Inferring a cognitive architecture from multi-task neuroimaging data: A data-driven test of the common model of cognition using granger causality
A common complaint levied at analyses based on cognitive architectures is their lack of connection to observed functional neuroimaging data, particularly for architectural models that rely on high level, theoretical components of cognition. Previous work has connected task-based functional MRI data to the Common Model of Cognition (CMC), using a top-down modeling approach. Here, a bottom-up method, Granger Causality Modeling (GCM), is applied to the same task-based data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model.