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: 10818994
• 负责人: Matthew Nassar
• 金额: $ 8.55万
• 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/10818994
• 关键词: Accidents; Adopted; Anxiety Disorders; Arousal; Attentional deficit; Basic Science; Bayesian Modeling; Behavior; Behavioral; Brain; COVID-19; Complex; Computer Models; Cues; Diameter; EP300 gene; Environment; Experimental Designs; Failure; Future; Goals; Human; Impairment; Individual; Learning; Life; Masks; Measures; Mediating; Mental Health; Mental disorders; Modeling; Nature; Neural Network Simulation; Peripheral; Persons; Play; Positioning Attribute; Process; Pupil; Research; Rodent; Role; Structure; Testing; Time; Training; Underrepresented Students; Update; Work; automobile accident; computational basis; computational neuroscience; coping; experience; experimental study; falls; flexibility; graduate student; high dimensionality; locus ceruleus structure; multidimensional data; neural; norepinephrine system; personalized predictions; pharmacologic; response; scale up; theories; therapeutic target; tool; validation studies

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.

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（即在新冠疫情之后），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，而是受到环境结构假设的控制，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 操作将如何改变行为的个性化预测，从而可能实现更好的 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 实验以更好地描述人们识别和响应的计算基础 上下文的变化。在这个多样性补充中，我们将研究识别和识别的计算基础 响应环境特征的变化，特别关注这些过程如何在 更高维的特征空间。该项目将提供神经网络建模、贝叶斯模型的培训 为一名来自某大学的有前途的研究生提供建模、实验设计和行为分析 代表性不足的背景谁可以利用这种培训来推动他进行独立研究 位置。我们将开发模型并测试他们的预测，以及它们与各种心理健康的相关性 在大规模在线验证研究中构建。