Cognitive control allows us to think and behave flexibly based on our context and goals. Most theories of cognitive control propose a control representation that enables the same input to produce different outputs contingent on contextual factors. In this talk, I will focus on an important property of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. This tradeoff has important implications for cognitive control. In this talk, I will present initial evidence from fMRI and EEG showing that task representations in the human brain leverage both ends of this tradeoff during flexible behavior.