Computational Biases of Learning and Decision-Making in PTSD

PTSD 中学习和决策的计算偏差

• 批准号: 10678907
• 负责人: Joshua M Cisler
• 金额: $ 39.03万
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678907
• 关键词: Address; Adult; Anxiety; Anxiety Disorders; Behavior; Behavioral; Brain; Characteristics; Clinical; Computational Science; Computer Models; Corpus striatum structure; Data; Decision Making; Detection; Development; Disease; Disease remission; Distress; Extinction; Fright; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Hippocampus; Individual; Interpersonal Violence; Intervention; Knowledge; Learning; Literature; Mediating; Mediator; Memory; Mental disorders; Methods; Modeling; Morbidity - disease rate; Outcome; Participant; 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; case control; clinically relevant; computational neuroscience; conditioned fear; conflict resolution; design; expectation; learning extinction; memory retrieval; men; neural; neural circuit; novel; pharmacologic; reward processing; social; 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 这项建议的目标是刻画在学习和决策中的新的计算偏差 创伤后应激障碍。对创伤后应激障碍的主要理解强调加强恐惧学习和威胁检测，以及 减弱的恐惧消退/抑制。虽然这个模型解释了创伤后应激障碍的许多方面，但两者之间的差距越来越大 在我们的创伤后应激障碍模型和定义创伤后应激障碍标准计算机制的新兴文献之间 学习和决策。该提案旨在通过将计算偏差分为两部分来弥合这一差距 创伤后应激障碍的学习和决策的临床相关领域。首先，通过哪些机制 患有创伤后应激障碍的人更喜欢以失去潜在回报为代价来避免威胁，这一点尚不清楚。这 在接近-回避冲突解决方面的偏见是创伤后应激障碍临床表现的一个基本特征，并且 虽然威胁处理和奖励处理在创伤后应激障碍中分别具有特征，但威胁和奖励处理如何 奖赏加工的交互作用导致对回避的偏见从未被调查过。第二, 情境依赖(即潜伏期)学习的失调对创伤后应激障碍有明显的临床意义： 创伤情境外威胁学习的泛化与创伤后应激障碍的发展有关； 在临床环境之外的消退学习的推广与创伤后应激障碍的治疗有关。 然而，语境调节学习的计算模型还没有被用来理解这些 创伤后应激障碍的进程。目前的项目建议使用学习和决策的计算模型- 提出新的任务，探索接近-回避偏见的行为和大脑机制(特定的 目标1)和语境调节(即潜伏期)学习(特定目标2)。病例对照设计将是 健康成人、无创伤后应激障碍的创伤暴露成人、创伤后应激障碍成人以及 患有非创伤后应激障碍的成年人在功能磁共振成像过程中会经历新的学习和决策任务 同时进行心理生理评估。通过定义学习和学习中的新的计算偏差 在创伤后应激障碍的决策中，项目1)将弥合我们对创伤后应激障碍的理解和我们的 不断发展的学习计算机制科学，2)有可能解释与临床相关的 创伤后应激障碍的功能障碍特征，以及3)将为追踪创伤后应激障碍的发展轨迹提供目标 和治疗，并激发超越传统恐惧条件反射的治疗创伤后应激障碍的新方法 和灭绝模型。