The ACT-R cognitive architecture allows high level simulations of human mental processes during the execution of various tasks. So far a small number of models exist, that simulate the working of the mind when it is solving Raven's progressive matrices intelligence tests. Some of these models use the ACT-R architecture. The existing ACT-R models are incapable of solving all the standardized Raven's progressive matrices tests, even when removing human limitations such as forgetting. We created a model based on the work of Ragni et al. (2012), with which we tried to understand the problems associated with solving these tests. We try to determine whether the difficulties in solving certain tests stem from the limitations of the cognitive architecture, or are perhaps avoidable with modifications of the model. We find that there is a trade-off between a model's performance at solving tests, its accuracy as a simulation of the mind, and the level of its complexity.