Daniel Brand

• Cognitive Modeling
• Predictive Modelling of Human Reasoning
• Syllogistic Reasoning
• Information Systems

• since 02/2022: Research Assistant, TU Chemnitz, Professorship of Predictive Analytics
• 02/2021 - 07/2021: Research Assistant, University of Southern Denmark, Department of Design and Communication
• 07/2020 - 02/2022: Research Assistant, University of Freiburg, Cognitive Computation Lab
• 05/2017 - 06/2020: Research Assistant, University of Freiburg, Center for Cognitive Science

• 2016: MSc. Computer Science, University of Freiburg
• 2012: BSc. Computer Science, University of Freiburg

• SS 2022: Lecturer, Seminar: Cognitive Ergonomics, TU Chemnitz
• SS 2021: Assistant, Seminar: Cognitive Modeling, Cognitive Computation Lab, University of Freiburg
• SS 2020: Assistant, Seminar: Cognitive Modeling, Cognitive Computation Lab, University of Freiburg
• WS 2019/20: Assistant, Seminar: Cognitive Reasoning: Methods, Algorithms, and Statistics to Discern Human from Artificially Generated Data, Cognitive Computation Lab, University of Freiburg
• SS 2019: Assistant, Seminar: Cross-Domain Modeling of Human Cognition, Cognitive Computation Lab, University of Freiburg
• SS 2014: Tutor, Cloud Computing, Department for Databases and Information Systems, University of Freiburg
• SS 2012: Tutor, Software Engineering, Department for Software Engineering, University of Freiburg

• Automatische Prozessmodellgenerierung für Kognitive Modellierung [Automatic process model generation for cognitive modeling]

• FADEp. Intentionales Vergessen und Änderungen in Arbeitsprozessen: Ein prozesskonditional-orientierter Ansatz im Verwaltungs- und IT Kontext. [Website]
• Freiraum 2022: MeMo: Stärkung der Metakognition und Motivation Studierender durch individualisierte Smart Personal Assistants [Website]

• CCOBRA (Cognitive COmputation for Behavioral Reasoning Analysis) Framework: Online predictive modelling of human reasoning. [Website] [GitHub]
• PVA Webexperiment Tools: Collection of existing tasks and templates aiming to make the development of psychological web-experiments easier. [GitHub]
• Syllogistic Task Predictor: Online interactive prediction environment for syllogistic reasoning [Website]
• pyTailorshop: Python-based implementation of the Tailorshop simulation[GitHub]

• Brand, D., Todorovikj, S., & Ragni, M. (2024). Predicting complex problem solving performance in the tailorshop scenario. In Sibert, C. (Ed.), Proceedings of the 22th International Conference on Cognitive Modeling (pp. 30–36). University Park, PA: Applied Cognitive Science Lab, Penn State. [PDF] [GitHub]
• Todorovikj, S., Brand, D., & Ragni, M. (2024). Model verification and preferred mental models in syllogistic reasoning. In Sibert, C. (Ed.), Proceedings of the 22th International Conference on Cognitive Modeling (pp. 185–191). University Park, PA: Applied Cognitive Science Lab, Penn State. [PDF]
• Brand, D., Todorovikj, S., & Ragni, M. (2024). Necessity, Possibility and Likelihood in Syllogistic Reasoning. In L. K. Samuelson, S. L. Frank, M. Toneva, A. Mackey, & E. Hazeltine (Eds.), Proceedings of the 46th Annual Meeting of the Cognitive Science Society (pp. 2776–2782). [Link] [GitHub]
• Brand, D., & Ragni, M. (2023). Effect of Response Format on Syllogistic Reasoning. In M. Goldwater, F. K. Anggoro, B. K. Hayes, & D. C. Ong (Eds.), Proceedings of the 45th Annual Meeting of the Cognitive Science Society (pp. 2408–2414). [PDF] [GitHub]
• Todorovikj, S., Brand, D., & Ragni, M. (2023). Preferred Mental Models in Syllogistic Reasoning. In C. Sibert (Ed.), Proceedings of the 21th International Conference on Cognitive Modeling (pp. 252–258). University Park, PA: Applied Cognitive Science Lab, Penn State. [PDF]
• Brand, D., Riesterer, N., & Ragni, M. (2023). Uncovering Iconic Patterns of Syllogistic Reasoning: A Clustering Analysis. In C. Sibert (Ed.), Proceedings of the 21th International Conference on Cognitive Modeling (pp. 57–63). University Park, PA: Applied Cognitive Science Lab, Penn State. [PDF]
• Dames, H., Brand, D., & Ragni, M. (2022). Evidence for Multiple Mechanisms Underlying List-Method Directed Forgetting. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Meeting of the Cognitive Science Society (pp. 519–525).
• Brand, D., Riesterer, N., & Ragni, M. (2022). Model-Based Explanation of Feedback Effects in Syllogistic Reasoning. Topics in Cognitive Science, 14(4), 828-844. doi: 10.1111/tops.12624. [GitHub]
• Mittenbühler, M., Brand, D., & Ragni, M. (2022). Do Models of Syllogistic Reasoning extend to Generalized Quantifiers?. In T. C. Stewart (Ed.), Proceedings of the 20th International Conference on Cognitive Modeling (pp. 189–195). Applied Cognitive Science Lab, Penn State: University Park, PA. [PDF] [GitHub]

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• "Effect of Response Format on Syllogistic Reasoning" @ CogSci 2023. Online, July 2023. [poster]
• "Uncovering iconic patterns of syllogistic reasoning: A clustering analysis" @ 21th International Conference on Cognitive Modeling. Online, July 2023. [slides]
• "Do models of syllogistic reasoning extend to generalized quantifiers?" @ 20th International Conference on Cognitive Modeling. Online, July 2022. [slides]
• "Model-based explanation of feedback effects in syllogistic reasoning" @ 19th International Conference on Cognitive Modeling. Online, July 2021. [talk]
• "Unifying models for belief and syllogistic reasoning" @ 43th Annual Meeting of the Cognitive Science Society. Online, July 2021. [slides] [poster]
• "How usable is Galaxy? A usability evaluation of Galaxy" @ 2019 Galaxy Community Conference (GCC2019). Freiburg, Germany, July 2019. [poster]
• "Extending TransSet: An Individualized Model for Human Syllogistic Reasoning" @ 18th International Conference on Cognitive Modeling. Online, July 2020. [short slides] [talk]

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• ORCID