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To examine how the brain produces behavior, new statistical methods have linked neurophysiological measures directly to mechanisms of cognitive models, modeling both modalities simultaneously. However, current simultaneous modeling efforts are largely based on either correlational methods or on functions that map one stream of data to the other. Such frameworks are limited in their ability to infer causality between brain activity and behavior, typically ignore important temporal dynamics of neural measures, or ignore large- and small-scale functional networks necessary for completing cognitive tasks. In this article, we investigate one causal framework for modeling brain dynamics as a potential alternative for explaining how behavior can be viewed as an emergent property of brain dynamics. Our proposed framework can be considered an extension of multivariate dynamical systems (MDS; Ryali et al …