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.

A functional magnetic resonance imaging (fMRI) study was performed in which participants performed a complex series of mental calculations that spanned about 2 min. An Adaptive Control of Thought—Rational (ACT-R) model [Anderson, J. R. How can the human mind occur in the physical universe? New York: Oxford University Press, 2007] was developed that successfully fit the distribution of latencies. This model generated predictions for the fMRI signal in six brain regions that have been associated with modules in the ACT-R theory. The model's predictions were confirmed for a fusiform region that reflects the visual module, for a prefrontal region that reflects the retrieval module, and for an anterior cingulate region that reflects the goal module. In addition, the only significant deviations to the motor region that reflects the manual module were anticipatory hand movements. In contrast, the predictions were relatively poor for a parietal region that reflects an imaginal module and for a caudate region that reflects the procedural module. Possible explanations of these poor fits are discussed. In addition, exploratory analyses were performed to find regions that might correspond to the predictions of the modules.

This research tests a model of the computational role of three cortical regions in tasks like algebra equation solving. The model assumes that there is a left parietal region-of-interest (ROI) where the problem expression is represented and transformed, a left prefrontal ROI where information for solving the task is retrieved, and a motor ROI where hand movements to produce the answer are programmed. A functional magnetic resonance imaging (fMRI) study of an abstract symbolmanipulation task was performed to articulate the roles of these three regions. Participants learned to associate words with instructions for transforming strings of letters. The study manipulated the need to retrieve these instructions, the need to transform the strings, and whether there was a delay between calculation of the answer and the output of the answer. As predicted, the left parietal ROI mainly reflected the need for a transformation and the left prefrontal ROI the need for retrieval. Homologous right ROIs showed similar but weaker responses. Neither the prefrontal nor the parietal ROIs responded to delay, but the motor ROI did respond to delay, implying motor rehearsal over the delay. Except for the motor ROI, these patterns of activity did not vary with response hand. In an ACT-R model, it was shown that the activity of an imaginal buffer predicted the blood oxygen level-dependent (BOLD) response of the parietal ROI, the activity of a retrieval buffer predicted the response of the prefrontal ROI, and the activity of a manual buffer predicted the response of the motor ROI.