Anterior cingulate cortex is one of the largest riddles in cognitive neuroscience and presents a major challenge to mental health research. ACC dysfunction contributes to a wide spectrum of psychiatric and neurological disorders but no one knows what it actually does. Although more than a thousand papers are published about it each year, attempts to identify its function have been confounded by the fact that a multiplicity of tasks and events activate ACC, as if it were involved in everything. Recently, I proposed a theory that reconciles many of the complexities surrounding ACC. This holds that ACC selects and motivates high-level, temporally extended behaviors according to principles of hierarchical reinforcement learning. For example, on this view ACC would be responsible for initiating and sustaining a run up a steep mountain. I have instantiated this theory in two computational models that make explicit the theory's assumptions, while yielding testable predictions. In this project I will integrate the two computational models into a unified, biologically-realistic model of ACC function, which will be evaluated using mathematical techniques from non-linear dynamical systems analysis. I will then systematically test the unified model in a series of experiments involving functional magnetic resonance imaging, electroencephalography and psychopharmacology, in both healthy human subjects and patients. The establishment of a complete, formal account of ACC will fill an important gap in the cognitive neuroscience of cognitive control and decision making, strongly impact clinical practice, and be important for artificial intelligence and robotics research, which draws inspiration from brain-based mechanisms for cognitive control. The computational modelling work will also link high level, abstract processes associated with hierarchical reinforcement learning with low level cellular mechanisms, enabling the theory to be tested in animal models.