Computational and electrochemical substrates of social decision-making in humans

人类社会决策的计算和电化学基础

• 批准号: 10428547
• 负责人: Xiaosi Gu
• 金额: $ 99.64万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10428547
• 关键词: Action Potentials; Address; Affect; American Psychiatric Association; Antisocial Personality Disorder; Attention deficit hyperactivity disorder; Behavior; Behavioral; Brain; Catecholamines; Cognition; Collaborations; Computer Models; Conscious; Consensus; Corpus striatum structure; Decision Making; Detection; Diagnostic; Disease; Dopamine; Drug Addiction; Electrodes; Emotional; Emotions; Essential Tremor; Failure; Functional disorder; Goals; Health; Human; Injury; Interpersonal Relations; Investments; Joints; Learning; Machine Learning; Major Depressive Disorder; Measurement; Measures; Mental Depression; Mental disorders; Methodology; Methods; Modeling; Moods; Neuromodulator; Neurotransmitters; Norepinephrine; Obsessive-Compulsive Disorder; Operative Surgical Procedures; Parkinson Disease; Pathologic; Patients; Perception; Persons; Play; Positive Valence; Research Domain Criteria; Rewards; Risk; Role; Safety; Serotonergic System; Serotonin; Signal Transduction; Site; Social Interaction; Social Perception; Social Processes; System; Time; Ventral Striatum; Withdrawal; Work; arm; autism spectrum disorder; base; cognitive function; diagnostic criteria; dopamine system; human model; implantation; information processing; mental state; neurochemistry; neuroregulation; novel; psychologic; putamen; relating to nervous system; social; social communication; social deficits; social norm; social relationships

SUMMARY Dopamine and serotonin systems in the human brain represent two key neuromodulatory signalling systems that impact mood, value-based decision-making, learning, and a host of other cognitive functions. Despite their importance, there is no consensus view from a psychological or computational perspective concerning their exact information-processing functions. Consequently, there is a scarceness of strategies to treat disorders that afflict these systems. We believe that this gap exists primarily because of methodological limitations. While there are many fast methods for recording action potential activity and local field potentials, similar progress for tracking `the other end of the problem' - neurochemical dynamics - has lagged far behind. This is unfortunate since just considering the catecholamines dopamine, serotonin, and norepinephrine, the worldwide health burden of dysfunction in these neuromodulatory systems is immense approaching 400 million people worldwide if one includes just Major Depression and Attention-deficit hyperactivity disorders (WHO, 2017). The overall goal of this project is to investigate the computational and neuromodulatory substrates of interactive social processes hypothesized to be trans-diagnostic RDoC constructs. We will utilize two levels of analysis (molecules/circuits and behavioral/computational) across two RDoC constructs (systems for social processes: perception and understanding of others, subconstruct understanding mental states, and positive valence systems: reward learning, subconstruct reward prediction error) to make inroads into understanding the computational and neural underpinnings of social interaction in humans. Crucially, the interactive social tasks used in this proposal generate an important class of learning signal – a reward prediction error signal – but expressed in the context of an inter-personal interaction. Our two levels of analysis employ (1) computational models of human (two-agent) social exchange estimated from detailed, observed behavior and (2) unique sub- second measurements of dopamine and serotonin from human striatum (three separate sites: caudate, putamen, ventral striatum) during the window-of-opportunity afforded by deep brain stimulating (DBS) electrode implantation surgery for Parkinson's Disease, Essential Tremors, Obsessive Compulsive Disorder.

概括 人脑中的多巴胺和血清素系统代表两个关键的神经调节信号系统 影响情绪、基于价值的决策、学习和许多其他认知功能。尽管他们的 重要性，从心理学或计算的角度来看，对于它们的重要性还没有达成共识。 精确的信息处理功能。因此，缺乏治疗以下疾病的策略： 折磨这些系统。我们认为，这种差距的存在主要是由于方法论的局限性。在那里 有许多快速记录动作电位活动和局部场电位的方法，跟踪的进展类似 “问题的另一端”——神经化学动力学——却远远落后。这很不幸，因为刚刚 考虑到儿茶酚胺、多巴胺、血清素和去甲肾上腺素，全球健康负担 这些神经调节系统的功能障碍是巨大的，全世界有接近 4 亿人，如果 仅包括重度抑郁症和注意力缺陷多动障碍（WHO，2017）。 该项目的总体目标是研究交互式的计算和神经调节基础 社会过程被假设为跨诊断 RDoC 结构。我们将利用两个层次的分析 （分子/电路和行为/计算）跨越两个 RDoC 构造（社交系统） 过程：对他人的感知和理解、子结构理解心理状态以及积极的 价系统：奖励学习、子构造奖励预测误差）以促进理解 人类社交互动的计算和神经基础。至关重要的是，互动社交 该提案中使用的任务会生成一类重要的学习信号——奖励预测误差信号—— 而是在人际互动的背景下表达出来的。我们的两个层次的分析采用 (1) 计算 根据详细的、观察到的行为估计的人类（双主体）社会交换模型和（2）独特的子模型 第二次测量人类纹状体的多巴胺和血清素（三个不同的部位：尾状核、壳核、 腹侧纹状体）在深部脑刺激（DBS）电极提供的机会窗口期间 帕金森病、特发性震颤、强迫症的植入手术。