Single-trial reward learning and generalization

描述（由申请人提供）：为了适应行为，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。先前在动物模型中的研究已经证明，在联想学习期间参与的神经系统可以影响这种泛化行为，例如自适应泛化需要海马体的参与。然而，大多数对人类受试者的研究都是在多次尝试的概率反馈学习中研究纹状体引导的奖励学习和泛化。在日常生活中，奖励值通常是在一次经历或一次经历之后获得的;指导这种单次尝试奖励反馈学习的神经机制是不确定的。目前的建议提出的主张，单次试验奖励反馈学习和泛化的特点是强化学习和情景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成像来验证这一说法。我们将使用高分辨率功能磁共振成像（目标1）的单次试验奖励反馈协会收购过程中从事的神经系统的特点。此外，我们将描述如何通过单次试验奖励反馈的联想学习有助于泛化行为使用的学习建模（目标2）和基于模型的功能磁共振成像（目标3）。我们预测：（1）单次奖励反馈学习将在联想学习过程中参与纹状体和海马之间的相互作用，（2）这些系统之间的相互作用将促进奖励协会的适应性泛化。从这个提议中获得的知识将促进我们对奖励学习的神经生物学的理解，并为健康和受损的奖励相关行为（如药物成瘾）的机制提供证据。

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.