Implicit serial learning

项目摘要 强化学习(RL)是理解结果驱动行为及其神经网络的有力框架 基础。RL的一个关键问题是如何结合进行推理的能力，从而允许适当的响应 训练中没有遇到的新情况。推理依赖于对潜在关系的检测 预测奖励，但不是通过明确的暗示发出信号。在之前的资助期，我们开发了行为 以及严格解决这个问题的计算方法，并使单细胞神经生理学 开始揭示大脑如何代表潜在结构的观察。在这里，我们重新概念化这一点 问题是使用认知地图来表示潜在顺序和支持推理的问题。我们打算测试一下 这一想法与新开发的行为方法相结合，可以前所未有地深入了解 基于模型的RL中的皮质-纹状体神经回路和上行神经调节系统。这些 调查将集中在神经生理学、神经成像和损伤研究表明的大脑区域 内隐系列学习中的重要作用，特别是背外侧(DlPFC)和前额叶腹内侧皮质 (VmPFC)和背侧纹状体。三个目标将1.测试NHP进行基于模型的推理的能力 反补贴奖励激励的存在，2.在dlPFC，vmPFC， 以及3.检验乙酰胆碱和多巴胺浓度的变化是否与连续学习有关， 特别是在转移期间。推理推理在许多精神疾病中都会受到损害，包括 精神分裂症和双相情感障碍。这种损害可能是妄想症和精神错乱等思维障碍的基础 妄想症。

Project Summary Reinforcement learning (RL) is a powerful framework for understanding outcome-driven behavior and its neural basis. A key issue for RL is how to incorporate an ability to make inferences that allow appropriate responding to novel conditions not encountered during training. Inference relies on the detection of latent relationships that predict rewards but are not signaled by explicit cues. In the previous funding period, we developed behavioral and computational approaches to rigorously address this problem, and made single-cell neurophysiological observations that began to reveal how the brain represents latent structures. Here, we re-conceptualize this problem as one of using a cognitive map to represent latent order and support inference. We propose to test this idea with a newly developed behavioral approach, which allows unprecedented insight into the role of cortico-striatal neural circuits and ascending neuromodulatory systems in model-based RL. These investigations will focus on brain regions that neurophysiology, neuroimaging, and lesion studies suggest have important roles in implicit serial learning, specifically dorsolateral (dlPFC) and ventromedial prefrontal cortex (vmPFC), and dorsal striatum. Three aims will 1. Test NHPs ability to make model-based inferences in the presence of countervailing reward incentives, 2. Identify neural circuitry of serial learning in the dlPFC, vmPFC, and caudate nucleus, and 3. Test if changes in Ach and DA concentrations are correlated with serial learning, particularly during transfer. Inferential reasoning is impaired in many psychiatric illnesses including schizophrenia and bipolar disorder. This impairment may underlie thought disorders such as delusions and paranoia.