Dispositional Negativity and the Pavlovian Control of Active and Passive Defensive Behavior

负面情绪的个体差异由一个相对稳定的、跨诊断的维度来捕获，称为 性格消极性，可以分解为两个相关的子成分：焦虑困扰（AD； 反映悲伤和焦虑的倾向）和烦躁不安（ID；反映 沮丧和愤怒）。该提案的目标是提供行为实验、计算方面的培训 建模和功能磁共振成像（fMRI）方法可用于询问 构成消极性格子成分的学习过程。了解 性格消极性的神经计算基础至关重要，因为它有助于几乎所有 精神病理学的多种形式，与临床预后、功能障碍和经济密切相关 负担。当前提案的中心假设是，性格消极性的升高反映了 不灵活的巴甫洛夫学习系统相对于厌恶性工具行为控制的优势 AD 反映了巴甫洛夫倾向进行被动回避，ID 反映了巴甫洛夫倾向 偏向于积极防御。具体来说，该项目的目标是 1）证明被动和 主动巴甫洛夫偏差与 AD 和 ID 的个体差异存在差异性相关； 2）表征 AD、ID 及其相关巴甫洛夫偏差背后的神经回路； 3）表明偏向于 主动防御和被动防御与 AD 和 ID 的常见现实世界关联相关。符合一个 根据研究领域标准 (RDoC) 的跨诊断、维度方法，200 名成年人 代表了性格消极性的全部范围及其子组成部分将完成一系列 操纵巴甫洛夫学习系统对工具行为影响的行为范式 在厌恶的背景下，包括新颖的厌恶巴甫洛夫工具转移（PIT）任务。的子样本 70 名参与者将在接受功能磁共振成像的同时完成厌恶性 PIT 任务。将使用以下方式分析行为 形式量化巴甫洛夫的频率论多层次模型和强化学习 (RL) 模型 贝叶斯决策理论框架内的影响。 RL模型估计的预测误差将会回归 针对 BOLD 信号，结构方程模型将用于链接 AD、ID 及其相关的 巴甫洛夫对现实世界结果的偏见。拟议的培训计划利用了世界一流的研究 拥有高技能导师和顾问团队的环境，为候选人提供培训 实验学习范式、计算模型和功能神经影像方法。符合 NIMH 的战略目标，拟议的工作将描述与复杂相关的神经回路 习得的防御行为的形式，能够发展临床上有用的行为和生物学 性格消极指数，并确定跨诊断干预的潜在目标。

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 biological indices of dispositional negativity, and identify potential targets for transdiagnostic interventions.