brock, O. (2022). One Object at a Time: Accurate and Robust Structure From Motion for Robots. IROS 2022. https://doi.org/10.1109/IROS47612.2022.9981953
Baum, M., & Brock, O. (2022). "The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection. ICRA 2022. https://doi.org/10.1109/ICRA46639.2022.9811845
Baum, M., Schattenhofer, L., Rössler, T., Osuna-Mascaro, A., Auersperg, A., Kacelnik, A., & Brock, O. (2022). Yoking-Based Identification of Learning Behavior in Artificial and Biological Agents. SAB 2022. https://doi.org/10.1007/978-3-031-16770-6_6
Bierbach, D., Gómez-Nava, L., Francisco, F. A., Lukas, J., Musiolek, L., Hafner, V. V., Landgraf, T., Romanczuk, P., & Krause, J. (2022). Live fish learn to anticipate the movement of a fish-like robot. Bioinspiration & Biomimetics.
Bolenz, F., & Eppinger, B. (2022). Valence bias in metacontrol of decision making in adolescents and young adults. Child Development. https://doi.org/10.1111/cdev.13693
Bolenz, F., & Pachur, T. (2022). Exploring the structure of predecisional information search in risky choice. Proceedings of the 44th Annual Conference of the Cognitive Science Society, 2297–2302.
Bolenz, F., Profitt, M., Stechbarth, F., Eppinger, B., & Strobel, A. (2022). Need for cognition does not account for individual differences in metacontrol of decision making. Scientific Reports. https://doi.org/10.1038/s41598-022-12341-y
Chen, Y., Mikkelsen, J., Binder, A., Alt, C., & Hennig, L. (2022). A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition. Proceedings of the 7th Workshop on Representation Learning for NLP. https://doi.org/10.18653/v1/2022.repl4nlp-1.6
Chevalère, J., Kirtay, M., Hafner, V., & Lazarides, R. (2022). Who to Observe and Imitate in Humans and Robots: The Importance of Motivational Factors. International Journal of Social Robotics. https://doi.org/10.1007/s12369-022-00923-9
Coelho Mollo, D. (2022). Intelligent Behaviour. Erkenntnis. https://doi.org/10.1007/s10670-022-00552-8
Cracco, E., Bernardet, U., Sevenhant, R., Copman, N. V. F., Durnez, W., Bombeke, K., & Brass, M. (2022). Evidence for a two-step model of social group influence. IScience, 9(25), 104891. https://doi.org/10.1016/j.isci.2022.104891
Deffner, D., & McElreath, R. (2022). When does selection favor learning from the old? Social learning in age-structured populations. Plos One. https://doi.org/10.1371/journal.pone.0267204
Deffner, D., Kandler, A., & Fogarty, L. (2022). Effective population size for culturally evolving traits. Plos Computational Biology. https://doi.org/10.1371/journal.pcbi.1009430
Doran, C., Bierbach, D., Lukas, J., Klamser, P., Landgraf, T., Klenz, H., Habedank, M., Arias-Rodriguez, L., Krause, S., Romanczuk, P., & Krause, J. (2022). Fish waves as emergent collective antipredator behavior. Current Biology. https://doi.org/10.1016/j.cub.2021.11.068
Driess, D., Schubert, I., Florence, P., Li, Y., & Toussaint, M. (2022). Reinforcement Learning with Neural Radiance Fields. NeurIPS 2022. https://doi.org/10.48550/arXiv.2206.01634
Driess, D., Huang, Z., Li, Y., Tedrake, R., & Toussaint, M. (2022). Learning Multi-Object Dynamics with Compositional Neural Radiance Fields. CoRL 2022. https://doi.org/10.48550/arXiv.2202.11855
Ehlman, S., Scherer, U., & Wolf, M. (2022). Developmental feedbacks and the emergence of individuality. Royal Society Open Science.
Formica, S., Gonzalez-Garcia, C., Senoussi, M., Marinazzo, D., & Brass, M. (2022). Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation. ENeuro, 9. https://doi.org/10.1523/ENEURO.0225-22.2022
Ghosh, S., & Gallego, G. (2022). Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion. ECCVW Ego4D 2022.
Gómez-Nava, L., Bon, R., & Peruani, F. (2022). Intermittent collective motion in sheep results from alternating the role of leader and follower. Nature Physics, 8. https://doi.org/10.1038/s41567-022-01769-8
Goris, J., Braem, S., Herck, S. V., Simoens, J., Deschrijver, E., Wiersema, J. R., Paton, B., Brass, M., & Todd, J. (2022). Reduced primacy bias in autism during early sensory processing. Journal of Neuroscience, 19(42). https://doi.org/10.1523/JNEUROSCI.3088-20.2022
Ha, J.-S., Driess, D., & Toussaint, M. (2022). Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input. IEEE Robotics and Automation Letters. https://doi.org/10.1109/LRA.2022.3194955
Halawa, M., Hellwich, O., & Bideau, P. (2022). Action based Contrastive Learning for Trajectory Prediction. European Conference on Computer Vision (ECCV), 143–159. https://doi.org/10.1007/978-3-031-19842-7_9
Hamann, F., & Gallego, G. (2022). Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras. International Conference on Pattern Recognition (ICPR), Workshop on Visual observation and analysis of Vertebrate And Insect Behavior.
Hansen, M., Krause, S., Dhellemmes, F., Pacher, K., Kurvers, R., Domenici, P., & Krause, J. (2022). Mechanisms of prey division in striped marlin, a marine group hunting predator. Communications Biology.
Harbecke, D., Chen, Y., Hennig, L., & Alt, C. (2022). Why only Micro-F1? Class Weighting of Measures for Relation Classification. Proceedings of the 1st Workshop on Efficient Benchmarking in NLP. https://doi.org/10.18653/v1/2022.nlppower-1.4
Harris, J., Driess, D., & Toussaint, M. (2022). FC3: Feasibility-Based Control Chain Coordination. IROS 2022. https://doi.org/10.1109/IROS47612.2022.9981758
Henke, L., Guseva, M., Wagemans, K., Pischedda, D., Haynes, J.-D., Jahn, G., & Anders, S. (2022). Surgical face masks do not impair the decoding of facial expressions of negative affect more severely in older than in younger adults. Cognitive Research: Principles and Implications, 7, 63. https://doi.org/10.1186/s41235-022-00403-8
Kamat, J., Ortiz-Haro, J., Toussaint, M., Pokorny, F. T., & Orthey, A. (2022). BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning. IROS 2022. https://doi.org/10.1109/IROS47612.2022.9981732
Kirtay, M., Oztop, E., Kuhlen, A. K., Asada, M., & Hafner, V. V. (2022). Forming robot trust in heterogeneous agents during a multimodal interactive game. 2022 IEEE International Conference on Development and Learning (ICDL), 307–313. https://doi.org/10.1109/ICDL53763.2022.9962212
Kirtay, M., Oztop, E., Kuhlen, A. K., Asada, M., & Hafner, V. V. (2022). Trustworthiness assessment in multimodal human-robot interaction based on cognitive load. 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 469–476. https://doi.org/10.1109/RO-MAN53752.2022.9900730
Lange, R., & Sprekeler, H. (2022). Learning not to learn: Nature versus Nurture in Silico. AAAI 2022. https://doi.org/10.48550/arXiv.2010.04466
Laskowski, K., Bierbach, D., Jolles, J., Doran, C., & Wolf, M. (2022). The emergence and development of behavioral individuality in clonal fish. Nature Communications. https://doi.org/10.1038/s41467-022-34113-y
Li, X., & Brock, O. (2022). Learning from Demonstration Based On Environmental Constraints. IEEE Robotics and Automation Letters with IROS Option. https://doi.org/10.1109/LRA.2022.3196096
Maier, M., Leonhardt, Alexander, & Abdel Rahman, R. (2022). Bad robots? Humans rapidly attribute mental states during the perception of robot faces. KogWis 2022.
Maier, M., Frömer, R., Rost, J., Sommer, W., & Abdel Rahman, R. (2022). Linguistic and semantic influences on early vision: evidence from object perception and mental imagery. Cognitive Neuroscience of Language Embodiment and Relativity.
Maier, M., Blume, F., Bideau, P., Hellwich, O., & Abdel Rahman, R. (2022). Knowledge-Augmented Face Perception: Prospects for the Bayesian Brain-Framework to Align AI and Human Vision. Consciousness and Cognition, 101. https://doi.org/10.1016/j.concog.2022.103301
Makowicz, A. M., Bierbach, D., Richardson, C., & Hughes, K. A. (2022). Cascading indirect genetic effects in a clonal vertebrate. Proceedings of the Royal Society B: Biological Sciences, 289(1978). https://doi.org/10.1098/rspb.2022.0731
McNamara, J., & Wolf, M. (2022). Social interaction can select for reduced ability. Proceedings of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rspb.2022.1788
Meindl, M., Molinari, F., Lehmann, D., & Seel, T. (2022). Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/TCST.2021.3109646
Meindl, M., Lehmann, D., & Seel, T. (2022). Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics. Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2022.793512
Molinari, F., Agrawal, N., Stańczak, S., & Raisch, J. (2022). Over-The-Air Max-Consensus in Clustered Networks Adopting Half-Duplex Communication Technology. IEEE Transactions on Control of Network Systems, Early access. https://doi.org/10.1109/TCNS.2022.3212870
Muscinelli, F., Roth, N., Shurygina, O., Obermayer, K., & Rolfs, M. (2022). Object-based Spread of Attention Affects Fixation Duration During Free Viewing. PERCEPTION / 44th European Conference on Visual Perception (ECVP) 2022.
Ortiz-Haro, J., Ha, J.-S., Driess, D., & Toussaint, M. (2022). Structured deep generative models for sampling on constraint manifolds in sequential manipulation. CoRL 2021.
Ortiz-Haro, J., Karpas, E., Katz, M., & Toussaint, M. (2022). A Conflict-driven Interface between Symbolic Planning and Nonlinear Constraint Solving. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2022.3191948
Pachur, T. (2022). Strategy selection in decisions from givens: Deciding at a glance? Cognitive Psychology. https://doi.org/10.1016/j.cogpsych.2022.101483
Pannen, T. J., Puhlmann, S., & Brock, O. (2022). A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition. IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA46639.2022.9811761
Poel, W., Daniels, B. C., Sosna, M. M. G., Twomey, C. R., Blanc, S. L., Couzin, I., & Romanczuk, P. (2022). Subcritical escape waves in schooling fish. Science Advances.
Puhlmann, S., Harris, J., & Brock, O. (2022). RBO Hand 3 – A Platform for Soft Dexterous Manipulation. IEEE Transactions on Robotics. https://doi.org/10.1109/TRO.2022.3156806

2021

Amjadi, A. S., Raoufi, M., & Turgut, A. E. (2021). A self-adaptive landmark-based aggregation method for robot swarms. Adaptive Behavior, 30(3), 223–236. https://doi.org/10.1177/1059712320985543
Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., Kao, A. B., Moran, R. E., Romanczuk, P., Rubenstein, D. I., Tombak, K. J., Bavel, J. J. V., & Weber, E. U. (2021). Stewardship of global collective behavior. Proceedings of the National Academy of Sciences, 118(27). https://doi.org/10.1073/pnas.2025764118
Battaje, A., & Brock, O. (2021). Interconnected Recursive Filters in Artificial and Biological Vision. DGR Days 2021, 32–32.
Bhatt, A., Sieler, A., Puhlmann, S., & Brock, O. (2021). Surprisingly Robust In-Hand Manipulation: An Empirical Study. Proceedings of Robotics: Science and Systems. https://doi.org/10.15607/RSS.2021.XVII.089
Bierbach, D., Francisco, F., Lukas, J., Landgraf, T., Maxeiner, M., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2021). Biomimetic robots promote the 3Rs Principle in animal testing. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10.1162/isal_a_00375
Bierbach, D., Wenchel, R., Gehrig, S., Wersing, S., O'Connor, O. L., & Krause, J. (2021). Male Sexual Preference for Female Swimming Activity in the Guppy (Poecilia reticulata). Biology, 10(2). https://doi.org/10.3390/biology10020147
Chouzouris, T., Roth, N., Cakan, C., & Obermayer, K. (2021). Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators. Physical Review E, 104(2), 24213. https://doi.org/10.1103/PhysRevE.104.024213
Coelho Mollo, D. (2021). Why go for a computation-based approach to cognitive representation. Synthese, 199(3), 6875–6895.
Coelho Mollo, D. (2021). Deflationary realism: Representation and idealisation in cognitive science. Mind & Language, 1–19. https://doi.org/10.1111/mila.12364
Coelho Mollo, D., Millière, R., Rathkopf, C., & Stinson, C. (2021). Conceptual Combinations – Benchmark task for Beyond the Imitation Game Benchmark. Github.
Daniels, B. C., & Romanczuk, P. (2021). Quantifying the impact of network structure on speed and accuracy in collective decision-making. Theory in Biosciences, 140, 379–390. https://doi.org/10.1007/s12064-020-00335-1
Demandt, N., Bierbach, D., Kurvers, R. H. J. M., Krause, J., Kurtz, J., & Scharsack, J. P. (2021). Parasite infection impairs the shoaling behaviour of uninfected shoal members under predator attack. Behavioral Ecology and Sociobiology. https://doi.org/10.1007/s00265-021-03080-7
Driess, D., Ha, J.-S., Toussaint, M., & Tedrake, R. (2021). Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning. CoRL 2021. https://doi.org/10.48550/arXiv.2110.00792
Driess, D., Ha, J.-S., & Toussaint, M. (2021). Learning to solve sequential physical reasoning problems from a scene image. The International Journal of Robotics Research, 40(12–14), 1435–1466. https://doi.org/10.1177/02783649211056967
Gu, C., Learned-Miller, E., Gallego, G., Sheldon, D., & Bideau, P. (2021). The Spatio-Temporal Poisson Point process: A simple Model for the Alignment of Event Camera Data. International Conference on Computer Vision (ICCV), 13495–13504. https://doi.org/10.1109/ICCV48922.2021.01324
Kahnau, P., Guenther, A., Boon, M. N., Terzenbach, J. D., Hanitzsch, E., Lewejohann, L., & Brust, V. (2021). Lifetime Observation of Cognition and Physiological Parameters in Male Mice. Frontiers in Behavioral Neuroscience, 15, 709775. https://doi.org/10.3389/fnbeh.2021.709775
Kirtay, M., Oztop, E., Asada, M., & Hafner, V. V. (2021). Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction. 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 189–196. https://doi.org/10.1109/RO-MAN50785.2021.9515494
Kirtay, M., Chevalère, J., Lazarides, R., & Hafner, V. V. (2021). Learning in Social Interaction: Perspectives from Psychology and Robotics. 2021 IEEE International Conference on Development and Learning (ICDL), 1–8. https://doi.org/10.1109/ICDL49984.2021.9515648
Kirtay, M., Oztop, E., Asada, M., & Hafner, V. V. (2021). Modeling robot trust based on emergent emotion in an interactive task. 2021 IEEE International Conference on Development and Learning (ICDL), 1–8. https://doi.org/10.1109/ICDL49984.2021.9515645
Klamser, P., & Romanczuk, P. (2021). Collective predator evasion: Putting the criticality hypothesis to the test. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1008832
Klamser, P. P., Gómez-Nava, L., Landgraf, T., Jolles, J. W., Bierbach, D., & Romanczuk, P. (2021). Impact of Variable Speed on Collective Movement of Animal Groups. Frontiers in Physics, 9. https://doi.org/10.3389/fphy.2021.715996
Krause, J., Romanczuk, P., Cracco, E., Arlidge, W., Nassauer, A., & Brass, M. (2021). Collective rule-breaking. Trends in Cognitive Sciences.
Kurvers, R. H. J. M., Herzog, S. M., Hertwig, R., Krause, J., & Wolf, M. (2021). Pooling decisions decreases variation in response bias and accuracy. IScience, 24(7), 102740. https://doi.org/10.1016/j.isci.2021.102740
Laskowski, K. L., Seebacher, F., Habedank, M., Meka, J., & Bierbach, D. (2021). Two Locomotor Traits Show Different Patterns of Developmental Plasticity Between Closely Related Clonal and Sexual Fish. Frontiers in Physiology, 12, 740604. https://doi.org/10.3389/fphys.2021.740604
Lazarides, R., & Chevalère, J. (2021). Artificial intelligence and education: Addressing the variability in learners' emotion and motivation with adaptive teaching assistants. Bildung Und Erziehung, 74(3), 264–279. https://doi.org/10.13109/buer.2021.74.3.264
Lazarides, R., & Raufelder, D. (2021). Control-value theory in the context of teaching: does teaching quality moderate relations between academic self-concept and achievement emotions? British Journal of Educational Psychology, 91(1), 127–147. https://doi.org/10.1111/bjep.12352
Leonhardt, A., Maier, M., & Abdel Rahman, R. (2021). The impact of affective knowledge on the perception and evaluation of robot faces. 5th Virtual Social Interactions (VSI) Conference.
Lois-Milevicich, J., Cerrutti, M., Kacelnik, A., & Reboreda, J. (2021). Sex differences in learning flexibility in an avian brood parasite, the shiny cowbird. Behavioural Processes. Behavioural Processes, 189. https://doi.org/10.1016/j.beproc.2021.104438
Lu, Y., Bierbach, D., Ormanns, J., Warren, W. C., Walter, R. B., & Schartl, M. (2021). Fixation of allelic gene expression landscapes and expression bias pattern shape the transcriptome of the clonal Amazon molly. Genome Research. https://doi.org/10.1101/gr.268870.120
Lukas, J., Romanczuk, P., Klenz, H., Klamser, P., Arias Rodriguez, L., Krause, J., & Bierbach, D. (2021). Acoustic and visual stimuli combined promote stronger responses to aerial predation in fish. Behavioral Ecology, arab043. https://doi.org/10.1093/beheco/arab043
Lukas, J., Auer, F., Goldhammer, T., Krause, J., Romanczuk, P., Klamser, P., Arias-Rodriguez, L., & Bierbach, D. (2021). Diurnal Changes in Hypoxia Shape Predator-Prey Interaction in a Bird-Fish System. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.619193
Lukas, J., Kalinkat, G., Miesen, F. W., Landgraf, T., Krause, J., & Bierbach, D. (2021). Consistent Behavioral Syndrome Across Seasons in an Invasive Freshwater Fish. Frontiers in Ecology and Evolution, 8. https://doi.org/10.3389/fevo.2020.583670
Mieske, P., Diederich, K., & Lewejohann, L. (2021). Roaming in a Land of Milk and Honey: Life Trajectories and Metabolic Rate of Female Inbred Mice Living in a Semi Naturalistic Environment. Animals, 11(10), 3002. https://doi.org/10.3390/ani11103002
Monteiro, T., Hart, T., & Kacelnik, A. (2021). Imprinting on time-structured acoustic stimuli in ducklings. Biology Letters, 17. https://doi.org/10.1098/rsbl.2021.0381
Mühlhoff, R. (2021). Predictive Privacy: Towards an Applied Ethics of Data Analytics. Ethics and Information Technology. https://doi.org/10.1007/s10676-021-09606-x
Páll, E., & Brock, O. (2021). Analysis of Open-Loop Grasping From Piles. IEEE International Conference on Robotics and Automation (ICRA), 2591–2597. https://doi.org/10.1109/ICRA48506.2021.9561065
Pischedda, D., Lange, A., Kirtay, M., Wudarczyk, O. A., Abdel Rahman, R., Hafner, V. V., Kuhlen, A. K., & Haynes, J.-D. (2021). Am I speaking to a human, a robot, or a computer? Neural representations of task partners in communicative interactions with humans or artificial agents. Neuroscience 2021.
Pischedda, D., Lange, A., Kirtay, M., Wudarczyk, O. A., Abdel Rahman, R., Hafner, V. V., Kuhlen, A. K., & Haynes, J.-D. (2021). Who is my interlocutor? Partner-specific neural representations during communicative interactions with human or artificial task partners. 5th Virtual Social Interactions (VSI) Conference.
Poel, W., Winklmayr, C., & Romanczuk, P. (2021). Spatial Structure and Information Transfer in Visual Networks. Frontiers in Physics. https://doi.org/10.3389/fphy.2021.716576
Pütz, O. (2021). Managing exactness and vagueness in computer science work: Programming and self-repair in meetings. Social Studies of Science, 51(6), 938–961. https://doi.org/10.1177/03063127211010972
Raoufi, M., Hamann, H., & Romanczuk, P. (2021). Speed-vs-Accuracy Tradeoff in Collective Estimation: An Adaptive Exploration-Exploitation Case. IEEE 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS).
Roth, N., Bideau, P., Hellwich, O., Rolfs, M., & Obermayer, K. (2021). Modeling the influence of objects on saccadic decisions in dynamic real-world scenes. PERCEPTION / 43rd European Conference on Visual Perception (ECVP) 2021.
Roth, N., Bideau, P., Hellwich, O., Rolfs, M., & Obermayer, K. (2021). A modular framework for object-based saccadic decisions in dynamic scenes. CVPR EPIC Workshop / arXiv:2106.06073. https://doi.org/10.48550/arXiv.2106.06073
Schmittwilken, L., Matic, M., Maertens, M., & Vincent, J. (2021). BRENCH: An open-source framework for b(r)enchmarking brightness models. Journal of Vision. https://doi.org/10.1167/jov.22.3.36
Schubert, I., Driess, D., Oguz, O. S., & Toussaint, M. (2021). Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. NeurIPS 2021. https://doi.org/10.48550/arXiv.2111.07908
Schweitzer, R., & Rolfs, M. (2021). Intrasaccadic motion streaks jump-start gaze correction. Science Advances, 7(30), eabf2218. https://doi.org/10.1126/sciadv.abf2218
Shurygina, O., & Rolfs, M. (2021). Visual sensitivity and reaction time measures show no evidence for purely exogenous object-based attention. Journal of Vision / VSS 2021, 2571–2571. https://doi.org/10.1167/jov.21.9.2571
Shurygina, O., Pooresmaeili, A., & Rolfs, M. (2021). Pre-saccadic attention spreads to stimuli forming a perceptual group with the saccade target. Cortex, 140, 179–198. https://doi.org/10.1016/j.cortex.2021.03.020
Spatola, N., & Wudarczyk, O. (2021). Ascribing emotions to robots: Explicit and implicit attribution of emotions and perceived robot anthropomorphism. Computers in Human Behavior, 124, 106934. https://doi.org/10.1016/j.chb.2021.106934

2020

Andresen, N., Wöllhaf, M., Hohlbaum, K., Lewejohann, L., Hellwich, O., Thöne-Reineke, C., & Belik, V. (2020). Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis. PLOS ONE, 15(4), e0228059. https://doi.org/10.1371/journal.pone.0228059
Bastien, R., & Romanczuk, P. (2020). A model of collective behavior based purely on vision. Science Advances, 6(6), eaay0792. https://doi.org/10.1126/sciadv.aay0792
Bierbach, D., Krause, S., Romanczuk, P., Lukas, J., Arias-Rodriguez, L., & Krause, J. (2020). An interaction mechanism for the maintenance of fission–fusion dynamics under different individual densities. PeerJ, 8, e8974. https://doi.org/10.7717/peerj.8974
Bierbach, D., Mönck, H. J., Lukas, J., Habedank, M., Romanczuk, P., Landgraf, T., & Krause, J. (2020). Guppies Prefer to Follow Large (Robot) Leaders Irrespective of Own Size. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.00441
Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, A., others, Pischedda, D., others, & Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 84–88. https://doi.org/10.1038/s41586-020-2314-9
Coelho Mollo, D. (2020). Against Computational Perspectivalism. The British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/axz036
Halawa, M., Wollhaf, M., Vellasques, E., Sanchez Sanz, U., UrkoSanz, & Hellwich, O. (2020). Learning Disentangled Expression Representations from Facial Images. arxiv and WiCV at ECCV2020. https://doi.org/10.48550/arXiv.2008.07001
Jolles, J. W., Weimar, N., Landgraf, T., Romanczuk, P., Krause, J., & Bierbach, D. (2020). Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish. Biology Letters, 16(9), 20200436. https://doi.org/10.1098/rsbl.2020.0436
Kirtay, M., Wudarczyk, O. A., Pischedda, D., Kuhlen, A. K., Abdel Rahman, R., Haynes, J.-D., & Hafner, V. V. (2020). Modeling robot co-representation: state-of-the-art, open issues, and predictive learning as a possible framework. 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 1–8. https://doi.org/10.1109/ICDL-EpiRob48136.2020.9278031
Landgraf, T., Moenck, H. J., Gebhardt, G. H. W., Weimar, N., Hocke, M., Maxeiner, M., Musiolek, L., Krause, J., & Bierbach, D. (2020). Socially competent robots: adaptation improves leadership performance in groups of live fish. arXiv:2009.06633 [cs]. https://doi.org/10.48550/arXiv.2009.06633
Landgraf, T., Gebhardt, G. H. W., Bierbach, D., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2020). Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/annurev-control-061920-103228
Lupyan, G., Abdel Rahman, R., Boroditsky, L., & Clark, A. (2020). Effects of Language on Visual Perception. Trends in Cognitive Sciences, 24(11), 930–944. https://doi.org/10.1016/j.tics.2020.08.005
Meindl, M., Molinari, F., Raisch, J., & Seel, T. (2020). Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation. IFAC-PapersOnLine 21st IFAC World Congress, 53, 1480–1486. https://doi.org/10.1016/j.ifacol.2020.12.1938
Mellmann, H., Schlotter, B., Musiolek, L., & Hafner, V. V. (2020). Anticipation as a Mechanism for Complex Behavior in Artificial Agents. Artificial Life Conference Proceedings, 32, 157–159. https://doi.org/10.1162/isal_a_00314
Mühlhoff, R. (2020). Prädiktive Privatheit: Warum wir alle "etwas zu verbergen haben". VerantwortungKI – Künstliche Intelligenz und gesellschaftliche Folgen, herausgegeben von Christoph Markschies und Isabella Hermann. Bd. 3/2020. Berlin-Brandenburgische Akademie der Wissenschaften.
Mühlhoff, R. (2020). Automatisierte Ungleichheit: Ethik der Künstlichen Intelligenz in der biopolitische Wende des Digitalen Kapitalismus. Deutsche Zeitschrift Für Philosophie, 6(68). https://doi.org/10.1515/dzph-2020-0059
Musiolek, L., Hafner, V. V., Krause, J., Landgraf, T., & Bierbach, D. (2020). Robofish as Social Partner for Live Guppies. Biomimetic and Biohybrid Systems, 270–274. https://doi.org/10.1007/978-3-030-64313-3_26
Pischedda, D., Palminteri, S., & Coricelli, G. (2020). The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: From an absolute to a relative neural code. The Journal of Neuroscience, 40(16), 3268–3277. https://doi.org/10.1523/JNEUROSCI.1712-19.2020
Rahmani, P., Peruani, F., & Romanczuk, P. (2020). Flocking in complex environments—Attention trade-offs in collective information processing. PLOS Computational Biology, 16(4), e1007697. https://doi.org/10.1371/journal.pcbi.1007697
Spatola, N., & Wudarczyk, O. A. (2020). Implicit Attitudes Towards Robots Predict Explicit Attitudes, Semantic Distance Between Robots and Humans, Anthropomorphism, and Prosocial Behavior: From Attitudes to Human–Robot Interaction. International Journal of Social Robotics. https://doi.org/10.1007/s12369-020-00701-5
Tump, A. N., Pleskac, T. J., & Kurvers, R. H. J. M. (2020). Wise or mad crowds? The cognitive mechanisms underlying information cascades. Science Advances, 6(29), eabb0266. https://doi.org/10.1126/sciadv.abb0266
Winklmayr, C., Kao, A. B., Bak-Coleman, J. B., & Romanczuk, P. (2020). The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy. Proceedings of the Royal Society B: Biological Sciences, 287(1938), 20201802. https://doi.org/10.1098/rspb.2020.1802
Yun, H. S., Karl, M., & Fortenbacher, A. (2020). Designing an interactive second language learning scenario: a case study of Cozmo. Proceedings of HCI Korea, 384–387.
Yun, H. S., Fortenbacher, A., Geißler, S., & Heumos, T. (2020). Towards External Regulation of Emotions Using Sensors: Tow Case Studies. INTED2020, 9313–9320. https://doi.org/10.21125/inted.2020.2576
Zöller, G., Wall, V., & Brock, O. (2020). Active Acoustic Contact Sensing for Soft Pneumatic Actuators. 2020 IEEE International Conference on Robotics and Automation (ICRA), 7966–7972. https://doi.org/10.1109/ICRA40945.2020.9196916

2019

Galbusera, L., Finn, M. T. M., Tschacher, W., & Kyselo, M. (2019). Interpersonal synchrony feels good but impedes self-regulation of affect. Scientific Reports, 9(1), 14691. https://doi.org/10.1038/s41598-019-50960-0
Kurvers, R. H. J. M., Herzog, S. M., Hertwig, R., Krause, J., Moussaid, M., Argenziano, G., Zalaudek, I., Carney, P. A., & Wolf, M. (2019). How to detect high-performing individuals and groups: Decision similarity predicts accuracy. Science Advances, 5(11), eaaw9011. https://doi.org/10.1126/sciadv.aaw9011
Morik, M., Rastogi, D., Jonschkowski, R., & Brock, O. (2019). State Representation Learning with Robotic Priors for Partially Observable Environments. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6693–6699. https://doi.org/10.1109/IROS40897.2019.8967938
Sosna, M. M. G., Twomey, C. R., Bak-Coleman, J., Poel, W., Daniels, B. C., Romanczuk, P., & Couzin, I. D. (2019). Individual and collective encoding of risk in animal groups. Proceedings of the National Academy of Sciences, 116(41), 20556–20561. https://doi.org/10.1073/pnas.1905585116
Wall, V., & Brock, O. (2019). Multi-Task Sensorization of Soft Actuators Using Prior Knowledge. 2019 International Conference on Robotics and Automation (ICRA), 9416–9421. https://doi.org/10.1109/ICRA.2019.8793697

Research

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