Representational dynamics for flexible learning in complex environments

复杂环境中灵活学习的表征动力学

• 批准号: 10674993
• 负责人: Matthew Nassar
• 金额: $ 58.86万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10674993
• 关键词: Accidents; Adopted; Affect; Algorithms; Anxiety Disorders; Arousal; Attention deficit hyperactivity disorder; Attentional deficit; Basic Science; Behavior; Behavioral; Brain; COVID-19; Cognitive; Complex; Computer Models; Cues; Data; Dependence; Diameter; EP300 gene; Effectiveness; Electroencephalography; Environment; Etiology; Failure; Functional Magnetic Resonance Imaging; Future; Goals; Human; Impairment; Individual; Individual Differences; Instruction; Learning; Life; Link; Masks; Measures; Mediating; Mental Health; Mental disorders; Modeling; Nature; Norepinephrine; Participant; Pattern; Peripheral; Persons; Pharmaceutical Preparations; Play; Policies; Process; Proxy; Pupil; Reversal Learning; Rodent; Role; Signal Transduction; Structure; Testing; Time; Update; Work; automobile accident; behavior influence; behavior prediction; computational neuroscience; computer framework; coping; design; experience; experimental study; falls; flexibility; inter-individual variation; learning algorithm; locus ceruleus structure; neural; neuromechanism; norepinephrine system; novel; personalized predictions; pharmacologic; post-COVID-19; predictive modeling; research study; response; theories; therapeutic target; tool; validation studies

Project Abstract Humans display tremendous flexibility in their everyday behavior, adjusting it rapidly when appropriate (i.e. adopting mask wearing after onset of Covid-19), but not when inappropriate (i.e. continuing to drive after involvement in an unavoidable car accident). Recent work has highlighted the role that transient fluctuations in arousal, thought to be mediated by activation of the locus coeruleus norepinephrine (LC/NE) system, play in behavioral adjustments. Increasing NE pharmacologically promotes behavioral updating in rodents and peripheral measures of arousal, such as pupil diameter and P300 orienting response, provide a window into the dynamics that underlie these behavioral adjustments in humans. A mechanistic understanding of these processes could provide a valuable therapeutic target for a wide range of psychiatric disorders in which behavioral flexibility is impaired. However, current theory falls short, in part because it fails to account for the contextual nature of arousal: that heightened arousal reflects more behavioral adjustment in some settings or individuals, but less in others. We believe that previous computational accounts of NE have likely failed to explain heterogenous effects on behavior because they have ignored the neural representations on which NE acts. Recent advances in computational neuroscience have highlighted the importance of neural representations for efficient learning in complex environments, and provided tools to measure them. Building on this work, we developed a computational model in which NE drives transitions in neural representation that lead to behavioral adjustment when new representations persist in time (ie. after Covid), but reduce behavioral adjustment when they do not (after a freak accident). We propose that representational dynamics evoked by NE are not random, but instead are governed by assumptions about environmental structure, which differ across settings and individuals, to produce heterogeneous effects of arousal on behavior. This idea could facilitate personalized predictions for how NE manipulations would alter behavior, potentially enabling better treatment of attention deficit and anxiety disorders. Achieving this goal would first require basic research experiments to better characterize how and why arousal differentially relates to behavior across task contexts, individuals, and learning. Here we conduct these basic research studies, first measuring arousal by proxy in adversarial task structures (i.e. post-covid versus post-accident) to dissect the computational mechanisms through which it modulates behavior. Next, we examine internal representations directly, using fMRI in a task with ambiguous structure, to understand whether and how inter-individual differences in representational structure give rise to inter-individual differences in behavior and its sensitivity to arousal. Finally, we extend our existing computational model such that it can learn structure through experience and make individualized predictions for behavior across a wide range of possible environments. We test these predictions, as well as their relevance to various mental health constructs, in a large-scale online validation study.

项目摘要 人类的日常行为表现出极大的灵活性，在适当时迅速调整（即 在covid-19发作后采用面具戴戴面具），但在不适当的情况下（即继续开车之后继续开车 参与不可避免的车祸）。最近的工作强调了瞬时波动在 唤醒，被认为是通过激活基因座肠系膜（LC/NE）系统介导的， 行为调整。增加NE的药理学可以促进啮齿动物的行为更新和 唤醒的外围度量，例如学生直径和p300响应，提供了一个窗口 这些行为调整是人类的动力。对这些的机械理解 过程可以为多种精神疾病提供宝贵的治疗靶点 行为灵活性受损。但是，当前的理论不足，部分是因为它无法解决 唤醒的上下文性质：加剧的唤醒反映了某些环境中的更多行为调整或 个人，但在别人中更少。我们认为，以前关于NE的计算帐户可能没有 解释对行为的异源作用，因为它们忽略了NE的神经表示 行为。计算神经科学的最新进展突出了神经的重要性 在复杂环境中有效学习的表示形式，并提供了测量它们的工具。建筑 在这项工作中，我们开发了一种计算模型，在该模型中，NE在神经表示中驱动过渡 当新表示持续时间时（即COVID之后）时，导致行为调整，但会降低行为 当他们不（怪胎事故发生后）时进行调整。我们提出了由 NE不是随机的，而是由关于环境结构的假设所支配的， 跨环境和个人产生唤醒对行为的异质作用。这个想法可以 促进对NE操作将如何改变行为的个性化预测 治疗注意力不足和焦虑症。实现这一目标将首先需要基础研究 实验以更好地表征如何以及为什么唤醒差异化与任务上下文中的行为相关的实验， 个人和学习。在这里，我们进行了这些基础研究，首先是通过代理测量的 对抗性任务结构（即后载体与后源），以剖析计算机制 通过它调节行为。接下来，我们在任务中使用fMRI直接检查内部表示 具有模棱两可的结构，以了解代表性中的个体差异以及如何 结构引起了行为间差异及其对唤醒的敏感性。最后，我们扩展了我们的 现有的计算模型可以通过经验来学习结构并实现个性化 在各种可能的环境中对行为的预测。我们测试这些预测以及 在一项大规模的在线验证研究中，它们与各种心理健康结构的相关性。