Single-trial reward learning and generalization

单次试验奖励学习和泛化

• 批准号: 8962069
• 负责人: Vishnu Pradeep Murty
• 金额: $ 2.86万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2015-09-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8962069
• 关键词: Address; Animal Model; Association Learning; Behavior; Behavioral; Computer Simulation; Corpus striatum structure; Data; Disease; Dissociation; Drug Addiction; Drug abuse; Environment; Episodic memory; Etiology; Feedback; Functional Magnetic Resonance Imaging; Hippocampus (Brain); Human; Individual; Knowledge; Lead; Learning; Life; Mediating; Memory; Methods; Modeling; Nature; Neurobiology; Outcome; Process; Psychological reinforcement; Relapse; Relative (related person); Research; Resolution; Restaurants; Rewards; Structure; System; Testing; Update; addiction; base; classical conditioning; design; experience; flexibility; human subject; neuroimaging; neuromechanism; neurotransmission; novel; public health relevance; relating to nervous system

DESCRIPTION (provided by applicant): To behave adaptively, individuals must learn associations between reward outcomes and features of the environment, and use these associations to inform behavior in novel situations. Previous research in animal models has demonstrated that the neural systems engaged during associative learning can influence such generalization behaviors, such that adaptive generalization requires engagement of the hippocampus. However, the majority of research in human subjects has investigated striatal-guided reward learning and generalization, often during multi-trial, probabilistic feedback learning. In everyday life, reward values are often acquired after only a single experience or episode; the neural mechanisms guiding such single-trial reward feedback learning are undefined. The current proposal advances the claim that single-trial reward feedback learning and generalization may be characterized by the interactions between reinforcement learning and episodic memory systems. We will test this claim by combining behavioral methods, computational modeling, and high-resolution neuroimaging. We will characterize the neural systems engaged during the acquisition of single-trial reward feedback associations using hi-resolution fMRI (Aim 1). Further, we will characterize how associative learning via single-trial reward feedback contributes to generalization behavior using reinforcement-learning modeling (Aim 2) and model-based fMRI (Aim 3). We predict that (1) single-trial reward-feedback learning will engage interactions between the striatum and hippocampus during associative learning, and (2) the interaction between these systems will promote adaptive generalization of reward associations. The knowledge obtained from this proposal will advance our understanding of the neurobiology of reward learning and provide evidence for the mechanisms underlying healthy and impaired reward-related behaviors such as drug addiction.

描述（由申请人提供）：为了自适应地行事，个人必须学习奖励结果和环境特征之间的关联，并使用这些关联来告知新情况下的行为。动物模型中的先前研究表明，在关联学习过程中参与的神经系统会影响这种泛化行为，因此自适应泛化需要海马的参与。然而，人类受试者的大多数研究经常在多审判，概率的反馈学习期间调查了纹状体指导的奖励学习和概括。在日常生活中，奖励价值通常仅在一次经验或情节之后获得。指导这种单审反馈学习的神经机制是不确定的。当前的建议推动了单次奖励反馈学习和概括的说法，其特征是强化学习与情节记忆系统之间的相互作用。我们将通过结合行为方法，计算建模和高分辨率神经影像来测试这一主张。我们将表征使用Hi-Jolution FMRI（AIM 1）获取单次奖励反馈协会时所参与的神经系统。此外，我们将通过单审奖励反馈通过增强学习模型（AIM 2）和基于模型的fMRI（AIM 3）来促进概括行为的联想学习如何有助于概括行为。我们预测（1）单次奖励反馈学习将在关联学习过程中参与纹状体和海马之间的相互作用，并且（2）这些系统之间的相互作用将促进奖励关联的适应性概括。从该提案中获得的知识将提高我们对奖励学习神经生物学的理解，并为健康和受损与奖励相关的行为（例如吸毒成瘾）提供了证据。