Neural Mechanisms of Rule-Based Behavior

基于规则的行为的神经机制

• 批准号: 10580819
• 负责人: Timothy J. Buschman
• 金额: $ 43.7万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580819
• 关键词: Acute; Animals; Area; Basal Ganglia; Bayesian Modeling; Behavior; Behavioral; Behavioral Paradigm; Belief; Brain; Chronic; Cognition Disorders; Cognitive; Cognitive deficits; Color; Complex; Computer Models; Corpus striatum structure; Dementia; Detection; Disease; Early Intervention; Eating; Electric Stimulation; Electrodes; Electrophysiology (science); Evolution; Feedback; Foundations; Functional Magnetic Resonance Imaging; Knowledge; Learning; Maintenance; Maps; Measures; Mental Health; Mental disorders; Modeling; Monkeys; Motor; Nature; Neurodegenerative Disorders; Neurons; Obsessive-Compulsive Disorder; Parietal Lobe; Pathologic; Pattern; Physiological; Positioning Attribute; Prefrontal Cortex; Research; Restaurants; Rewards; Role; Saccades; Schizophrenia; Sensory; Shapes; Short-Term Memory; Stimulus; Structure; Techniques; Testing; Training; Update; Work; attentional control; autism spectrum disorder; cognitive control; economic cost; experience; experimental study; flexibility; improved; insight; neural; neuromechanism; neuropsychiatric disorder; novel; novel diagnostics; predictive modeling; response; sensory input; sensory stimulus; social; theories

PROJECT SUMMARY/ABSTRACT Over our lifetime, we learn hundreds of ‘rules’ that define how we should act in a given situation. For example, when in a restaurant, we follow a set of rules that guide the way we order, eat, and pay for a meal. By learning and using rules, we can optimize our behavior and maximize social and physical rewards. Disrupting one’s ability to learn and follow rules can be pathological. Such disruptions are associated with many neuropsychiatric and neurodegenerative disorders, such as schizophrenia and dementia, where they carry high social and economic costs. To develop novel, mechanistically-informed, treatments for these diseases, we must first develop a detailed understanding of the neural mechanisms that support rules. Here, we aim to understand how the brain flexibly learns, follows, and switches between several different rules. Combining large-scale, multi-region electrophysiology with novel behavioral paradigms in monkeys, we will study two aspects of flexible rule-based behavior: First, one must be able to discover which rule to follow in a new situation. This requires integrating information from the world to decide which rule, from a set of known rules, is the correct one for the situation. Our first aim will leverage our large-scale recording techniques to distinguish hypotheses about the relative role of prefrontal cortex, parietal cortex, and basal ganglia in integrating feedback and deciding which rule to follow. Second, we aim to understand how multiple rules are learned, represented, and executed. Specifically, we will test hypotheses that the representation of rules is structured: computationally similar rules use similar neural mechanisms. Such structure is theorized to allow us to rapidly learn new rules in new situations. To this end, monkeys will learn and perform multiple, computationally-related, rules. In our second aim, we will use a combination of chronic and acute electrophysiology to track the neural representation of a rule through learning. This will distinguish hypotheses about how the neural representation of a rule is structured, and how it relates to other, similar, rules. In parallel, our third aim will use the same recordings to understand how rules act on stimulus representations to transform them into rule-appropriate responses. We will test three theories of cognitive control, including a novel dynamic model that hypothesizes rules act by dynamically transforming neural representations between subspaces of neural activity. While our proposed research is basic in nature, we believe it is an important first step in a mechanistic understanding of the core cognitive deficits of several mental illnesses, including schizophrenia. We believe this understanding will improve mental health by leading to new diagnostics and treatments for cognitive disorders. In particular, we hope to use our results to develop physiological markers that will improve detection, allow for earlier intervention, and guide targeted treatments.

项目总结/摘要 在我们的一生中，我们学习了数百条“规则”，这些规则定义了我们在特定情况下应该如何行动。比如说， 在餐馆里，我们遵循一套规则，指导我们如何点菜，吃饭和付款。通过学习 使用规则，我们可以优化我们的行为，最大化社会和物质回报。扰乱某人的能力 学习和遵守规则是病态的这种破坏与许多神经精神和 神经退行性疾病，如精神分裂症和痴呆症，在那里他们携带高社会和经济 成本为了开发针对这些疾病的新的、机械信息的治疗方法，我们必须首先开发一种 详细了解支持规则的神经机制。 在这里，我们的目标是了解大脑如何灵活地学习，遵循和在几种不同的规则之间切换。 结合大规模的，多区域的电生理学与猴子的新行为范式，我们将研究 灵活的基于规则的行为的两个方面： 首先，必须能够发现在新的情况下应该遵循哪条规则。这就需要整合信息 从一系列已知的规则中，决定哪一条规则是正确的。我们的首要目标 将利用我们的大规模记录技术来区分有关前额叶相对作用的假设， 大脑皮层、顶叶皮层和基底神经节在整合反馈和决定遵循哪条规则方面发挥着重要作用。 其次，我们的目标是了解多个规则是如何学习，表示和执行的。具体来说，我们将 测试假设规则的表示是结构化的：计算上相似的规则使用相似的神经网络 机制等这种结构的理论化使我们能够在新的情况下快速学习新的规则。为此目的， 猴子会学习并执行多种与计算相关的规则。在我们的第二个目标中，我们将使用 慢性和急性电生理学的组合来跟踪规则的神经表征， 学习这将区分关于规则的神经表征是如何结构化的假设，以及它是如何 与其他类似的规则有关。与此同时，我们的第三个目标将使用相同的记录来了解规则是如何产生的。 对刺激表征采取行动，将其转化为符合规则的反应。我们将检验三种理论 认知控制，包括一个新的动态模型，假设规则通过动态转换 神经活动的子空间之间的神经表征。 虽然我们提出的研究本质上是基础性的，但我们相信这是机械的重要的第一步。 了解包括精神分裂症在内的几种精神疾病的核心认知缺陷。我们认为 这种理解将通过导致认知障碍的新诊断和治疗来改善心理健康。 紊乱特别是，我们希望利用我们的研究结果来开发生理标记，以提高检测， 允许早期干预，并指导有针对性的治疗。