Individual differences in negative affect are captured by a relatively stable, transdiagnostic dimension known as dispositional negativity, which can be decomposed into two correlated subcomponents: anxious distress (AD; reflecting tendencies toward sadness and anxiety) and irritable distress (ID; reflecting tendencies toward frustration and anger). The goal of this proposal is to provide training in behavioral experiments, computational modeling, and functional magnetic resonance imaging (fMRI) methods that can be used to interrogate the learning processes that underlie the subcomponents of dispositional negativity. Understanding the neurocomputational basis of dispositional negativity is of central importance because it contributes to nearly all forms of psychopathology and is strongly related to clinical prognosis, functional impairment, and economic burden. The central hypothesis of the current proposal is that elevations in dispositional negativity reflect a predominance of an inflexible Pavlovian learning system over instrumental behavioral control in aversive contexts, with AD reflecting a Pavlovian bias to engage in passive avoidance, and ID reflecting a Pavlovian bias to engage in active defense. Specifically, the aims of this project are to 1) demonstrate that passive and active Pavlovian biases are differentially associated with individual differences in AD and ID; 2) characterize the neural circuitry underlying AD, ID, and their associated Pavlovian biases; and 3) show that biases toward active and passive defense are associated with common real-world correlates of AD and ID. Consistent with a transdiagnostic, dimensional approach informed by the Research Domain Criteria (RDoC), 200 adults representing the full spectrum of dispositional negativity and its subcomponents will complete a series of behavioral paradigms that manipulate the influence of the Pavlovian learning system on instrumental behavior in an aversive context, including a novel aversive Pavlovian-Instrumental Transfer (PIT) task. A subsample of 70 participants will complete the aversive PIT task while undergoing fMRI. Behavior will be analyzed using frequentist multi-level models and reinforcement learning (RL) models that formally quantify Pavlovian influence within a Bayesian decision theory framework. RL model-estimated prediction errors will be regressed against BOLD signal, and structural equation modeling will be used to link AD, ID, and their associated Pavlovian biases to real-world outcomes. The proposed training plan leverages a world-class research environment with a team of highly skilled mentors and consultants to provide the candidate with training in experimental learning paradigms, computational modeling, and functional neuroimaging methods. In line with NIMH’s Strategic Objectives, the proposed work will describe the neural circuitry associated with complex forms of learned defensive behavior, enable the development of clinically useful behavioral and b...