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: 9143067
• 负责人: MATTHEW R ROESCH
• 金额: $ 13.33万
• 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/9143067
• 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; 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 Groups; Universities; Work; base; broadening participation research; classical conditioning; conditioning; dissemination research; drug craving; evidence base; graduate student; high school; individualized medicine; insight; meetings; member; neurobehavioral; optogenetics; outreach; peer coaching; 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(申请人提供)：这项建议的目的是对巴甫洛夫条件反射过程中潜在的计算和神经生物学过程提供强有力的见解，并阐明个体对条件刺激(CS)反应的差异的根源，即由条件刺激吸引的手势追踪器(ST)和由奖赏直接吸引的目标追踪器(GT)。该项目的一个合作伙伴最近提出了一个计算模型，该模型解释了一系列检查ST/GT行为的研究。更重要的是，这一模型导致了一系列新的实验预测，如果在实验中进行测试，这些预测可能有助于进一步验证或驳斥构成日常学习基础的计算机制。在这里，我们提出了一系列独特的模型驱动的实验，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地测试这些预测。这将使我们能够评估和完善所提出的计算理论，从而提供关于学习过程中潜在的个体差异的机制的详细描述。 智力价值(由申请者提供)：了解大脑如何整合预测性信息是一个基本问题，在理论和应用层面都有重大影响。在生态和人工情况下，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它们的表现。这里特别不了解的是个体间学习差异的潜在机制，这可能解释了为什么一些人在特定情况下学习失败，而 其他人则取得了成功。了解这些个体差异可以帮助我们更好地刻画为什么一些人更容易在与吸毒环境相关的CS面前上瘾和渴望，并对个体化治疗具有启示意义。这里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关键的参数，并评估多巴胺能错误信号的动力学和作用。我们的工作涉及相关和选择性干预方法的独特组合，这些方法直接测试模型的基本假设。因此，我们的结果将为条件反射中无模型过程和基于模型过程的竞争提供确凿的证据。因此，作为这一提议核心的计算模型可能代表着在处理个体差异方面迈出的重要一步。 更广泛的影响(由申请者提供)：这项建议的更广泛的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。国际和平协会将通过与埃莉诺·罗斯福高中的持续伙伴关系，向高中生和代表不足的本科生提供研究机会，扩大代表不足群体的参与。后一项计划将包括在暑期为1-2名学生举办为期3周的科学新兵训练营，然后在国际学生联合会的实验室进行为期一年的专业发展研究。该项目将培养一名博士后、三名研究生、至少三名本科生和几名高中生进行合作研究。分级同行指导将允许由更资深的实验室成员培训新人员，所有这些都在PI的指导下进行。3)教学。由PI开发的新兵训练营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实验室中执行的技术来调查研究问题。法国联合投资促进机构将继续为高中生开设科学课程入门课程，并在不同大学级别开设课程。4)研究成果的传播。所有受训人员将在实验室会议、期刊俱乐部和会议上陈述他们的研究成果，并将参与手稿准备，以便与科学界分享成果。该项目产生的所有出版物都将开放获取(通过PI的教员页面和法国的HAL)。