CRCNS: Neurocomputational Study of Reward-Related Decision-Making & Uncertainty

CRCNS：奖励相关决策的神经计算研究

• 批准号: 10021445
• 负责人: Angela Yu
• 金额: $ 23.87万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021445
• 关键词: Address; Affect; Alcohol or Other Drugs use; Anhedonia; Animals; Anxiety; Area; Bayesian Method; Behavior; Behavioral; Brain; Brain region; Cardiac; Choice Behavior; Cognitive; Complex; Control Locus; Data; Decision Making; Detection; Disease; Drug abuse; Drug usage; Environment; Functional Magnetic Resonance Imaging; Goals; Human; Individual Differences; Instruction; Learning; Life; Measures; Mediation; Modeling; Monitor; Motivation; Neurobiology; Neurocognitive; Neurosciences; Outcome; Physiological; Play; Prevention; Probability; Process; Psychological reinforcement; Psychology; Questionnaires; Research; Rewards; Signal Transduction; Social Interaction; Structure; Substance Addiction; Substance abuse problem; System; Uncertainty; Update; Weather; Work; base; control theory; depressive symptoms; experimental study; ideal observer (Bayesian); innovation; neuroregulation; relating to nervous system; respiratory; response; theories; trait

Humans and animals often make decisions under uncertainty, whereby each decision affects not only the immediate reward gain but also longer-term information gain. While important advances have been made in understanding human learning and decision-making, there is still a lack of understanding of the different motivational factors that come into play when the behavioral context confers systematically varying amounts of reward and information gain. This project tackles this problem using a combination of sophisticated cognitive modeling, innovative behavioral experiments, fMRI data, physiological (pupillometry, cardiac, and respiratory) data, and psychiatric measures (questionnaires addressing depressiveness, anxiety, anhedonia, locus of control, pessimism, and substance abuse). The objectives are (1) to develop a statistically grounded and neurobiologically informed theory for how different motivational factors (immediate reward, long-term reward, reduction of uncertainties, and random stochasticity) jointly influence human decision making; (2) use this theoretical framework to guide the understanding of how different brain regions, in particular neuromodulatory systems, work separately and conjointly to implement behavioral choices in response to the reward and informational structure of the environment; (3) characterize individual differences in terms of motivations, subjective monitoring of uncertainties, neural and physiological responses, and psychiatric profile. This work builds on multiple theoretic approaches: Bayesian ideal observer, reinforcement learning, Markov decision process, and control theory; and multiple neuroscientific research areas: learning, information seeking, confidence, decision making, change-point detection. It will advance an integrated understanding of computational theory, neuro-cognitive processes, behavioral manifestations, physiological signals, and psychiatric traits in choice behavior under uncertainty. It will help to clarify how different cortical and subcortical (especially neuromodulatory) brain regions differentially and cooperatively contribute to reward- and information-based learning, decision making, and exploration. These outcomes can be expected to contribute to advancements in basic scientific understanding of brain circuits, mechanisms, and functions related to the use and abuse of addictive substances, as well as their prevention and treatment. RELEVANCE (See instructions): Drug use and abuse often involve alterations in reward learning, decision-making, and uncertainty-related processing. This project contributes to basic computational and neurobiological understanding of these processes in the healthy brain, and may help to elucidate how these processes go awry in substance use and addiction disorders.

人类和动物经常在不确定性下做出决定，每个决定不仅影响 立即奖励增益，但也可以增加长期信息增益。虽然已经取得了重要的进步 在理解人类的学习和决策时，仍然缺乏对不同的 当行为环境赋予系统变化时，会发挥动力 奖励和信息收益量。该项目通过组合解决此问题 复杂的认知建模，创新的行为实验，fMRI数据，生理学 （瞳孔测定法，心脏和呼吸道）数据和精神病措施（调查问卷 沮丧，焦虑，狂热，控制源，悲观和滥用毒品）。目标 （1）开发一种统计基础和神经生物学知情的理论 动机因素（立即奖励，长期奖励，不确定性减少和随机 随机性）共同影响人类决策； （2）使用此理论框架指导 了解不同的大脑区域，特别是神经调节系统如何单独工作以及 共同实施行为选择，以应对奖励和信息结构 环境; （3）在动机方面表征个体差异，主观监测 不确定性，神经和生理反应以及精神病学概况。这项工作建立在多个 理论方法：贝叶斯理想观察者，加强学习，马尔可夫决策过程和 控制理论；和多个神经科学研究领域：学习，信息寻求，信心， 决策，更改点检测。它将提高对计算的综合理解 理论，神经认知过程，行为表现，生理信号和精神病特征 在不确定性下的选择行为中。这将有助于阐明皮质和皮层下的不同（尤其是 神经调节）大脑区域差异化，并合作有助于奖励和 基于信息的学习，决策和探索。这些结果可以预期 在对脑电路，机制和功能的基本科学理解方面的进步促进 与上瘾物质的使用和滥用以及预防和治疗有关。 相关性（请参阅说明）： 吸毒和滥用通常涉及奖励学习，决策和与不确定性有关的改变 加工。该项目有助于对这些项目的基本计算和神经生物学理解 健康大脑中的过程，可能有助于阐明这些过程在使用物质时如何出现问题 和成瘾障碍。