CRCNS: Computational Foundations for Externalizing/Internalizing Psychopathology

心理健康的一个核心问题是什么潜在的过程引起症状（例如，NIMH战略 计划目标#1）。答案可能不在于个别的诊断类别，而是在全球， 症状的transdiagnosis模式，特别是其突出的和临床上有用的划分为 内化（例如，焦虑）与外化（例如，侵略）形式。该项目旨在描述 这两个症状群及其发展（根据NIMH战略计划目标#2），通过将它们与 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为一些内在症状提供了基础，例如担心， 失调的心理模拟，或“内部信息寻求。“在这里，我们提出并检验了一个假设， 扩展这一框架，以包括外部化症状，我们建议这些症状是平行的 寻求外部信息（探索环境）的失调，建立在最近的理论基础上。 因为这些计算能力，以及许多心理健康症状，都是在儿童时期出现的 和青春期，有一个独特的机会，了解他们的关系，通过发展。 我们将使用计算建模来获得这两种信息的签名， 参与者在两个强化学习任务中的选择行为。我们假设，内化与 外化症状分别与增强的内部信息和外部信息相关 寻求，并进一步反映了异常的发展轨迹。我们在目标1中通过比较任务来测试这一点 在两个大的一般人群样本中收集的成年人在线行为的精神症状。下一个是 在目标2中，我们研究了这些过程是如何在儿童和青少年中使用相同的任务发展的， 以及这种发展在被诊断为内化或外化障碍的儿童中有何不同。 本研究利用并测试了一个统一的计算理论，该理论适用于两种类型的 信息寻求作为一个平行的选择之间的权衡立即回报与延迟， 收集信息并改进以后的选择。这个账户可以弥补目前的一个关键缺口， 计算精神病学研究，它只占相对狭窄的症状范围。通过 连接计算神经科学，精神病学和发展，这个项目将澄清 广泛的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.