Leveraging large-scale brain connectivity data to explore and expand the common model of cognition

Over the past decades, a vast amount of models and architectures have been developed, looking at the large scale organization of the human brain on different levels of abstraction. In an attempt to synthesize the ideas from some of the most established existing models of cognitive processing, namely ACT-R, SOAR, and Sigma, the Common Model of Cognition (CMC) has been proposed. It identifies five different modules within the brain with discrete functionalites and processing connections between them, modules for Perception, Action, Long-Term Memory, Procedural Memory, as well as Working Memory. These are considered to be essential for cognition across different domains and tasks, representing a generalized model of the structuring and processing of the mind. Previous work has connected the structure of the CMC to activity in the specific brain regions, helping to validate the model and compare it to other models and architectures, like Hub-and-Spoke Architectures and Hierarchical Architectures. The CMC was found to outperform its alternatives, being a significantly better match for the experimentally gained data. However, the results also suggested that modifications to the original formulation of the CMC would improve its fit. This is not surprising, as the CMC has a rather basic structure, only incorporating high level cognitive components. Other models consist of larger networks of sub-components, representing real human cognition more accurately. It further does not consider many significant aspects of cognitive processing like metacognition or emotional processing in the modularity and organization. The large scale parcellation currently used to identify signals associated with each cognitive component will not be sufficient in the future, as the model grows in complexity and additional cognitive components are incorporated. Better methods are needed for identifying regions associated with specific cognitive processes and modeling these and its connections in the CMC. To improve the identification of brain regions we can use meta-analyses of brain data. Tools like Neurosynth synthesize the results of many studies using neuroimaging, allowing to perform connectivity analyses on them. This makes it possible to relate specific brain regions to functions, as well as investigate the interactions between the different regions, which can be leveraged to inform the CMC about its structure. Due to the large amount of data and the wide variety of domains covered, meta-analyses of brain data are significantly more powerful than single studies. To validate our methods, we can use fMRI brain data from the Human Connectome Project. It provides a wide range of brain activity across multiple tasks allowing us to compare different configurations of the CMC using methods of connectivity analysis. We propose leveraging the power of connectivity analyses with both large-scale fMRI brain data and meta-analyses of brain data to create expanded and more robust versions of the CMC. The methodology used to research this is defined as follows: First, look at shortcomings of the current CMC structure and create expanded versions with additional components integrated in a plausible way. Then identify and isolate brain activity associated with those components using the proposed combination of meta-analyses and fMRI brain data. Finally, compare the resulting predictions with the current CMC structure.