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There has been much research in recent decades aimed at discovering what the underlying principles are, if any, that drive the brain. As the cortex appears to be basically uniform, it seems that if there is an underlying principle, it is ubiquitous. However, the principles which have been proposed to explain the brain have largely been specialized principles, which each explain a particular aspect of the brain. Principles such as efficient coding, predictive coding, and temporal invariance have been proposed to explain sensory coding, and have succeeded to some measure in reproducing the receptive field properties of neurons in the visual cortex. Bayesian surprise has been offered as an explanation of attention, and has enjoyed some success in modeling human saccades, while reinforcement learning and intelligent adaptive curiosity have been aimed at explaining how actions are chosen. In this dissertation we propose a novel principle which we call predictive action. It is an information theoretic principle which unifies all of the above proposals. We show its relationship to each of the above proposals, and give several algorithms which approximate predictive action for specific environments. We hope that this principle will allow not only for a greater understanding of the brain, but also serve as a principled basis for the design of future algorithms to solve a broad range of problems in artificial intelligence.