Studying aversive behaviour is critical for understanding negative emotions and associated psychopathologies. However a comprehensive picture of the mechanisms underlying aversion is lacking, with associative learning theories focusing on Pavlovian reactions and decision-making theoretic approaches on prospective functions. We propose a computational model of aversion that combines goal-directed and Pavlovian forms of control into a unifying framework in which their relative importance is regulated by factors such as threat distance and controllability. Using simulations, we test whether the model can reproduce available empirical findings and discuss its relevance to understanding factors underlying negative emotions such as fear and anxiety. Furthermore, the specific method used to construct the model permits a natural mapping from its components to brain structure and function. Our model provides a basis for a unifying account of aversion that can guide empirical and interventional study contexts.