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）是理解结果驱动的行为及其神经的强大框架基础。 RL的关键问题是如何合并一些允许适当响应的推论能力在训练期间未遇到的新颖条件。推论依赖于检测潜在关系预测奖励，但不会通过明确提示发出信号。在上一个资金期间，我们发展了行为和严格解决此问题的计算方法，并使单细胞神经生理学观察开始揭示大脑如何代表潜在结构。在这里，我们重新概念化了这个问题是使用认知图来表示潜在的秩序和支持推论之一。我们建议测试这个想法采用新开发的行为方法，可以使人深入了解基于模型的RL中的皮质 - 纹状体神经回路和上升的神经调节系统。这些研究将重点放在神经生理学，神经影像学和病变研究表明的大脑区域上在隐式串行学习中，特别是背外侧（DLPFC）和腹侧前额叶皮层中的重要作用（VMPFC）和背纹状体。三个目标将为1。测试NHP的能力，可以在存在反击奖励激励措施，2。确定DLPFC中串行学习的神经回路，VMPFC，和尾状核，以及3。测试ACH和DA浓度的变化是否与串行学习相关，特别是在转移期间。在许多精神疾病中，推理推理受到了损害精神分裂症和躁郁症。这种障碍可能是妄想和诸如妄想的思想障碍的基础偏执狂。