Positioning. The article, published at the European Conference on Artificial Intelligence 2012, presents an ACT-R model that is able to solve Raven’s Matrix Test.
Research Question. Is it possible to explain the limitations in human reasoning in IQ- tests based on working memory limitations?
Method. Cognitive modeling; formal analysis
Results. The cognitive model in ACT-R, is able to solve analogously developed problems of Raven’s Standard and Advanced Progressive Matrices. The model solves 66 of the 72 tested problems of both tests. The model’s predicted error rates correlate to human performance with r = .8 for the Advanced Progressive Matrices and r = .7 for all problems together.
Ragni, M., & Neubert, S. (2012). Solving Raven’s IQ-tests: An AI and Cognitive Modeling Approach. In L. D. Raedt et al. (Eds.), Proceedings of the 20th European Conference on Artificial Intelligence (Vol. 242, pp. 666–671). Amsterdam: IOS Press.
Positioning. The article, published at the Proceedings of the 33rd Annual Conference of the Cognitive Science Society, develops an ACT-R model for the four problem classes for the Tower of London task mentioned in Kaller et al. (2011).
Method. Cognitive modeling; heuristic analysis
Results. The model can replicate the empirical results of Kaller et al. (2011) satisfactorily and introduces structural patterns for the first time. Additionally, representational aspects can be responsible for the used heuristics of the participants. This provides later the foundation for the more complex Rush Hour problem and another visual representation aspect: the Gestalt principles.
Albrecht, R., Brüssow, S., Kaller, C., & Ragni, M. (2011). Using a Cognitive Model for an In-Depth Analysis of the Tower of London. In L. Carlson, C. Hoelscher, & T. F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 693–698). Austin, TX: Cognitive Science Society.
Positioning. The article, published in Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence 2011, presents a reimplementation of the SRM in the cognitive architecture ACT-R by using the imaginal buffer as the location to construct mental models. The model is then tested on a previous experiment. This article presents together with Ragni et al. (2010) the foundation for formalizing the preferred mental model theory.
Research Question. Can we reproduce the empirical data of Ragni, Fangmeier, Webber, and Knauff (2007) with reaction times and error rates? Can we determine the influence of memory allocation strategies in problem solving and introduces an additional explanation pattern why some mental models are neglected?
Method. Formal analysis; cognitive modeling; empirical analysis
Results. The ACT-R model produces a high correlation between the empirical data and the model predictions.
Ragni, M., & Brüssow, S. (2011). Human spatial relational reasoning: Processing demands, representations, and cognitive model. In W. Burgard & D. Roth (Eds.), Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press.
Positioning. The article, published at the Cognitive Science Conference (2012), presents an ACT-R model for a driving task.
Method. Cognitive modeling
Results. The model is fully implemented in ACT-R 6.0 and extends a previous driver model proposed by Salvucci (2006).
Haring, K., Ragni, M., & Konieczny, L. (2012). Cognitive Model of Drivers Attention. In N. Rußwinkel, U. Drewitz, & H. van Rijn (Eds.), Proceedings of the 11th International Conference on Cognitive Modeling (pp. 275–280). Universitaetsverlag der TU Berlin.
Positioning. The article, published at the International Conference on Cognitive Modeling 2012, presents a cognitive ACT-R agent controlling a simple robot by the cognitive architecture ACT-R.
Research Question. Can we replicate human behavior in a navigation task by implementing ACT-R on a Lego MindStorm Robot?
Method. Cognitive Modeling
Results. The cognitive robotic system (Lego MindStorm Robot) shows a similar behavior to humans while navigating in a labyrinth. Especially the construction of a pseudo mental map (based on chunks) shows that many limitations of humans depend on the restriction of the working memory.
Bennati, S., & Ragni, M. (2012). Cognitive Robotics: Analysis of Preconditions and Implementation of a Cognitive Robotic System for Navigation Tasks. In N. Rußwinkel, U. Drewitz, & H. van Rijn (Eds.), Proceedings of the 11th International Conference on Cognitive Modeling (pp. 157–162). Berlin: Universitätsverlag der TU Berlin.