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

人类和动物经常在不确定的情况下做出决定，因此每个决定不仅影响 不仅可以获得短期回报，还可以获得更长期的信息。虽然已经取得了重要的进展 在理解人类的学习和决策方面，仍然缺乏对不同 当行为背景系统地变化时，起作用的动机因素 奖励和信息收益的数额。该项目使用以下几个组合来解决此问题 复杂的认知建模、创新的行为实验、功能磁共振数据、生理学 (斜视测量、心脏和呼吸)数据和精神测量(问卷涉及 抑郁、焦虑、快感缺乏、心理控制点、悲观和滥用药物)。目标 是(1)开发一个有统计学基础和神经生物学信息的理论，说明 激励因素(即时奖励、长期奖励、减少不确定性和随机性 随机性)共同影响人类的决策；(2)用这个理论框架来指导 了解不同的大脑区域，特别是神经调节系统，是如何分别和 共同实施行为选择，以响应奖励和信息结构 环境；(3)表征个体在动机、主观监控和 不确定性，神经和生理反应，以及精神状态。这项工作建立在多个 理论方法：贝叶斯理想观测器、强化学习、马尔可夫决策过程和 控制理论；以及多个神经科学研究领域：学习、信息寻求、自信、 决策、变点检测。它将促进对计算的综合理解 理论、神经认知过程、行为表现、生理信号和精神特征 在不确定条件下的选择行为。它将有助于澄清大脑皮层和皮质下(尤其是 神经调节)大脑区域不同地和协同地对奖赏做出贡献-和 基于信息的学习、决策和探索。这些结果可以预期到 有助于提高对大脑回路、机制和功能的基本科学认识 与使用和滥用令人上瘾的物质及其预防和治疗有关。 相关性(请参阅说明)： 药物使用和滥用通常涉及奖赏学习、决策和不确定性相关的改变。 正在处理。该项目有助于基本的计算和神经生物学方面的理解 并可能有助于阐明这些过程在物质使用中是如何出错的 和成瘾障碍。

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.