In this study, we used model-based functional MRI (fMRI) to locate two functions of the fronto-parietal network: declarative memory retrievals and updating of working memory. Because regions in the fronto-parietal network are by definition coherently active, locating functions within this network is difficult. To overcome this problem, we applied model-based fMRI, an analysis method that uses predictions of a computational model to inform the analysis. We applied model-based fMRI to five previously published datasets with associated computational cognitive models, and subsequently integrated the results in a meta-analysis. The meta-analysis showed that declarative memory retrievals correlated with activity in the inferior frontal gyrus and the anterior cingulate, whereas updating of working memory corresponded to activation in the inferior parietal lobule, as well as to activation around the inferior frontal gyrus and the anterior cingulate.I n this study, we used model-based functional MRI (fMRI) to locate two functions of the so-called "fronto-parietal network." The fronto-parietal network consists of brain areas that are coherently active and assumed to implement cognitive control functions: working memory, attentional selection, and error monitoring (1-5). It typically involves at least the dorsolateral prefrontal cortex, the anterior cingulate, and a region around the intraparietal sulcus. Because the regions in the fronto-parietal network are by definition active at the same time, distinguishing the precise functional characteristics of those regions is difficult with conventional fMRI methods. Model-based fMRI is particularly well suited for dissociating between highly correlated contributions to the blood oxygen level-dependent (BOLD) signal. We used model-based fMRI to locate two functions within the fronto-parietal network: declarative memory retrieval and updating of working memory. These functions are often assumed to be part of the fronto-parietal network, but the hypothesized locations within the network differ among studies (2, 6-11).Model-based fMRI is a relatively recent approach to analyzing fMRI data. Instead of using the condition structure of the experiment to inform the analysis, as in a conventional fMRI analysis, model-based fMRI uses information derived from a computational model (12, 13). For example, Daw et al. (14) fitted a mathematical reinforcement learning model to the behavior of their study participants and then used parameter values of the model as regressors in the fMRI analysis. This resulted in brain regions that correlated significantly with those parameter values, and thus with certain features of their model. Another recent study showed that modelbased fMRI can be used successfully in combination with more high-level information-processing models (15). By regressing the activity of model components (e.g., visual processing, declarative memory retrieval) against neural activity, the neural correlates of the model components can be located.To identify the neural correlates o...