CRCNS: Computational Foundations for Externalizing/Internalizing Psychopathology
CRCNS:外化/内化精神病理学的计算基础
基本信息
- 批准号:10831117
- 负责人:
- 金额:$ 20.27万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-08-03 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAdolescenceAdolescentAdultAggressive behaviorAnxietyArchitectureBehaviorBrainCategoriesChildChildhoodChoice BehaviorClinicalClinical DataCompulsive BehaviorComputer ModelsDataDecision MakingDevelopmentDevelopmental ProcessDiagnosisDiagnosticDiagnostic testsDimensionsDiseaseDissociationEnvironmentFoundationsFunctional disorderGeneral PopulationGoalsImpulsivityIndividualLearningLegal patentMapsMeasuresMental DepressionMental HealthMental disordersMethodsNational Institute of Mental HealthParticipantPatientsPatternPharmacological TreatmentPopulationProcessPsyche structurePsychiatryPsychological reinforcementPsychopathologyRaceResearchRewardsSamplingSortingStrategic PlanningSymptomsTaxonomyTestingUpdateWorkassociated symptomcomorbiditycomputational neuroscienceexperimental studyfollow-upimprovedindividualized medicineinformation gatheringinsightneglectneuralnovelnovel therapeutic interventionpopulation basedpsychiatric symptompsychologicresponseruminationsimulationsymptom clustertheories
项目摘要
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.
心理健康的一个核心问题是哪些潜在过程会引起症状(例如,NIMH 战略
计划目标#1)。答案可能较少在于个体诊断类别,而是在于全局,
症状的跨诊断模式,特别是它们突出且临床上有用的划分
内化(例如焦虑)与外化(例如攻击性)形式。该项目旨在表征
这两个症状群及其发展(根据 NIMH 战略计划目标 #2),将它们与
已在健康大脑中研究过的决策计算机制。
先前的计算精神病学研究基于一些内化症状,例如担忧
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
扩展这个框架以包含外化症状,我们建议这些症状是并行的
基于最新理论的外部信息寻求(环境探索)失调。
因为这些计算能力以及许多心理健康症状是在童年时期出现的
和青春期,有一个独特的机会通过发展来了解他们的关系。
我们将使用计算模型来导出两种信息的签名
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
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
在线收集的两个成人普通人群样本中的行为对精神症状的影响。下一个,
在目标 2 中,我们研究了这些过程如何在儿童和青少年中使用相同的任务来发展,
以及患有内化或外化障碍的儿童的这种发展有何不同。
本研究利用并测试了一种统一的计算理论,该理论将两种类型的
信息寻求作为并行选择,在立即奖励与延迟奖励之间进行权衡
收集信息并改进以后的选择。该帐户可以克服当前的关键差距
计算精神病学研究,仅解释相对狭窄的症状范围。经过
该项目将计算神经科学、精神病学和发展联系起来,将阐明
广泛的外化和内化症状的神经计算基础。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Nathaniel Douglass Daw其他文献
Nathaniel Douglass Daw的其他文献
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{{ truncateString('Nathaniel Douglass Daw', 18)}}的其他基金
Differentiating reward seeking and loss avoidance with reference-dependent learning models
通过参考依赖学习模型区分奖励寻求和损失避免
- 批准号:
10219070 - 财政年份:2019
- 资助金额:
$ 20.27万 - 项目类别:
Differentiating reward seeking and loss avoidance with reference-dependent learning models
通过参考依赖学习模型区分奖励寻求和损失避免
- 批准号:
10015342 - 财政年份:2019
- 资助金额:
$ 20.27万 - 项目类别:
Differentiating reward seeking and loss avoidance with reference-dependent learning models
通过参考依赖学习模型区分奖励寻求和损失避免
- 批准号:
10449209 - 财政年份:2019
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Representational foundations of adaptive behavior in natural and artificial
CRCNS:自然和人工适应性行为的代表性基础
- 批准号:
9292377 - 财政年份:2015
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Representational foundations of adaptive behavior in natural and artificial
CRCNS:自然和人工适应性行为的代表性基础
- 批准号:
9052441 - 财政年份:2015
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Computational and neural mechanisms of memory-guided decisions
CRCNS:记忆引导决策的计算和神经机制
- 批准号:
8837113 - 财政年份:2014
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Computational and neural mechanisms of memory-guided decisions
CRCNS:记忆引导决策的计算和神经机制
- 批准号:
8926934 - 财政年份:2014
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Computational and neural mechanisms of memory-guided decisions
CRCNS:记忆引导决策的计算和神经机制
- 批准号:
9098673 - 财政年份:2014
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Reinforcement learning in multi-dimensional action spaces
CRCNS:多维行动空间中的强化学习
- 批准号:
7779551 - 财政年份:2009
- 资助金额:
$ 20.27万 - 项目类别:
CRCNS: Reinforcement learning in multi-dimensional action spaces
CRCNS:多维行动空间中的强化学习
- 批准号:
8068884 - 财政年份:2009
- 资助金额:
$ 20.27万 - 项目类别:
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