Daniel Brand

• Kognitive Modellierung
• Prädiktive Modellierung menschlichen Schlussfolgerns
• Syllogistisches Schließen
• Informationssysteme

• seit 02/2022: Wissenschaftlicher Mitarbeiter; TU Chemnitz; Professur Prädiktive Verhaltensanalyse
• 02/2021 - 07/2021: Wissenschaftlicher Mitarbeiter; Syddansk Universitet (SDU); Abteilung für Design und Kommunikation
• 07/2020 - 02/2022: Wissenschaftlicher Mitarbeiter; Albert-Ludwigs-Universität Freiburg; Cognitive Computation Lab
• 05/2017 - 06/2020: Wissenschaftlicher Mitarbeiter; Albert-Ludwigs-Universität Freiburg; Center for Cognitive Science

• 2016: MSc. Computer Science, Albert-Ludwigs-Universität Freiburg
• 2012: BSc. Computer Science, Albert-Ludwigs-Universität Freiburg

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

• FADEp. Intentionales Vergessen und Änderungen in Arbeitsprozessen: Ein prozesskonditional-orientierter Ansatz im Verwaltungs- und IT Kontext. [Website]
• CCOBRA (Cognitive COmputation for Behavioral Reasoning Analysis) Framework: Online predictive modelling of human reasoning. [Website] [GitHub]
• Freiraum 2022: MeMo: Stärkung der Metakognition und Motivation Studierender durch individualisierte Smart Personal Assistants [Website]

• Brand, D., & Ragni, M. (2023). Effect of Response Format on Syllogistic Reasoning. In Proceedings of the 45th Annual Meeting of the Cognitive Science Society. [PDF] [GitHub]
• Todorovikj, S., Brand, D., & Ragni, M. (2023). Preferred Mental Models in Syllogistic Reasoning. In Proceedings of the 21th International Conference on Cognitive Modeling. [PDF]
• Brand, D., Riesterer, N., & Ragni, M. (2023). Uncovering Iconic Patterns of Syllogistic Reasoning: A Clustering Analysis. In Proceedings of the 21th International Conference on Cognitive Modeling. [PDF]
• Dames, H., Brand, D., & Ragni, M. (2022). Evidence for Multiple Mechanisms Underlying List-Method Directed Forgetting. In Proceedings of the 44th Annual Meeting of the Cognitive Science Society.
• 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 Proceedings of the 20th International Conference on Cognitive Modeling. [PDF] [GitHub]
• Todorovikj, S., Brand, D., & Ragni, M. (2022). Predicting Algorithmic Complexity for Individuals. In Proceedings of the 20th International Conference on Cognitive Modeling. [PDF]
• Kettner, F., Heinrich, E., Brand, D., & Ragni, M. (2022). Reverse-Engineering of Boolean Concepts: A Benchmark Analysis. In Proceedings of the 20th International Conference on Cognitive Modeling. [PDF]
• Brand, D., Dames, H., Puricelli, L., & Ragni, M. (2022). Rule-Based Categorization: Measuring the Cognitive Costs of Intentional Rule Updating. In Proceedings of the 44th Annual Meeting of the Cognitive Science Society. [PDF] [GitHub]
• Brand, D., Mittenbühler, M., & Ragni, M. (2022). Generalizing Syllogistic Reasoning: Extending Syllogisms to General Quantifiers. In Proceedings of the 44th Annual Meeting of the Cognitive Science Society. [PDF] [GitHub]
• Todorovikj, S., Kettner, F., Brand, D., Beggiato, M., & Ragni, M. (2022). Predicting Individual Discomfort in Autonomous Driving. In Proceedings of the 44th Annual Meeting of the Cognitive Science Society.
• von Stülpnagel, R., Findler, F., & Brand, D. (2022). Census-Based Variables Are Informative about Subjective Neighborhood Relations, but Only When Adjusted for Residents’ Neighborhood Conceptions. Sustainability, 14(8), 4434. doi: 10.3390/su14084434.
• Ragni, M., Brand, D., & Riesterer, N. (2021). The Predictive Power of Spatial Relational Reasoning Models: A New Evaluation Approach. Frontiers in Psychology, 12. doi: 10.3389/fpsyg.2021.626292. [GitHub]
• Brand, D., Riesterer, N., and Ragni, M. (2021). Unifying models for belief and syllogistic reasoning. In Proceedings of the 43th Annual Meeting of the Cognitive Science Society, eds. T. Fitch, C. Lamm, H. Leder, and K. T. mar Raible. [PDF] [GitHub]
• Brand, D., Riesterer, N., & Ragni, M. (2021). Model-Based Explanation of Feedback Effects in Syllogistic Reasoning. In Proceedings of the 19th International Conference on Cognitive Modeling. [GitHub]
• Mannhardt, J., Bucher, L., Brand, D., & Ragni, M. (2021). Predicting spatial belief reasoning: comparing cognitive and AI models. In Proceedings of the 19th International Conference on Cognitive Modeling. [PDF]
• Riesterer, N., Brand, D., & Ragni, M. (2020). Feedback Influences Syllogistic Strategy: An Analysis based on Joint Nonnegative Matrix Factorization. In Proceedings of the 18th International Conference on Cognitive Modeling. [PDF] [GitHub]
• Brand, D., Riesterer, N., & Ragni, M. (2020). Extending TransSet: An Individualized Model for Human Syllogistic Reasoning. In Proceedings of the 18th International Conference on Cognitive Modeling. [PDF] [GitHub]
• Riesterer, N., Brand, D., & Ragni, M. (2020). Do Models Capture Individuals? Evaluating Parameterized Models for Syllogistic Reasoning. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 3377-3383). Cognitive Science Society. [PDF] [GitHub]
• Brand, D., Riesterer, N., Dames, H., & Ragni, M. (2020). Analyzing the Differences in Human Reasoning via Joint Nonnegative Matrix Factorization. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 3254-3260). Cognitive Science Society. [PDF] [GitHub]
• Riesterer, N., Brand, D., & Ragni, M. (2020). Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance. Topics in Cognitive Science. doi: 10.1111/tops.12501. [GitHub]
• Riesterer, N., Brand, D., Dames, H., & Ragni, M. (2020). Modeling Human Syllogistic Reasoning: The Role of "No Valid Conclusion". Topics in Cognitive Science, 12(1), 446-459. doi: 10.1111/tops.12487. [GitHub]
• von Stülpnagel, R., Brand, D., & Seemann, A. K. (2019). Your neighbourhood is not a circle, and you are not its centre. Journal of Environmental Psychology, 66, 101349. doi: 10.1016/j.jenvp.2019.101349.
• Brand, D., Riesterer, N., & Ragni, M. (2019). On the Matter of Aggregate Models for Syllogistic Reasoning: A Transitive Set-Based Account for Predicting the Population. In Stewart T. (Ed.), Proceedings of the 17th International Conference on Cognitive Modeling. [PDF] [GitHub]
• Riesterer, N., Brand, D., & Ragni, M. (2019). Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance. In Stewart T. (Ed.), Proceedings of the 17th International Conference on Cognitive Modeling. [PDF] [GitHub]
• Riesterer, N., Brand, D., Dames, H., & Ragni, M. (2019). Modeling Human Syllogistic Reasoning: The Role of "No Valid Conclusion", In Goel, A., Seifert, C., & Freska, C. (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society. [PDF] [GitHub]
• Ragni, M., Dames, H., Brand, D., & Riesterer, N. (2019). When Does a Reasoner Respond: Nothing Follows?, In Goel, A., Seifert, C., & Freska, C. (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society.
• Riesterer N., Brand D., Ragni M. (2018) The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning. In: Trollmann F., Turhan AY. (Eds.) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science, vol 11117. Springer, Cham. [Poster] [GitHub]
• Riesterer, N., Brand, D., & Ragni, M. (2018) A Machine Learning Approach for Syllogistic Reasoning. In C. Rothkopf et al. (Eds.), Proceedings of the 14th Biannual Conference of the German Society for Cognitive Science (p. 54). [Poster]

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weniger anzeigen

• "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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