To survive in complex environments, animals need to have mechanisms to select effective actions quickly, with minimal computational costs. As perhaps the computationally most parsimonious of these systems, Pavlovian control accomplishes this by hardwiring specific stereotyped responses to certain classes of stimuli. It is well documented that appetitive cues initiate a Pavlovian bias toward vigorous approach; however, Pavlovian responses to aversive stimuli are less well understood. Gaining a deeper understanding of aversive Pavlovian responses, such as active avoidance, is important given the critical role these behaviors play in several psychiatric conditions. The goal of the current study was to establish a behavioral and computational framework to examine aversive Pavlovian responses (activation vs. inhibition) depending on the proximity of an aversive state (escape vs. avoidance). We introduce a novel task in which participants are exposed to primary aversive (noise) stimuli and characterized behavior using a novel generative computational model. This model combines reinforcement learning and drift-diffusion models so as to capture effects of invigoration/inhibition in both explicit choice behavior as well as changes in RT. Choice and RT results both suggest that escape is associated with a bias for vigorous action, whereas avoidance is associated with behavioral inhibition. These results lay a foundation for future work seeking insights into typical and atypical aversive Pavlovian responses involved in psychiatric disorders, allowing us to quantify both implicit and explicit indices of vigorous choice behavior in the context of aversion.