M.Sc. Daniel Brand

Inhalt meiner Forschung ist die Erforschung und Modellierung menschlicher Denkprozesse, insbesondere in Bezug auf Inferenzprozesse und Problemlösen. Hierbei ist meine Forschung methodisch vielseitig und liegt zwischen Kognitionswissenschaft und kognitiver Modellierung auf der einen Seite, sowie Data Science, Informationssystemen und künstlicher Intelligenz auf der anderen Seite. Zielsetzung ist hierbei, unabhängig von der jeweiligen Methode, Erkenntnisgewinn und aktuelle Erkenntnisse mittels prädiktiver computationaler Modelle zugänglich und anwendbar zu machen.
Folgende Bereiche liegen hierbei im Schwerpunkt:

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

• Brand, D., & Ragni, M. (2025). Using Cross-Domain Data to Predict Syllogistic Reasoning Behavior. In Barner, D., Bramley, N.R., Ruggeri, A. and Walker, C.M. (Eds.), Proceedings of the 47th Annual Meeting of the Cognitive Science Society (pp. 4370-4377). [Link] [GitHub]
• Todorovikj, S., Brand, D., & Ragni, M. (2025). The Cognitive Complexity of Rule Changes. In Barner, D., Bramley, N.R., Ruggeri, A. and Walker, C.M. (Eds.), Proceedings of the 47th Annual Meeting of the Cognitive Science Society (pp. 461-467). [Link]
• Todorovikj, S., Brand, D., & Ragni, M. (In Press). Empirical Test of a Formal Framework of Forgetting. Proceedings of the 23rd International Conference on Cognitive Modeling.
• 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]
• Todorovikj, S., Brand, D., & Ragni, M. (2022). Predicting Algorithmic Complexity for Individuals. In T. C. Stewart (Ed.), Proceedings of the 20th International Conference on Cognitive Modeling (pp. 240–246). Applied Cognitive Science Lab, Penn State: University Park, PA. [PDF]
• Kettner, F., Heinrich, E., Brand, D., & Ragni, M. (2022). Reverse-Engineering of Boolean Concepts: A Benchmark Analysis. In T. C. Stewart (Ed.), Proceedings of the 20th International Conference on Cognitive Modeling (pp. 164–169). Applied Cognitive Science Lab, Penn State: University Park, PA. [PDF]
• Brand, D., Dames, H., Puricelli, L., & Ragni, M. (2022). Rule-Based Categorization: Measuring the Cognitive Costs of Intentional Rule Updating. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Meeting of the Cognitive Science Society (pp. 2810–2817). [PDF] [GitHub]
• Brand, D., Mittenbühler, M., & Ragni, M. (2022). Generalizing Syllogistic Reasoning: Extending Syllogisms to General Quantifiers. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Meeting of the Cognitive Science Society (pp. 722–728). [PDF] [GitHub]
• Todorovikj, S., Kettner, F., Brand, D., Beggiato, M., & Ragni, M. (2022). Predicting Individual Discomfort in Autonomous Driving. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Meeting of the Cognitive Science Society (pp. 3103–3109).
• 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 T. Fitch, H. Lamm, H. Leder, & K. Teßmar-Raible (Eds.), Proceedings of the 43th Annual Meeting of the Cognitive Science Society (pp. 2801–2807).[PDF] [GitHub]
• Brand, D., Riesterer, N., & Ragni, M. (2021). Model-Based Explanation of Feedback Effects in Syllogistic Reasoning. In T. C. Stewart (Ed.), Proceedings of the 19th International Conference on Cognitive Modeling (pp. 16–22). University Park, PA: Applied Cognitive Science Lab, Penn State. [GitHub]
• Mannhardt, J., Bucher, L., Brand, D., & Ragni, M. (2021). Predicting spatial belief reasoning: comparing cognitive and AI models. In T. C. Stewart (Ed.), Proceedings of the 19th International Conference on Cognitive Modeling (pp. 184–190). University Park, PA: Applied Cognitive Science Lab, Penn State. [PDF]
• Riesterer, N., Brand, D., & Ragni, M. (2020). Feedback Influences Syllogistic Strategy: An Analysis based on Joint Nonnegative Matrix Factorization. In T. C. Stewart (Ed.), Proceedings of the 18th International Conference on Cognitive Modeling (pp. 223–228). University Park, PA: Applied Cognitive Science Lab, Penn State. [PDF] [GitHub]
• Brand, D., Riesterer, N., & Ragni, M. (2020). Extending TransSet: An Individualized Model for Human Syllogistic Reasoning. In T. C. Stewart (Ed.), Proceedings of the 18th International Conference on Cognitive Modeling (pp. 17–22). University Park, PA: Applied Cognitive Science Lab, Penn State. [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, 12(3), 960–974. 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 (pp. 5–10). Waterloo, Canada: University of Waterloo. [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 (pp. 178–183). Waterloo, Canada: University of Waterloo. [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 (pp. 953–959). Montreal, QB: 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 (pp. 2640–2646). Montreal, QB: Cognitive Science Society.
• Riesterer N., Brand D., Ragni M. (2018) The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning. In F. Trollmann & A.-Y. Turhan (Eds.), KI 2018: Advances in Artificial Intelligence. KI 2018. (Vol. 11117, pp. 415–421). Cham: Springer International Publishing. doi: 10.1007/978-3-030-00111-7_35. [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]
• Kottlors, J., Brand, D., & Ragni, M. (2012). Modeling behavior of attention-deficit-disorder patients in a N-back task. In N. Rußwinkel, U. Drewitz, & H. van Rijn (Eds.), Proceedings of 11th international conference on cognitive modeling (pp. 297–302). Berlin: Universitätsverlag der TU Berlin.

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