Computational Biases of Learning and Decision-Making in PTSD

PTSD 中学习和决策的计算偏差

• 批准号: 10451045
• 负责人: Joshua M Cisler
• 金额: $ 60.68万
• 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/10451045
• 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; 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 该建议的目的是表征在学习和决策中的新计算偏见 PTSD。对PTSD的主要理解强调了恐惧学习和威胁检测的更高，以及 恐惧灭绝 /抑制减弱。尽管该模型解释了PTSD的许多方面，但差距越来越大 在我们的PTSD模型与新兴文献之间，定义了规范计算机制 学习和决策。该建议旨在通过定义两个计算偏见来弥合这一差距 PTSD中的学习和决策与临床相关的领域。首先，通过 具有PTSD的个人更喜欢避免威胁以牺牲潜在的奖励为代价。这 避免进路冲突的偏见是解决PTSD的临床表现的重要特征， 尽管威胁处理和奖励处理已在PTSD中分别表征，但威胁和威胁如何 从未研究过奖励处理相互作用以导致避免偏见的偏见。第二， 探视环境的失调（即潜在状态）学习在PTSD中具有明显的临床意义： 在创伤背景之外进行威胁学习的概括与PTSD的发展有关。 在临床环境之外的灭绝学习的概括与PTSD的治疗有关。 但是，尚未使用上下文调制学习的计算模型来理解这些 PTSD中的过程。当前的项目建议使用学习和决策的计算建模 - 制作探测避免接近偏见的行为和大脑机制的新任务（特定 目标1）和上下文调制（即潜在状态）学习（特定目的2）。案例控制的设计将是 在健康的成年人中使用的，不含PTSD的外伤成年人，具有PTSD创伤的成年人和 非PTSD焦虑症的成年人将在fMRI期间接受新颖的学习和决策任务 并发心理生理评估。通过定义学习中新颖的计算偏见和 PTSD的决策，项目1）将弥合我们对PTSD和我们的理解之间的差距 不断发展的学习计算机制科学，2）有可能解释与临床相关的 PTSD中功能障碍的功能，3）将提供追踪PTSD开发轨迹的目标 和治疗，并刺激治疗PTSD的新方法，这些方法超出了传统恐惧条件 和灭绝模型。