Many theories of decision-making consider pain, monetary loss, and other forms of punishment to be interchangeable quantities that are processed by the same neural system. For example, standard reinforcement learning models utilize a single reinforcement term to represent both monetary losses and pain signals. By contrast, I propose that 1) pain signals present unique computational challenges, 2) these challenges are addressed in humans and other animals by anterior cingulate cortex (ACC), and 3) pain is regulated by cognitive control during goal-directed tasks, using principles of the hierarchical reinforcement learning model of the ACC (HRL-ACC). To show this, I conducted 3 studies. In Study 1, I conducted an electrophysiological study to investigate the effect of task goals on event-related brain potentials (ERPs) during conditions where pain and reward are used. Specifically, I investigated whether feedback stimuli predicting forthcoming pain would elicit the reward positivity, an ERP component that is more positive-going to positive feedback than to negative feedback, when the goal of the task is to find electrical shocks. Contrary to my predictions, a standard reward positivity was not elicited by pain feedback in this task. In Study 2, I conducted three behavioral experiments wherein the subjective costs of mild electrical shocks were equated with monetary losses for each individual participant using a calibration procedure. I hypothesized that decision-making behavior in face of painful events and decision making behavior in the face of monetary losses would be different from each other despite the outcomes (pain vs. monetary loss) being equated for their subjective costs. This prediction was confirmed, demonstrating that the costs associated with pain and monetary losses differ in more than just magnitude. In Study 3, to explain these results, I developed an extension to an existing computational framework, the HRL-ACC model. The present model provides insight into choice behaviour in the pain and monetary loss (ML) conditions by showing that cognitive control levels converge to an average level across trials. In the pain condition, cognitive control fluctuates from trial to trial in a systematic fashion, causing trials with low shock levels to be over-valued and shocks with high-shock levels to be undervalued. By contrast, in the ML condition cognitive wanes across trials because it is not needed and the model displays normative behavior. These findings are in line with psychological approaches to pain treatment and provide neuro-cognitive explanations that underlie their mechanisms. In line with the HRL-ACC theory, I propose that the ACC regulates pain by motivating good performance in the face of physical punishments (but not monetary losses) in order to achieve long-term goals that are produced by ACC.