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

CRCNS：奖励相关决策的神经计算研究

基本信息

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
项目类别：
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

项目摘要

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的情况下做出决定，因此每个决定不仅影响到动物