Neurocognitive mechanisms of task representation reorganization during task learning

任务学习过程中任务表征重组的神经认知机制

• 批准号: 10561485
• 负责人: Jiefeng Jiang
• 金额: $ 54.31万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10561485
• 关键词: Adaptive Behaviors; Affect; Animals; Artificial Intelligence; Attention deficit hyperactivity disorder; Behavior; Behavioral; Brain; Brain imaging; Clinical; Cognitive; Collection; Complex; Data; Disease; Functional Magnetic Resonance Imaging; Goals; Hippocampus (Brain); Human; Human Characteristics; Intelligence; Knowledge; Learning; Link; Maps; Measures; Memory; Mental disorders; Methods; Modeling; Neurocognitive; Participant; Patients; Pattern; Performance; Process; Psyche structure; Quality of life; Research; Response to stimulus physiology; Retrieval; Role; Schizophrenia; Shapes; Specific qualifier value; Statistical Data Interpretation; Structure; Support System; System; Task Performances; Testing; Thinking; Work; base; cognitive control; executive function; experience; experimental study; flexibility; improved; information processing; insight; intelligent agent; learning ability; long term memory; neuroimaging; procedural memory; programs; relating to nervous system; relational memory; response; young adult

Project Summary/Abstract Humans possess extraordinary flexibility in our behavior: Given the same environmental input, we can act differently depending on our goals and the context. For example, when facing the same data, we can process them differently depending on our goals (e.g., visualize the data to obtain a figure, perform statistical analysis to test a prediction, or even delete the data if the goal is to free storage space). To date, research on this topic has focused on how such flexible behavior is implemented via cognitive control, which is a set of cognitive mechanisms supporting goal-directed and top-down modulation on information processing in the brain. In other words, much research has been conducted to study how a task is executed. However, less is known about where such task knowledge is from, that is, the mnemonic mechanisms that encode, reinforce, and generalize the neural representations of task knowledge. Understanding these mechanisms is crucial to fully understand human intelligence, as the remarkable abilities of learning and retaining task knowledge promptly and efficiently distinguish humans from other animals and artificial intelligent agents and make us adaptive to this ever-changing world. Furthermore, filling the knowledge gap of how we learn and remember task knowledge is also key to understand, detect and treat task learning deficits that are common in mental disorders such as schizophrenia and attention-deficit / hyperactivity disorder (ADHD). In this project, we will focus on the hippocampus, a central brain structure for learning and memory. To achieve the objective of uncovering the hippocampal contributions to task learning, six experiments are proposed using a combination of behavioral methods and human functional magnetic resonance imaging. Specifically, Aim 1 will identify hippocampal contribution to constructing a task representation by assembling task information and experiences to build a task model. Aim 2 will identify how the hippocampus encodes a new task representation into a memory network of existing representations. Aim 3 will identify how the hippocampus reshapes existing task representations when they become associated with other tasks via compositional relations. This work is expected to identify how the human hippocampus constructs key content of task representations (Aim 1) and organizes multiple task representations in relation to each other (Aim 2 and 3). These findings will further our understanding of how the hippocampus contributes to task learning, cognitive control and adaptive behavior. This work will also have clinical impact in bridging the gap between hippocampal abnormality and task learning deficits, which were separately observed in mental disorders such as schizophrenia and ADHD. Ultimately, this work will have a broad impact in helping detect and treat task learning deficits.

项目摘要/摘要 人类的行为具有非凡的灵活性：鉴于相同的环境输入，我们可以采取行动 不同于我们的目标和背景。例如，当面对相同的数据时，我们可以处理 它们根据我们的目标有所不同（例如，可视化数据以获得数字，执行统计分析 测试预测，甚至删除数据，如果目标是免费存储空间）。迄今为止，研究此主题 专注于如何通过认知控制实现这种灵活行为，这是一组认知 支持目标指导和自上而下的关于大脑信息处理的机制。在其他 单词，已经进行了许多研究以研究如何执行任务。但是，关于 这些任务知识来自何处，即编码，增强和推广的助攻机制 任务知识的神经表示。了解这些机制对于充分理解 人类智能，作为迅速学习和保留任务知识的非凡能力 有效地将人类与其他动物和人工智能代理区分开，并使我们适应 不断变化的世界。此外，填补我们学习和记住任务知识的知识差距 也是理解，检测和治疗任务学习缺陷的关键，这些缺陷在精神障碍中很常见 精神分裂症和注意力缺陷 /多动症（ADHD）。在这个项目中，我们将专注于 海马，一种用于学习和记忆的中央大脑结构。实现揭示的目标 海马对任务学习的贡献，使用行为的组合提出了六个实验 方法和人类功能磁共振成像。具体来说，AIM 1将识别海马 通过组装任务信息和经验来构建一个任务表示形式的贡献 任务模型。 AIM 2将确定海马如何编码新任务表示形式 现有表示的网络。 AIM 3将确定海马如何重塑现有任务 表示通过组成关系与其他任务相关联时。这项工作是 希望确定人类海马如何构建任务表示的关键内容（AIM 1）和 相互组织多个任务表示（AIM 2和3）。这些发现将进一步 了解海马如何有助于任务学习，认知控制和适应性行为。 这项工作还将在弥合海马异常和任务学习之间的差距方面产生临床影响 缺陷，在精神分裂症和多动症等精神疾病中分别观察到。最终，这个 工作将在帮助检测和治疗任务学习缺陷方面产生广泛的影响。