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.

项目概要 该提案的目标是表征学习和决策中的新计算偏差 创伤后应激障碍。对 PTSD 的主流理解强调加强恐惧学习和威胁检测，以及 恐惧消退/抑制减弱。虽然这个模型解释了 PTSD 的许多方面，但差距越来越大 我们的 PTSD 模型与定义规范计算机制的新兴文献之间 学习和决策。该提案旨在通过定义两个方面的计算偏差来弥合这一差距 PTSD 的学习和决策的临床相关领域。首先，其机制 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 接近-回避冲突解决中的偏向是 PTSD 临床表现的一个基本特征，并且 尽管威胁处理和奖励处理在 PTSD 中被分别描述，但威胁和奖励处理如何 奖励处理相互作用导致回避偏见的研究从未被研究过。第二， 情境相关（即潜伏状态）学习的失调对 PTSD 具有明显的临床意义： 创伤背景之外的威胁学习的泛化与 PTSD 的发展有关； 临床背景之外的消退学习的推广与 PTSD 的治疗有关。 然而，上下文调制学习的计算模型尚未用于理解这些 PTSD 的过程。当前的项目建议使用学习和决策的计算模型 制作新颖的任务，探索接近回避偏差的行为和大脑机制（具体 目标 1) 和上下文调制（即潜在状态）学习（具体目标 2）。病例对照设计是 使用，其中健康成年人、遭受创伤但没有 PTSD 的成年人、遭受创伤且患有 PTSD 的成年人，以及 患有非 PTSD 焦虑症的成年人将在功能磁共振成像期间经历新的学习和决策任务 同时进行心理生理评估。通过定义学习中新的计算偏差 PTSD 的决策，该项目 1) 将弥合我们对 PTSD 的理解和我们对 PTSD 的理解之间的差距。 学习计算机制科学的不断发展，2）有可能解释临床相关的 PTSD 功能障碍的特征，3）将为跟踪 PTSD 发展轨迹提供目标 和治疗，并激发治疗 PTSD 的新方法，超越传统的恐惧条件反射 和灭绝模型。