UIUC Quant Brownbag

Cognitive architecture models can provide a high-level, whole-brain account of cognition, but have not seen wide use in the neuroscience research community due to a perceived lack of connection between the theoretical model elements and the biological reality of the brain. Recent work has tried to bridge this gap by using the structural configuration of architecture models, specifically the Common Model of Cognition (CMC), as a framework for Dynamic Causal Modeling (DCM) analyses of large scale fMRI data, both during tasks and at rest. The high-level framework proposed by the CMC produces higher quality predictions of human brain data than several plausible alternatives, suggesting both the presence of a consistent underlying structure of cognition in the brain as well as a sense of its shape and dynamics. These results demonstrate how the analysis of brain signaling data can benefit from the addition of cognitive modeling theories, as well as showing how brain data can be used to validate, refine, and improve cognitive architecture frameworks. Combining these tools and approaches provides many challenges and opportunities for gaining a deeper understanding of the structure and mechanisms of cognition.