The ability to switch between tasks is a core component of adaptive cognition, yet a mechanistic understanding of this capacity has remained elusive. In particular, there are longstanding debates over the extent to which task-switching is primarily influenced by interference from previous tasks or active preparation for upcoming tasks. We advance this debate by modeling the evolution of task representations in human electroencephalographic recordings as linear dynamical systems over a high-dimensional latent space. Using control theoretic analyses of dynamical systems fit to both brains and task-optimized neural networks, we reveal neural signatures of active preparation that reconcile the role of interference and control in task switching. These findings inform a core debate in cognitive control and offer a promising new paradigm for human neuroimaging analysis.