The Role of Sleep in Insight and Generalization

睡眠在洞察力和概括中的作用

• 批准号: 9300726
• 负责人: Anna C Schapiro
• 金额: $ 5.92万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9300726
• 关键词: Algorithms; Behavioral; Brain; Categories; Complex; Computer Simulation; Disease; Electroencephalography; Environment; Exposure to; Future; Hour; Human; Impairment; Individual; Lead; Learning; Link; Literature; Measures; Memory; Mental disorders; Modeling; Neural Network Simulation; Participant; Pattern; Performance; Physiological; Physiology; Play; Polysomnography; REM Sleep; Research; Role; Running; Schools; Semantics; Sleep; Sleep Stages; Sleep disturbances; Stimulus; Structure; Testing; Time; Traction; Training; Work; awake; computational network modeling; density; effective therapy; experience; experimental study; improved; insight; memory consolidation; memory process; nervous system disorder; novel; public health relevance; rapid eye movement; simulation; teacher

 DESCRIPTION (provided by applicant): Sleep is now known to play a critical role in facilitating the long-term storage of memories. Some of the most intriguing findings in the literature suggest that sleep does not simply support the storage of individual memories, but can help to uncover relationships between experiences that were not apparent while awake - it can provide new "insight" into these experiences. This phenomenon has far-reaching implications for how we make sense of our environment, but it is a mystery how sleep accomplishes this. The proposed research will help us understand how sleep is able to promote insight into the experiences we have while awake. Simulations from neural network computational models suggest that the key to generalization is to experience related pieces of information many times, in many different orders - to interleave the information. Our environment usually does not provide us with this kind of interleaved exposure to new information. We propose that sleep may provide an opportunity to replay memories from the day in an interleaved fashion, allowing for discovery of the relationships between them. Our first aim is to run an experiment that tests this novel hypothesis, in which human participants will learn about the features of three categories of related objects. At the very end of this training, participants will be exposed to two new objects that have features spanning two of the categories, creating a semantic bridge between the categories. To test generalization, we will then show participants novel stimuli, some of which will have new configurations of features spanning the bridged categories, and ask them to rate how realistic they seem. We expect that the interleaving of the bridge stimuli with the other stimuli during sleep will allow participants who sleep in between two test periods to improve in this generalization, such that they become more likely to feel that novel stimuli with features from the bridged categories are realistic. We will record sleep polysomnographically with high-density electroencephalography, allowing us to determine which sleep stages and specific features of sleep are associated with the improved generalization. Our second aim is to extend our existing computational model of learning during sleep to incorporate sleep physiology, and demonstrate how specific features of sleep might contribute to the pattern of improved generalization performance. This research will help us understand the mechanisms of learning and memory consolidation in the brain, which may in turn lead to insights into how to treat neurological disorders with learning or memory disturbances.

 描述（由申请人提供）：现在已知睡眠在促进记忆的长期储存方面起着关键作用。文献中一些最有趣的发现表明，睡眠不仅支持个人记忆的存储，而且可以帮助揭示清醒时不明显的经验之间的关系-它可以为这些经验提供新的“见解”。这种现象对我们如何理解我们的环境有着深远的影响，但睡眠是如何实现这一点的，这是一个谜。这项拟议中的研究将帮助我们了解睡眠如何能够促进我们清醒时的体验。来自神经网络计算模型的模拟表明，泛化的关键是以许多不同的顺序多次体验相关的信息片段-交织信息。我们的环境通常不会为我们提供这种交叉接触新信息的机会。我们认为睡眠可能提供了一个机会，以交错的方式重放白天的记忆，从而发现它们之间的关系。我们的第一个目标是运行一个实验来测试这个新的假设，在这个实验中，人类参与者将学习三类相关物体的特征。在培训的最后，参与者将接触到两个新对象，这些对象具有跨越两个类别的特征，从而在类别之间创建语义桥梁。为了测试泛化，我们将向参与者展示新的刺激，其中一些将具有跨越桥接类别的新特征配置，并要求他们评估它们看起来有多真实。我们期望在睡眠期间将桥接刺激与其他刺激交错，这将允许在两个测试期间之间睡眠的参与者改善这种概括，使得他们更有可能感觉到具有桥接类别特征的新刺激是现实的。我们将用高密度脑电图记录睡眠，使我们能够确定哪些睡眠阶段和睡眠的具体特征与改善的概括有关。我们的第二个目标是扩展我们现有的睡眠学习计算模型，将睡眠生理学纳入其中，并展示睡眠的特定特征如何有助于提高泛化性能。这项研究将帮助我们了解大脑中学习和记忆巩固的机制，这反过来可能会导致对如何治疗学习或记忆障碍的神经系统疾病的见解。