Computational Biases of Learning and Decision-Making in PTSD

PTSD 中学习和决策的计算偏差

• 批准号: 10425365
• 负责人: Joshua M Cisler
• 金额: $ 40.97万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10425365
• 关键词: Address; Adult; Anxiety Disorders; Behavior; Behavioral; Brain; Characteristics; Clinical; Computational Science; Computer Models; Corpus striatum structure; Data; Decision Making; Detection; Development; Disease; Disease remission; Distress; Extinction (Psychology); Fright; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Hippocampus (Brain); Individual; Interpersonal Violence; Intervention; Knowledge; Learning; Literature; Mediating; Mediator of activation protein; Memory; Mental disorders; Methods; Modeling; Morbidity - disease rate; Outcome; Participant; Pharmacology; Post-Traumatic Stress Disorders; Process; Psychiatry; Psychological reinforcement; Psychopathology; Psychophysiology; Quality of life; Research; Resolution; Retrieval; Rewards; Route; Stimulus; Structure; Symptoms; Techniques; Testing; Therapeutic; Trauma; Woman; Work; anxious; approach avoidance behavior; behavioral pharmacology; case control; clinically relevant; computational neuroscience; conditioned fear; conflict resolution; design; expectation; learning extinction; memory retrieval; men; neural circuit; novel; relating to nervous system; reward processing; social; stress related disorder; therapy development; trauma exposure; violence exposure

Project Summary The goal of this proposal is to characterize novel computational biases in learning and decision-making in PTSD. The dominant understanding of PTSD emphasizes heightened fear learning and threat detection, and weakened fear extinction / inhibition. While this model explains many aspects of PTSD, there is a growing gap between our models of PTSD and the emerging literature defining the normative computational mechanisms of learning and decision-making. This proposal aims to bridge this gap by defining computational biases in two clinically-relevant domains of learning and decision-making in PTSD. First, the mechanisms by which individuals with PTSD prefer avoiding threat at the expense of losing potential reward is not understood. This bias in approach-avoidance conflict resolution is an essential feature of the clinical presentation of PTSD, and though threat processing and reward processing have separately been characterized in PTSD, how threat and reward processing interact to result in biases towards avoidance has never been investigated. Second, dysregulation of context-depending (i.e., latent state) learning has clear clinical implications in PTSD: generalization of threat learning outside the trauma context is related to the development of PTSD; generalization of extinction learning outside of the clinical context is related to the treatment of PTSD. However, computational models of context-modulated learning have not been used to understand these processes in PTSD. The current project proposes to use computational modeling of learning and decision- making in novel tasks that probe the behavioral and brain mechanisms of approach-avoidance biases (Specific Aim 1) and context-modulated (i.e., latent state) learning (Specific Aim 2). A case-controlled design would be used, in which healthy adults, trauma-exposed adults without PTSD, trauma-exposed adults with PTSD, and adults with non-PTSD anxiety disorders would undergo novel learning and decision-making tasks during fMRI with concurrent psychophysiological assessment. By defining novel computational biases in learning and decision-making in PTSD, the project 1) would bridge the gap between our understanding of PTSD and our the growing science of computational mechanisms of learning, 2) has the potential to explain clinically-relevant features of dysfunction in PTSD, and 3) would provide targets for tracking trajectories of PTSD development and treatment, and stimulate novel methods for treating PTSD that go beyond the traditional fear conditioning and extinction models.

项目摘要 这项建议的目标是描述新的计算偏见在学习和决策中的特点， 创伤后应激障碍对PTSD的主流理解强调恐惧学习和威胁检测的增强， 恐惧消退/抑制减弱。虽然这个模型解释了创伤后应激障碍的许多方面，但差距越来越大 我们的创伤后应激障碍模型和新兴文献定义的规范计算机制之间的关系， 学习和决策。该提案旨在通过以下两个方面定义计算偏差来弥合这一差距： 临床相关领域的学习和决策的创伤后应激障碍。第一， PTSD患者更喜欢以失去潜在奖励为代价来避免威胁。这 在处理接近-回避冲突时的偏见是PTSD临床表现的一个基本特征， 虽然威胁处理和奖励处理在PTSD中被分别描述，但是威胁和奖励处理在PTSD中是如何被描述的？ 奖励处理相互作用，导致偏向回避从未被调查。第二、 环境依赖性的失调（即，潜在状态）学习在PTSD中具有明确的临床意义： 创伤情境外威胁学习的泛化与PTSD的发生有关; 临床背景之外的消退学习的泛化与PTSD的治疗有关。 然而，情境调节学习的计算模型还没有被用来理解这些 PTSD的过程。目前的项目建议使用学习和决策的计算建模， 在新的任务，探索行为和大脑机制的接近回避偏见（具体 目标1）和上下文调制（即，潜在状态）学习（具体目标2）。病例对照设计将是 使用，其中健康成人，创伤暴露成人无创伤后应激障碍，创伤暴露成人创伤后应激障碍，和 患有非PTSD焦虑症的成年人在功能磁共振成像中会经历新的学习和决策任务， 同时进行心理生理评估通过定义学习中的新计算偏差， 该项目将弥合我们对创伤后应激障碍的理解与我们对创伤后应激障碍的理解之间的差距。 不断发展的学习计算机制科学，2）有可能解释临床相关的 创伤后应激障碍的功能障碍的特点，3）将提供跟踪创伤后应激障碍发展轨迹的目标 和治疗，并刺激治疗PTSD的新方法，超越传统的恐惧条件反射， 灭绝模型。