Anna Lange
Photo: SCIoI
Anna possesses a diverse academic background that spans multiple fields. She obtained her Bachelor of Science degree in Mathematics with Psychology from Queen Mary University of London. She further pursued her academic journey by completing a Master of Science degree in Computational Neuroscience at the Bernstein Center for Computational Neuroscience Berlin. During her Master’s program, Anna actively engaged in research at Charite-Universitätsmedizin Berlin, where her work revolved around investigating Human-Robot Interactions using functional Magnetic Resonance Imaging (fMRI). In parallel, she also contributed to the development of neural networks inspired by human psychology for Robot-Robot Interactions at Humboldt-Universität zu Berlin. Both of these positions were part of her involvement in SCIoI Project 09. She is currently part of the Adaptive Systems group at Humboldt-Universität zu Berlin under the supervision of Professor Verena Hafner. At SCIoI, Anna is working as a PhD for the Project 50. Her current research revolves around enhancing adaptive learning and teaching strategies in robots during social interactions. She achieves this by creating adaptive neural networks grounded in the principles of human psychology.
Projects
Anna Lange is member of:
Publications +
6984777
Lange
1
apa
50
date
desc
year
19923
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Ackermann, H., Lange, A. L., Dumont, H., Hafner, V. V., & Lazarides, R. (2026). Real-time cognitive-affective dynamics of failure feedback in a technology-based learning task. Communications Psychology, 4(1), 95. https://doi.org/10.1038/s44271-026-00487-8
Ackermann, H., Lange, A. L., Hafner, V. V., & Lazarides, R. (2025). How adaptive social robots influence cognitive, emotional, and self-regulated learning. Scientific Reports, 15(1), 6581. https://doi.org/10.1038/s41598-025-91236-0
Taliaronak, V., Lange, A. L., Kirtay, M., Oztop, E., & Hafner, V. V. (2023). Advancing Humanoid Robots for Social Integration: Evaluating Trustworthiness Through a Social Cognitive Framework. 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2112–2119. https://doi.org/10.1109/RO-MAN57019.2023.10309519
Gómez-Nava, L., Lange, R. T., Klamser, P. P., Lukas, J., Arias-Rodriguez, L., Bierbach, D., Krause, J., Sprekeler, H., & Romanczuk, P. (2023). Fish shoals resemble a stochastic excitable system driven by environmental perturbations. Nature Physics, 19(5), 663–669. https://doi.org/10.1038/s41567-022-01916-1
Lange, R. T., & Sprekeler, H. (2022). Learning Not to Learn: Nature versus Nurture In Silico. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7290–7299. https://doi.org/10.1609/aaai.v36i7.20691
Vischer, M., Lange, R. T., & Sprekeler, H. (2022). On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning. International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2105.01648
Pischedda, D., Lange, A., Kirtay, M., Wudarczyk, O., 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. [Poster]. 5th Virtual Social Interactions (VSI) Conference.
Pischedda, D., Lange, A., Kirtay, M., Wudarczyk, O., 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 [Poster]. Annual Meeting of the Society for Neuroscience (SfN).
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