CRCNS:US-Fr Research: Neurobehavioral Assessment of a Reward Learning Model

CRCNS：US-Fr 研究：奖励学习模型的神经行为评估

• 批准号: 9052451
• 负责人: MATTHEW R ROESCH
• 金额: $ 13.77万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9052451
• 关键词: Accounting; Animals; Behavior; Behavioral; Biology; Brain; Camping; Communities; Computer Simulation; Conditioned Stimulus; Development; Drug Addiction; Educational process of instructing; Engineering; Environment; Faculty; Foundations; France; Goals; Heart; High School Student; Human; Human Resources; Individual; Individual Differences; Journals; Learning; Manuscripts; Mathematics; Medicine; Mentors; Methods; Modeling; Neurobiology; Performance; Pharmaceutical Preparations; Pharmacogenetics; Postdoctoral Fellow; Preparation; Process; Protocols documentation; Psychology; Publications; Research; Rewards; Robot; Role; Science; Scientist; Series; Side; Signal Transduction; Students; Techniques; Testing; Training; Underrepresented Minority; Universities; Work; base; classical conditioning; conditioning; dissemination research; drug craving; evidence base; graduate student; high school; individualized medicine; insight; meetings; member; neurobehavioral; optogenetics; outreach; peer; programs; research study; response; symposium; theories; trait

 DESCRIPTION (provided by applicant): The objective of this proposal is to provide powerful insight into the computational and neurobiological processes underlying learning during Pavlovian conditioning, and to elucidate the origin of differences between individuals in their response to a conditioned stimulus (CS), namely sign-trackers (ST) attracted by the CS and goal-trackers (GT) directly attracted by the reward. One of the project's partners recently proposed a computational model which accounts for a large set of studies examining ST/GT behaviors. More importantly, this model has led to a series of new experimental predictions which, if tested experimentally, could help further validate or refute the computational mechanisms that underlie everyday learning. Here we propose a unique series of model-driven experiments to precisely test those predictions on both the computational and neurobiological levels using rigorous behavioral protocols and state-of-the art optogenetic and pharmacogenetic methods. This will enable us to assess and refine the proposed computational theory, and thus to provide a detailed description of the mechanisms underlying inter-individual differences during learning. Intellectual Merit (provided by applicant): Understanding how the brain integrates predictive information is a fundamental issue and has major implications at both theoretical and applied levels. In both ecological and artificial situations, these processes enable animals, humans and even robots to flexibly adapt their performances according to changes in the environment. Particularly poorly understood here are the mechanisms underlying inter-individual differences in learning, which may explain why some individuals fail in learning in particular situations while others succeed. Understanding these individual differences can help us better characterize why some individuals are more prone to drug addiction and craving in front of a CS associated to a drug-taking context, and has implications about individualized treatment. The research herein draws on complementary expertise from Biology, Psychology, Medicine, Applied Mathematics and Engineering in order to elucidate the combination of computational processes and behavioral traits that underlie these differences. To this end, we will systematically manipulate parameters that the model identifies as crucial and evaluate the dynamics and role of dopaminergic error signaling. Our work involves a unique combination of correlative and selective interventional approaches that directly test the fundamental assumptions of the model. Our results will thus provide definitive evidence regarding the competition of model-free and model-based processes in conditioning. The computational model that is at the heart of this proposal may thus represent a major step in the approach of individual differences. Broader Impact (provided by applicant): The broader impacts of this proposal will occur through the integration of the proposed research with teaching and training as follows. 1) Outreach. The PI will broaden the participation of underrepresented groups through research opportunities provided to high school students and underrepresented undergraduates through an ongoing partnership with Eleanor Roosevelt High School. The latter program will consist of a 3-week scientific boot camp during the summer for 1-2 students followed by a year of research in the PI's lab 2) Professional development. This project will train one postdoc, three graduate students, at least three undergraduates, and several high school students in collaborative research. Tiered peer mentoring will allow training of new personnel by more senior lab members, all carried out under the guidance of the PI. 3) Teaching. The boot camp, developed by the PI, will provide students with a foundation in scientific methods and will use evidence-based approaches to introduce them to how scientists investigate research questions using techniques performed in the PI's lab. The French co-PIs will on their side continue their development of introductions to science classes for high-school students and courses at various university levels. 4) Dissemination of research findings. All trainees will present their research t lab meetings, journal clubs, and conferences and will participate in manuscript preparation in order to share results with the scientific community. All publications generated by the project wil be made open-access (via PI's faculty pages and HAL in France).

 描述（由申请人提供）：该提案的目的是提供对巴甫洛夫条件学习期间学习的计算和神经生物学过程的深入了解，并阐明个体对条件刺激（CS）反应差异的根源，即被CS吸引的符号跟踪器（ST）和被奖励直接吸引的目标跟踪器（GT）。该项目的一个合作伙伴最近提出了一种计算模型，该模型可以解释大量检查 ST/GT 行为的研究。更重要的是，该模型引发了一系列新的实验预测，如果通过实验进行测试，可以帮助进一步验证或反驳日常学习背后的计算机制。在这里，我们提出了一系列独特的模型驱动实验，使用严格的行为协议和最先进的光遗传学和药物遗传学方法在计算和神经生物学水平上精确测试这些预测。这将使我们能够评估和完善所提出的计算理论，从而提供学习过程中个体间差异背后的机制的详细描述。 智力优点（由申请人提供）：了解大脑如何整合预测信息是一个基本问题，在理论和应用层面都有重大影响。在生态和人工环境中，这些过程使动物、人类甚至机器人能够根据环境的变化灵活地调整其性能。人们对学习中个体间差异的机制了解甚少，这可以解释为什么有些人在特定情况下学习失败，而有些人则在特定情况下学习失败。 其他人成功了。了解这些个体差异可以帮助我们更好地描述为什么有些人更容易吸毒成瘾并渴望与吸毒环境相关的 CS，并对个体化治疗产生影响。本文的研究借鉴了生物学、心理学、医学、应用数学和工程学的互补专业知识，以阐明这些差异背后的计算过程和行为特征的组合。为此，我们将系统地操纵模型确定的关键参数，并评估多巴胺能错误信号传导的动态和作用。我们的工作涉及相关性和选择性干预方法的独特组合，直接测试模型的基本假设。因此，我们的结果将为无模型和基于模型的调节过程的竞争提供明确的证据。因此，作为该提案核心的计算模型可能代表了个体差异方法的重要一步。 更广泛的影响（由申请人提供）：该提案的更广泛影响将通过将拟议的研究与教学和培训相结合而产生，如下所示。 1）外展。 PI 将通过与埃莉诺·罗斯福高中的持续合作，为高中生和代表性不足的本科生提供研究机会，扩大代表性不足群体的参与。后一个项目将包括在夏季为 1-2 名学生举办为期 3 周的科学训练营，然后在 PI 实验室进行一年的研究 2) 专业发展。该项目将培训一名博士后、三名研究生、至少三名本科生和几名高中生进行合作研究。分层同行指导将允许更高级的实验室成员对新人员进行培训，所有这些都在 PI 的指导下进行。 3）教学。该训练营由 PI 开发，将为学生提供科学方法的基础，并将使用基于证据的方法向他们介绍科学家如何使用 PI 实验室中执行的技术来调查研究问题。法国联合PI将继续开发高中生科学入门课程和各个大学级别的课程。 4）研究成果的传播。所有学员都将在实验室会议、期刊俱乐部和会议上展示他们的研究成果，并将参与手稿准备工作，以便与科学界分享成果。该项目生成的所有出版物都将开放获取（通过 PI 的教师页面和法国的 HAL）。