Representational dynamics for flexible learning in complex environments

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 在新型冠状病毒肺炎（COVID-19）发病后戴口罩），但在不适当的情况下（即在发病后继续驾车）， 发生不可避免的车祸）。最近的研究强调了瞬态波动在 唤醒，被认为是通过激活蓝斑去甲肾上腺素（LC/NE）系统介导的，在 行为调整增加NE β可促进啮齿动物的行为更新， 唤醒的外围测量，如瞳孔直径和P300定向反应，提供了一个窗口， 这些人类行为调整背后的动力学。机械地理解这些 这些过程可以为广泛的精神疾病提供有价值的治疗靶点， 行为灵活性受损。然而，目前的理论福尔斯不够的，部分原因是它没有考虑到 唤醒的背景性质：增强的唤醒反映了在某些环境中更多的行为调整， 个人，但在其他人身上更少。我们认为，以前的NE计算帐户可能未能 解释对行为的异质性影响，因为他们忽略了NE 行为的计算神经科学的最新进展强调了神经网络的重要性。 在复杂的环境中有效学习的表示，并提供了工具来衡量它们。建筑 在这项工作中，我们开发了一个计算模型，其中NE驱动神经表征的转换， 当新的表征持续存在时（即在新冠肺炎之后），导致行为调整，但减少行为 当他们没有调整（在一个反常的事故后）。我们提出，表征动态诱发的 NE不是随机的，而是由关于环境结构的假设决定的， 在不同的环境和个体中，产生不同的唤醒对行为的影响。这个想法可以 促进个性化预测NE操作将如何改变行为，可能使更好地 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 实验，以更好地表征计算基础，通过该基础，人们认识到并作出反应， 上下文的变化。在这个多样性补充，我们将研究识别和计算基础， 应对环境特征的变化，特别关注这些过程如何在 高维特征空间该项目将提供神经网络建模、贝叶斯 建模，实验设计和行为分析，从一个有前途的研究生， 一个代表性不足的背景，可以利用这种培训，推动他走向独立的研究， 位置我们将开发模型并测试它们的预测，以及它们与各种心理健康的相关性。 在一个大规模的在线验证研究中。

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 (i.e. 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 the computational basis through which people recognize and respond to changes in context. In this diversity supplement we will examine the computational basis for recognizing and responding to changes in environmental features, specifically focusing on how such processes scale up in higher dimensional feature spaces. The project will provide training in neural network modeling, Bayesian modeling, experimental design, and behavioral analysis to a promising graduate student from an underrepresented background who could leverage this training to propel him toward an independent research position. We will develop models and test their predictions, as well as their relevance to various mental health constructs, in a large-scale online validation study.