TY - JOUR A1 - Said, Nadia A1 - Engelhart, Michael A1 - Kirches, Christian A1 - Körkel, Stefan A1 - Holt, Daniel V. T1 - Applying mathematical optimization methods to an ACT-R instance-based learning model JF - PLoS ONE N2 - Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods. Y1 - 2016 U6 - https://doi.org/10.1371/journal.pone.0158832 VL - 11 IS - 7 PB - PLOS ER - TY - CHAP A1 - Said, Nadia A1 - Engelhart, Michael A1 - Kirches, Christian A1 - Körkel, Stefan A1 - Holt, Daniel V. ED - Taatgen, Niels A. ED - Van Vugt, Marieke K. ED - Borst, Jelmer P. ED - Mehlhorn, Katja T1 - Mathematical formalization and optimization of an ACT-R instance-based learning model T2 - Proceedings of ICCM 2015 - 13th International Conference on Cognitive Modeling, 09-11.04.2015, Groningen N2 - The performance of cognitive models often depends on the settings of specific model parameters, such as the rate of memory decay or the speed of motor responses. The systematic exploration of a model’s parameter space can yield relevant insights into model behavior and can also be used to improve the fit of a model to human data. However, exhaustive parameter space searches quickly run into a combinatorial explosion as the number of parameters investigated increases. Taking an established instance-based learning task as example, we show how simulation using parallel computing and derivative-free optimization methods can be applied to investigate the effects of different parameter settings. We find that both global optimization methods involving genetic algorithms as well as local methods yield satisfactory results in this case. Furthermore, we show how a model implemented in a specific cognitive architecture (ACT-R) can be mathematically reformulated to prepare the application of derivative-based optimization methods which promise further efficiency gains for quantitative analysis. KW - cognitive modeling KW - ACT-R KW - instance-based learning KW - discrete-time system KW - derivative-free optimization KW - parameter identification Y1 - 2015 UR - http://www.cognitive-modeling.com/proceedings/papers/0005/paper0005.pdf SN - 978-90-367-7763-6 SP - 25 EP - 30 PB - University of Groningen ER -