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Implicit serial learning

隐式串行学习

基本信息

批准号：
10736828
负责人：
VINCENT P FERRERA
金额：
$ 73.92万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-05 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10736828
关键词：
Acetylcholine Address Algorithms Alzheimer&apos s Disease Anhedonia Behavior Behavioral Bipolar Disorder Brain Brain region Cells Cognitive Cognitive deficits Corpus striatum structure Cues Decision Making Delusions Detection Disease Dissociation Dopamine Dorsal Equilibrium Funding Goals Health Impairment Incentives Individual Investigation Knowledge Learning Lesion Linear Models Maps Measures Mental disorders Modeling Neurons Neurotransmitters Organism Outcome Paranoia Phase Play Population Prefrontal Cortex Proxy Psyche structure Psychological Transfer Psychological reinforcement Pupil Reporter Research Resistance Resolution Rewards Role Running Schizophrenia Serial Learning Signal Transduction Stimulus Structure System Testing Time Training Update autism spectrum disorder behavior test caudate nucleus expectation experience experimental study insight learning strategy multi-electrode arrays neural neural circuit neurodevelopment neuroeconomics neuroimaging neurophysiology neuroregulation nonhuman primate novel patient population social neuroscience theories

项目摘要

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.

项目摘要强化学习（RL）是一个强大的框架，用于理解结果驱动行为及其神经网络。基础RL的一个关键问题是如何整合进行推理以做出适当响应的能力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，但没有明确的暗示。在上一个融资期，我们开发了行为和计算方法来严格解决这个问题，并使单细胞神经生理学这些观察开始揭示大脑如何表现潜在结构。在这里，我们重新定义了问题作为一个使用认知地图来表示潜在秩序和支持推理。我们建议测试这一想法与新开发的行为方法，这使得前所未有的洞察力的作用，皮质-纹状体神经回路和基于模型的RL中的上行神经调节系统。这些研究将集中在神经生理学、神经影像学和病变研究表明具有在内隐序列学习中的重要作用，特别是背外侧（dlPFC）和腹内侧前额叶皮层（vmPFC）和背侧纹状体。三个目标1。测试NHP在以下环境中进行基于模型的推理的能力存在补偿性奖励激励，2。识别dlPFC，vmPFC，和尾状核;测试乙酰胆碱和多巴胺浓度的变化是否与连续学习相关，特别是在转移过程中。推理能力在许多精神疾病中受损，包括精神分裂症和躁郁症。这种损害可能是思维障碍的基础，如妄想和妄想症