This thesis presents a novel method of modelling metacognition computationally. Metacognition is commonly described as cognition acting on itself, and is correlated with enhanced performance in memory, emotional regulation, motor skills, and reasoning. How it produces these effects remains unclear. Understanding metacognition requires overcoming two main barriers: its high abstraction and disputed terminology. To overcome these obstacles, this thesis employs a cognitive architecture to define the base units of cognition, and how they come to act on themselves to form metacognitive processes. These computational forms of metacognition are then connected back to the research literature. Finally, these forms of metacognition are built into working models within the cognitive architecture ACT-R. These working models serve as evidence of their theoretical viability and functionality. The intention of this thesis is to help clarify the inner mechanisms of metacognition, and its implications for advancing a unified theory of metacognition.