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）：本提案的目的是提供强大的洞察力的计算和神经生物学过程的基础上学习巴甫洛夫条件反射，并阐明个人之间的差异的起源，在他们的反应条件刺激（CS），即符号跟踪器（ST）所吸引的CS和目标跟踪器（GT）直接吸引的奖励。该项目的一个合作伙伴最近提出了一个计算模型，该模型解释了大量研究ST/GT行为的研究。更重要的是，这个模型已经导致了一系列新的实验预测，如果实验测试，可以帮助进一步验证或反驳日常学习的计算机制。在这里，我们提出了一系列独特的模型驱动实验，使用严格的行为方案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，从而提供一个详细的描述的机制，在学习过程中的个体间的差异。 智力优势（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具有重大意义。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。特别是在这里，人们对学习中个体间差异的潜在机制知之甚少，这可以解释为什么有些人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在与吸毒背景相关的CS面前成瘾和渴望，并对个性化治疗产生影响。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为至关重要的参数，并评估多巴胺能错误信号的动力学和作用。我们的工作涉及一个独特的相关性和选择性干预方法的组合，直接测试模型的基本假设。因此，我们的研究结果将提供明确的证据，关于竞争的无模型和模型为基础的过程中调节。因此，作为这一提议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）：通过将拟议的研究与教学和培训相结合，本提案将产生更广泛的影响，具体如下。1)外展PI将通过与Eleanor Roosevelt高中的持续合作关系，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周的科学靴子训练营，然后在PI实验室进行为期一年的研究。本计画将训练一位博士后、三位研究生、至少三位本科生及数位高中生进行合作研究。分层同行指导将允许由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)教学由PI开发的靴子营地将为学生提供科学方法的基础，并将使用循证方法向他们介绍科学家如何使用PI实验室中的技术调查研究问题。法国的共同PI将继续为高中学生和各大学级别的课程制定科学课程的介绍。4)传播研究成果。所有学员将出席他们的研究实验室会议，期刊俱乐部和会议，并将参与手稿准备，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）。