Representational dynamics for flexible learning in complex environments

项目摘要 人类在日常行为中表现出极大的灵活性，在适当的时候迅速调整(即 新冠肺炎发作后佩戴口罩)，但不适合时(即在以下情况下继续驾驶 卷入一场不可避免的车祸)。最近的研究强调了瞬时波动在体内的作用 觉醒被认为是通过激活蓝斑去甲肾上腺素(LC/NE)系统而起作用的 行为调整。提高去甲肾上腺素的药理作用促进啮齿动物的行为更新 唤醒的外周测量，如瞳孔直径和P300定向反应，提供了一扇了解 人类的这些行为调整背后的动力。对这些的机械性理解 这一过程可以为一系列精神疾病提供有价值的治疗靶点 行为灵活性受损。然而，目前的理论存在不足，部分原因是它未能解释 唤醒的背景性质：这种高度的唤醒反映了在某些环境或 个人，但在其他人身上就更少了。我们认为，以前对NE的计算很可能没有 解释对行为的异质性影响，因为他们忽略了NE上的神经表征 行为。计算神经科学的最新进展突显了神经科学的重要性 在复杂的环境中有效学习的表征，并提供了衡量它们的工具。建房 在这项工作中，我们开发了一个计算模型，在该模型中，NE驱动神经表示中的转换 当新的表征持续存在时，导致行为调整(即，Covid之后)，但减少了行为 当他们没有调整的时候(在一场反常的事故之后)。我们认为由以下因素引起的表征动力学 NE不是随机的，而是由关于环境结构的假设支配的，这些假设是不同的 在不同的环境和个体中，产生不同程度的唤醒对行为的影响。这一想法可能 促进对NE操作将如何改变行为的个性化预测，潜在地实现更好的 注意力缺陷和焦虑症的治疗。要实现这一目标，首先需要基础研究 为了更好地刻画唤醒如何以及为什么在不同的任务情境下与行为有不同的关系， 个人，和学习。在这里，我们进行了这些基础研究，首先通过代理测量唤醒 对抗性任务结构(即冬季后和事故后)，以剖析计算机制 它通过它来调节行为。接下来，我们在任务中使用fMRI直接检查内部表示 用模棱两可的结构，来了解个体间是否以及如何在表征上存在差异 结构会导致个体间的行为差异及其对唤醒的敏感性。最后，我们将我们的 现有的计算模型，它可以通过经验学习结构，并使之个性化 对各种可能环境中的行为进行预测。我们测试了这些预测，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。

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.