Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

Previous research has found three brain regions for tracking components of the ACT-R cognitive architecture: a posterior parietal region that tracks changes in problem representation, a prefrontal region that tracks retrieval of task-relevant information, and a motor region that tracks the programming of manual responses. This prior research has used relatively simple tasks to incorporate a slow event-related procedure, allowing the blood oxygen level-dependent (BOLD) response to go back to baseline after each trial. The research described here attempts to extend these methods to tracking problem solving in a complex task, the Tower of Hanoi, which involves many complex steps of cognition and motor actions in rapid succession. By tracking the activation patterns in these regions, it is possible to predict with intermediate accuracy when participants are planning a future sequence of moves. The article describes a cognitive model in the ACT-R architecture that is capable of explaining both the latency data in move generation and the BOLD responses in these three regions.