CRCNS: Computational Foundations for Externalizing/Internalizing Psychopathology

心理健康中的一个核心问题是什么潜在的过程会导致症状(例如，NIMH策略性 计划目标1)。答案可能不在个别诊断类别，而在全球， 症状的跨诊断模式，特别是它们显著的和临床上有用的分类 内化(如焦虑)与外化(如攻击性)形式。这个项目的目的是描述 这两个症状群及其发展(根据NIMH战略计划目标2)，通过将它们联系到 已经在健康大脑中研究过的决策计算机制。 以前的计算精神病学研究基于一些内在化症状，如焦虑 失调的心理模拟，或“内部信息寻求”。在这里，我们提出并检验一个假设 要扩展这个框架以包括外化症状，我们建议这些症状是平行的 建立在最近的理论基础上的外部信息寻求(环境探索)的失调。 因为这些计算能力，以及许多心理健康症状，在童年时期就出现了 和青春期，有一个独特的机会来了解他们的关系，通过发展。 我们将使用计算模型来获得这两种信息的签名 被试在两种强化学习任务中的选择行为。我们假设内部化与 外化症状分别与增强的内部信息和外部信息相关联 寻找，并进一步反映反常的发展轨迹。我们在目标1通过比较任务来测试这一点 在网上收集的两个大的成人普通人群样本中对精神症状的行为。下一首, 在目标2中，我们研究了在儿童和青少年中使用相同的任务时，这些过程是如何发展的， 以及这种发育在诊断为内化或外化障碍的儿童中有何不同。 目前的研究利用并测试了一种统一的计算理论，该理论位于两种类型的 在为获得直接回报而行动与拖延之间的权衡中，寻求信息作为平行的选择 收集信息并改进以后的选择。这一帐户可以克服当前的一个关键缺口 计算精神病学研究，这只解释了相对较窄的症状范围。通过 将计算神经科学、精神病学和发展联系起来，这个项目将阐明 一系列外化和内化症状的神经计算基础。

A core question in mental health is what underlying processes give rise to symptoms (e.g., NIMH strategic plan goal #1). Answers may lie less in individual diagnostic categories, but instead in global, transdiagnostic patterns of symptoms, notably their prominent and clinically useful division into internalizing (e.g., anxiety) vs. externalizing (e.g., aggression) forms. This project aims to characterize these two symptom clusters and their development (per NIMH strategic plan goal #2), by relating them to computational mechanisms for decision making that have been studied in the healthy brain. Previous computational psychiatry research grounds some internalizing symptoms such as worry in dysregulated mental simulation, or "internal information seeking." Here, we propose and test a hypothesis to extend this framework to comprise externalizing symptoms, which we suggest are grounded in parallel dysregulation of external information seeking (exploration of the environment), building on a recent theory. Because these computational capacities, as well as many mental health symptoms, emerge in childhood and adolescence, there is a unique opportunity to understand their relationship via development. We will use computational modelling to derive signatures of both sorts of information seeking from participants' choice behavior in two reinforcement learning tasks. We hypothesize that internalizing vs. externalizing symptoms are associated, respectively, with enhanced internal vs. external information seeking, and further reflect aberrant developmental trajectories. We test this in Aim 1 by comparing task behavior to psychiatric symptoms in two large general population samples collected online in adults. Next, in Aim 2, we examine how these processes develop using the same tasks in children and adolescents, and how this development differs in children with a diagnosed internalizing or externalizing disorder. The present research leverages and tests a unifying computational theory that situates both types of information seeking as parallel options in a tradeoff between acting for immediate reward vs delaying to gather information and improve later choices. This account can overcome a crucial gap in current computational psychiatry research, which only accounts for a relatively narrow range of symptoms. By connecting computational neuroscience, psychiatry, and development, this project will clarify the neurocomputational foundations of a wide range of externalizing and internalizing symptoms.