Representational dynamics for flexible learning in complex environments

复杂环境中灵活学习的表征动力学

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要人类在日常行为中表现出巨大的灵活性，在适当的时候迅速调整（即）在 Covid-19 发病后采取佩戴口罩的措施），但在不适当的情况下（即在 Covid-19 发病后继续开车）卷入不可避免的车祸）。最近的工作强调了瞬态波动的作用唤醒被认为是由蓝斑去甲肾上腺素 (LC/NE) 系统的激活介导的，在行为调整。增加 NE 药理学促进啮齿类动物的行为更新唤醒的外围测量，例如瞳孔直径和 P300 定向反应，提供了了解人类这些行为调整背后的动力。对这些的机械理解过程可以为广泛的精神疾病提供有价值的治疗靶点，其中行为灵活性受损。然而，当前的理论存在缺陷，部分原因是它未能解释唤醒的情境性质：增强的唤醒反映了在某些情况下更多的行为调整或个人，但其他人较少。我们认为，以前的 NE 计算帐户很可能未能成功解释对行为的异质性影响，因为他们忽略了 NE 所依赖的神经表征行为。计算神经科学的最新进展凸显了神经网络的重要性复杂环境中有效学习的表示，并提供了测量它们的工具。建筑在这项工作中，我们开发了一个计算模型，其中 NE 驱动神经表示的转换当新的表征持续存在时（即在Covid之后）会导致行为调整，但会减少行为当他们不这样做时（在一次奇怪的事故之后）进行调整。我们提出，代表性动态由 NE 不是随机的，而是受到有关环境结构的假设的控制，这些假设不同跨环境和个人，以产生对行为的异质性影响。这个想法可以促进对 NE 操纵将如何改变行为的个性化预测，从而有可能实现更好的治疗注意力缺陷和焦虑症。实现这一目标首先需要基础研究进行实验，以更好地描述唤醒如何以及为何与跨任务环境的行为存在差异相关，个人和学习。在这里，我们进行这些基础研究，首先通过代理测量唤醒对抗性任务结构（即新冠病毒后与事故后）来剖析计算机制它通过它来调节行为。接下来，我们在任务中使用功能磁共振成像直接检查内部表征具有模糊结构，了解个体间在代表性方面是否存在差异以及如何存在差异结构导致个体间的行为差异及其对唤醒的敏感性。最后，我们扩展我们的现有的计算模型，可以通过经验学习结构并做出个性化的对各种可能环境中的行为进行预测。我们测试这些预测，以及在一项大规模在线验证研究中，它们与各种心理健康结构的相关性。